Using Wearable Sensor Technology to Analyze Running Technique and Prospective Running-Related Injuries During United States Military Cadet Basic Training

Abstract

Background:

Running biomechanics have been linked to the development of running-related injuries in recreational and military runners.

Purpose/Hypothesis:

The purpose of this study was to determine if personal characteristics or running biomechanical variables are associated with running-related injury incidence or time to injury in military cadets undergoing training. It was hypothesized that a rearfoot strike pattern, greater rate of impact, or a lower step rate would be related to a greater running-related injury incidence and a decreased time to injury.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

Military cadets wore an on-shoe wearable sensor that analyzed biomechanical variables of foot strike pattern, rate of impact, running pace, step rate, step length, and contact time during cadet basic training (60 days). Running-related injuries during cadet basic training were determined by medical record review. Personal and running variables between the injured and uninjured cadets were compared using independent t tests and chi-square analyses. Time to injury and hazard ratios (HRs) were estimated using Kaplan-Meier survival curves and Cox proportional hazard regression models, respectively.

Results:

Of the 674 cadets who completed the study, 11% sustained a running-related injury. A significantly greater proportion of the injured participants were female (χ² = 7.95; P = .005) and had a prior history of injury (χ² = 7.36; P = .007). Univariate Cox proportional hazard regression models revealed greater injury risk in females (HR, 1.96; 95% CI, 1.22-3.16; P = .005) and cadets with a prior injury history (HR, 1.86; 95% CI, 1.18-2.93; P = .008). After adjusting Cox models for prior injury, females were found to be at a 1.89 times (95% CI, 1.17-3.04; P = .009) greater risk of injury. Running biomechanical variables were not associated with injury risk.

Conclusion:

Study results indicated that non-modifiable risk factors such as female sex and prior injury history increased the risk of running-related injury in cadets undergoing military training. Running biomechanical variables measured by the wearable sensor were not associated with injury in this study.

Keywords

wearable sensors running-related injury overuse injury cadet basic training

Running is a popular sport despite a high incidence of injury, with an estimated 50% of recreational runners experiencing a running-related injury (RRI).^28,57 The knee, foot, and shank are involved in up to 70% of all RRIs,^17,28 with common diagnoses including medial tibial stress syndrome, Achilles tendinopathy, plantar fasciitis, and patellofemoral pain syndrome.²⁸ Similarly, in the military, RRIs are a leading cause of lost or limited duty days⁴⁸ and account for 45% of all exercise and sports-related injuries.²² Because nearly all military service members run for physical training, understanding RRI risk factors is needed to identify prevention and treatment strategies and increase military force readiness.

Contributing risk factors to the development of RRIs may include age, sex, prior injury history, and running biomechanics,^5,13,25,40 although it is likely that RRIs have a multifactorial origin.³⁹ Due to the repetitive mechanical stress applied to the body during gait, running biomechanics have garnered attention as a potential modifiable risk factor for RRI prevention and treatment strategies.^16,20,41,56 Biomechanical variables such as foot strike pattern,^25,43 average vertical loading rate (AVLR),^27,53 step rate,^29,35 step length,⁵⁵ and contact time^11,54 have been linked to RRI risk, although large-scale prospective evidence is limited and results are frequently population dependent. Traditional laboratory-based gait assessments used to measure running biomechanical variables require costly and sophisticated technology, time-intensive analytical testing and data reduction techniques, and a controlled laboratory environment. These methods lack ecological transferability and fail to replicate a typical military training environment, which presents challenges for conducting large-scale studies in the military population.

Recently, lightweight wearable sensors with embedded triaxial accelerometers have been used to measure running biomechanical variables in the absence of laboratory-grade equipment.^38,46 Wearable sensors allow for continuous objective data collection in real-world settings, which may include varied terrain, speed, and fatigue conditions,³⁸ potentially increasing the ecological validity of the gait assessment. Select wearable sensor technology has demonstrated moderate to good validity and reliability in the assessment of foot strike pattern and spatiotemporal running variables^38,46,49 and has been used to assess running biomechanical variables in recreational and military runners.^11,54,57 However, no published study to our knowledge has leveraged wearable sensor technology to prospectively assess running biomechanical variables associated with future RRI in a military training setting.

The purpose of this study was to determine if personal characteristics or running biomechanical variables measured using a wearable sensor were associated with (1) RRI incidence and (2) time to injury in military cadets undergoing military training. We hypothesized that a rearfoot strike pattern, greater rate of impact, or lower step rate would be related to (1) greater RRI incidence and (2) a decreased time to injury.

Methods

Study Design and Setting

In this prospective cohort study, the relationship between multiple personal and running variables and RRI risk in military cadets was investigated. The primary outcome of interest was lower extremity overuse RRIs over a 60-day period during cadet basic training. The study was approved by the Regional Health Command Atlantic Institutional Review Board, and all participants received a full description of the study protocol and signed an informed consent document before participation.

Participants

All 1195 United States Military Academy cadets entering the class of 2022 were recruited to participate during a brief given by research personnel, who provided verbal and written information on study requirements. Individuals included in the study were 18 to 23 years of age (or emancipated 17-year-old individuals) who read and spoke English fluently to follow study instructions and who were deemed healthy and medically fit for military service by the US Department of Defense Medical Evaluation Board. Individuals were excluded if they were currently pregnant or had been in the previous 6 months, were on a running-limiting injury profile by a medical provider, or had a lower extremity or back surgery in the previous 6 months. Volitional consent was obtained from cadets, with no military leadership present in the room and an ombudsman in attendance during the consent process.

Baseline Data Collection

Each participant’s personal characteristics, self-reported mean weekly running distance, and any self-reported lower extremity injuries in the previous 12 months were collected via an intake form at the start of cadet basic training. Cadets were not issued running shoes for cadet basic training; however, they were provided guidance from the academy to arrive with relatively new shoes that had been broken in for training. Investigators attached a numbered wearable sensor or triaxial accelerometer (MilestonePod, Columbia, MD, USA) to the shoelaces of the participant’s right running shoe three-fourths of the way down the shoe toward the toes, per the manufacturer’s instructions. The participant was instructed to wear the shoe sensor for the duration of cadet basic training. The study identification number and the participant’s height were entered into an online application (Milestone Sports, Columbia, MD, USA) to store each participant’s running biomechanical data. The validity of the wearable sensor was established previously for measures of spatiotemporal parameters and dichotomous classification of foot strike pattern (rearfoot and non-rearfoot).^46,49 The sensor was capable of collecting up to 20 hours of data, allowing it to collect multiple run data points during cadet basic training.

Over the 60-day training period, the sensor captured running biomechanical variables of foot strike pattern (FSP), rate of impact, running pace, step rate, step length, and contact time at a sampling rate of 200 Hz. Rate of impact is reported as a surrogate for average vertical loading rate (AVLR), with the rate classified as low (equivalent to an AVLR of <64 body weights per second [BW/s]), medium (64-80 BW/s), or high (>80 BW/s) based on the derivative of the accelerometry signal from foot strike to peak positive acceleration.⁵⁰ The rate of impact was then converted to a single categorical number through the following equation:⁵⁰

Rate of impact = \frac{((3 \times % high) + (2 \times % medium) + (1 \times % low)) - 100}{2} .

Land navigation, ruck marching, and field training exercises during cadet basic training were conducted in combat boots and not included in the analysis. At the end of cadet basic training, the sensors were collected after the Army Physical Fitness Test, which consisted of a 2-minute maximum effort for push-ups, a 2-minute maximum effort for sit-ups, and a timed 2-mile run with each event having age- and sex-adjusted passing standards. The sensor data were then synchronized to the online application via Bluetooth technology.

Injury Surveillance

The primary outcome of interest, an RRI, was defined as any overuse lower extremity or low back musculoskeletal pain or injury with running as the main mechanism of injury that caused the participant to seek medical care⁵⁹ during cadet basic training. Cadets with acute injuries and/or non-RRI musculoskeletal injuries – or overuse injuries caused by another mechanism of injury other than running such as military training, activities of daily living, or sport – were excluded from the analysis.

Injury data were obtained through queries of the Military Health System electronic medical records. During cadet basic training, cadets who are ill or injured report to sick call to be seen by a medical provider, with all encounters documented in the Armed Forces Health Longitudinal Technology Application (AHLTA) electronic medical record system. Additionally, injuries resulting in time lost to physical activity are documented in the Cadet Illness and Injury Tracking System (CIITS). The participant’s AHLTA and CIITS medical records were reviewed for clinically documented RRIs during the 60-day observation period.

Data Reduction

All data were exported into RStudio (Version 1.1.463, 2009-2018 R Studio, Inc.). Custom MATLAB code (The MathWorks, Inc.) was used to filter sensor data to include only run data between 3 and 10 km in distance, between 600 and 3600 seconds, and with a step rate >140 steps/min. Distance and time parameters during the data filtration process were based on known run durations during cadet basic training and helped ensure that other walking and transient running periods were excluded from the analysis. To account for transitional spatiotemporal changes during warm-up and cool-down periods,¹⁴ data from the first and last 10% of each run were removed, which allowed focus on more traditional steady-state running metrics. The remaining run data were analyzed in MATLAB to determine an overall mean for the continuous variables (running pace, step rate, step length, and contact time) and mode for the categorical variables (foot strike pattern and rate of impact) for each participant. Foot strike pattern was dichotomized into rearfoot or non-rearfoot strike,⁴⁹ and step length values were normalized to the participant’s height.⁶

Statistical Analysis

Descriptive statistics (means with standard deviations or absolute values) were used to describe baseline participant and running characteristics for the injured and uninjured groups. Independent t tests were conducted to compare between-group differences for continuous variables of interest based on injury status. Chi-square analyses were conducted to assess the association of categorical variables of interest with injury status. To estimate time-to-event statistics, Kaplan-Meier survival curves were calculated with RRI as the event variable, survival time in days (0-60 days) until the first reported RRI, and personal and running-related variables as a predictor variable. Quartile-based frequency distributions were calculated for the continuous variables; the quartiles were divided in ascending order, with quartile 1 representing the least amounts for each variable and quartile 4 representing the greatest amounts for each variable. Pairwise comparisons with a Holm adjustment were used to compare significant values. Risk factors and promising covariates were carried forward into univariate and multivariate Cox proportional hazard regression models to estimate hazard ratios (HRs) with 95% confidence intervals between potential risk factors and time to injury. All analyses were conducted using R (Version 4.2.2; The R Foundation⁵²) and the chi-square calculator,⁴⁷ with an alpha level of .05.

Results

Participants

In total, 1075 cadets consented to participate, and complete data were available for 674 participants (63%). Information related to participant enrollment and missing data are detailed in Figure 1. The leading contributors for excluded and missing data were run sensors not returned (n = 264), damaged run sensors (n = 74), and acute injuries (n = 44).

Figure 1.

Flowchart showing participant enrollment.

Descriptive Data

A total of 75 participants (11.1%) sustained an RRI that required medical attention during cadet basic training. The mean time to injury was 22 days (range, 2-58 days). The knee was the most frequently injured area (n = 18; 24.0%), followed by the shin (n = 16; 21.3%) and foot (n = 11; 14.7%) (Figure 2). A 2 × 2 chi-square analysis for the association of sex and RRI showed a significantly greater proportion of injured females (26/148; 17.6%) than males (49/526; 9.3%) (χ² = 7.95; P = .005). Additionally, a 2 × 2 chi-square analysis for the association of prior injury history in the past 12 months and RRI was significant (χ² = 7.36; P = .007). No significant differences between injured and uninjured participants were noted for the remaining personal characteristics (Table 1).

Figure 2.

Number of running-related injuries per body region.

Table 1

Characteristics Overall and According to Injured and Uninjured Participants^a

Variable	Overall	Injured	Uninjured	P
Variable	(n = 674)	(n = 75)	(n = 599)	P
Personal characteristics
Age, y	18.32 ± 0.92	18.47 ± 1.06	18.30 ± 0.90	.14
Height, cm	176.73 ± 8.91	174.96 ± 10.12	176.95 ± 8.73	.07
Mass, kg	75.56 ± 13.20	76.09 ± 14.60	75.50 ± 13.03	.72
Sex^b				.005
Male	526	49	477
Female	148	26	122
Prior injury history^b				.007
Yes	213	34	179
No	461	41	420
Self-reported weekly running distance, km	16.60 ± 14.50	14.73 ± 12.76	16.84 ± 14.69	.24
Running characteristics
Foot strike pattern^b				.88
Rearfoot	562	63	499
Non-rearfoot	112	12	100
Rate of impact^b				.72
Low	615	71	544
Medium	35	2	33
High	24	2	22
Pace, m/s	3.49 ± 0.49	3.54 ± 0.55	3.49 ± 0.49	.38
Step rate, steps/min	162.27 ± 6.47	163.03 ± 6.24	162.17 ± 6.50	.86
Step length^c	0.64 ± 0.05	0.64 ± 0.05	0.64 ± 0.05	.54
Contact time, ms	296.84 ± 29.12	299.14 ± 26.04	296.56 ± 29.49	.23

Data are presented as mean ± SD or No. of participants. Boldface P values indicate a statistically significant difference between the injured and uninjured groups (P < .05).

Value based on chi-square analysis; Yates chi-square for rate of impact analysis.

Normalized to participant height.

Running-Related Biomechanical Variables

There was no significant association between foot strike pattern (P = .88) or rate of impact (P = .72) and injury. Furthermore, there was no significant difference in pace (P = .38), step rate (P = .86), step length (P = .54), or contact time (P = .23) between the cadets who sustained an injury and those who remained uninjured (Table 1).

Survival Analysis

Participant sex (P = .005) and prior injury history (P = .007) were found to be significant variables for estimating time to injury. On average, females were injured 3 days sooner than males (survival of 53 vs 56 days). Additionally, those with a prior injury history were injured 3 days sooner than those without a prior injury history (survival of 54 vs 57 days). No running biomechanical variables were significant for estimating time to injury (foot strike pattern, P = .85; rate of impact, P = .52; pace, P = .87; step rate, P = .20; step length, P = .30; contact time, P = .76).

Univariate Cox proportional hazard regression models revealed that females were at a 1.96 times (95% CI, 1.22-3.16; P = .005) greater risk of sustaining an injury during cadet basic training compared with male cadets. Individuals with a prior injury history were at a 1.86 times (95% CI, 1.18-2.93; P = .008) greater risk of injury than those without a prior injury history (Figure 3). After adjusting Cox models for prior injury, female cadets were at a 1.89 times (95% CI, 1.17-3.04; P = .009) greater risk of injury. No run variables were significantly associated with injury risk during the univariate analysis or after adjusting for sex and prior injury (Table 2).

Figure 3.

Kaplan-Meier survival curve estimates and hazard ratios for running-related injuries during cadet basic training based on (A) sex and (B) prior injury history. *Statistically significant (P < .05).

Table 2

Results of Cox Regression Analysis for Running-Related Variables

Variable	Unadjusted HR (95% CI)	P	Adjusted HR (95% CI)^a	P
Foot strike pattern
Rearfoot	Reference		Reference
Non-rearfoot	0.94 (0.51-1.75)	.85	0.93 (0.50-1.72)	.81
Rate of impact
Low	Reference		Reference
Medium	0.49 (0.12-1.98)	.31	0.48 (0.12-1.96)	.31
High	0.73 (0.18-2.97)	.66	0.73 (0.18-2.97)	.66
Pace, m/s	1.20 (0.76-1.89)	.43	1.17 (0.75-1.82)	.50
Step rate, steps/min	1.02 (0.98-1.05)	.30	1.01 (0.98-1.05)	.53
Step length^b	0.86 (0.12-59.19)	.94	0.44 (0.006-31.39)	.70
Contact time, ms	1.00 (0.99-1.01)	.45	1.00 (0.99-1.01)	.51

Adjusted hazard regression for sex and prior injury history. HR, hazard ratios

Normalized to participant height.

Discussion

In the present study, the relationship between personal characteristics, running biomechanical variables, and RRI risk in military cadets undergoing military training was investigated. Contrary to our hypothesis, rearfoot strike pattern, greater rate of impact, and lower step rate quantified using a wearable sensor were not associated with RRI incidence or time to injury. Instead, the findings revealed that female cadets and those with a prior history of injury in the past 12 months had a greater RRI incidence and a decreased time to injury. Even after adjusting for prior injury, female cadets were at an almost 2 times greater risk of RRI compared with male cadets.

Female sex has been identified as a contributing risk factor for RRIs in previous studies,^12,26,30,40 supporting the results of the current study. However, a systemic review and meta-analysis that included adolescent and adult runners did not identify sex as a risk factor when observing overall RRI risk.²⁶ When site-specific injuries were considered, females were twice as likely to sustain a bone stress injury while males were at a greater risk of Achilles tendon injuries.²⁶ In the current study, females were at an overall increased RRI risk, although no running-related biomechanical variables were associated with injury. It is plausible that variables other than the ones we assessed during this study, such as altered hip and knee frontal and transverse plane kinematics, may predispose females to RRIs.^5,8 Female runners typically exhibit greater peak hip adduction during running,⁸ which may increase patellofemoral pain syndrome risk.⁴⁵ Other factors such as the physiological effects of estrogen on tendon and ligament stiffness⁷ as well as weaker hip abductors and external rotators³³ may also play a role in injury development for females but were not assessed in the current study. In the military, female soldiers have a greater incidence of overall injuries compared with their male counterparts.^1,24,42 Among naval recruits, females were >4 times more likely to develop medial tibial stress syndrome,¹⁹ possibly related to females “overstriding” to match the male step length when marching in formation.⁶⁰ Although step length normalized to the participant’s height was not a significant risk factor for RRIs in the current study, we are unaware of the role and/or cumulative stress that other aspects of military training, such as marching in formation, may have had on overuse musculoskeletal injury development.

Furthermore, univariate analysis revealed a history of prior injury to be a significant predictor of injury incidence for the current study. Cadets with an injury 12 months before cadet basic training were at a 1.86 times greater risk of RRI, consistent with HRs observed in recreational runners with a prior injury (HR, 1.57-1.91).^3,13 It is unclear if incomplete tissue healing, loading of weakened structures, or compensatory movement patterns as a result of a previous injury predispose individuals to a future injury.³ Although all individuals in our current population were deemed healthy and medically fit for military service by the US Department of Defense Medical Evaluation Board, it is unknown if individuals ever received medical treatment and/or corrective biomechanical cues (ie, gait retraining) to address concerns of weakened tissues or compensatory movement patterns that may have resulted from a prior injury.

RRIs were medically documented in 11% of cadets in the current study. Similar injury incidence rates have been reported during cadet basic training²³ and in recreational runners.^18,37 The knee, shin, and foot were the most frequently injured regions, accounting for 60% of all injuries, consistent with previous research.^18,23,51 RRI incidence and/or time to injury was not influenced by running variables quantified during this study, contrary to our hypothesis.

A majority of cadets (83%) used a rearfoot strike pattern, which is representative of typical foot strike pattern selection in military and recreational runners.^10,54 Similar to recent studies,^4,15,44,54 we did not observe foot strike pattern to be associated with overall injury incidence or time to injury. Site-specific injuries may be associated with foot strike patterns; however, this was not the focus of this study. Rearfoot strike runners demonstrate more negative work at the knee^21,58 and a potential greater risk of knee injury,⁴³ while non–rearfoot strike runners demonstrate more Achilles tendon loading^31,58 and a potential greater risk of calf and/or Achilles tendon injuries.²⁵ Few calf injuries (n = 6) were observed during this study, likely due to our young population, limiting our confidence to make robust observations regarding foot strike pattern and site-specific injury risk.

Although rate of impact, a surrogate for loading rate, was not associated with injury risk, accelerometry data from an on-shoe sensor may not be sufficient to accurately estimate loading rate,⁴⁶ suggesting that our results should be interpreted with caution. Kinetic variables, such as a greater vertical impact peak,⁹ AVLR,^9,27,53 and peak braking force,⁴⁴ may have a role in RRI development. However, in a recent prospective study following >800 recreational runners for 6 months, vertical impact peak, instantaneous and average vertical loading rate, and peak braking force were not associated with injury risk.³⁷ More prospective studies are needed in the military training population to determine the role of kinetic factors on RRI risk.

Spatiotemporal variables of pace, step rate, step length, and contact time were also not associated with RRI risk in agreement with recent prospective studies.^15,51 Pooled data in a meta-analysis of retrospective studies revealed that step rate, step length, and contact time did not differ between healthy runners and those with a history of RRI.² A low step rate has been prospectively identified as a risk factor for shin injuries³⁵ and bone stress injuries²⁹ in competitive high school and collegiate cross-country runners but may not be applicable to our cadet population due to differences in running volume or other factors.

While gait retraining with the manipulation of biomechanical variables has been suggested as a tool to reduce RRI risk,^16,56 the results of our study suggest that foot strike pattern, rate of impact, pace, step rate, step length, or contact time may not influence RRI risk during cadet basic training.

Strengths and Limitations

To our knowledge, this is the largest prospective study to date to evaluate the association of personal characteristics and running biomechanical variables measured using a wearable sensor with RRI risk in a military population. Using a wearable sensor allowed for the collection of multiple data points over various runs, fatigue conditions, and speeds, increasing the ecological validity of our run assessment.

Several limitations should be considered during the interpretation of our results. Shoe selection was not controlled for during this study, leading to a heterogeneity of running shoe types (motion control, minimal or maximum cushioning, etc) self-selected by the cadets. Shoe type may influence some running biomechanical variables³² or RRI risk,³⁶ although further evidence is warranted. In a previous study, cadets wearing shoes with mild to moderate lateral torsion stiffness and heel heights were less likely to sustain an overuse lower extremity injury during cadet basic training compared with cadets wearing shoes with minimal lateral stiffness and heel heights.²³ Future studies should consider the influence of shoe (eg, type, age, and prior wear) in addition to personal and running characteristics on injury risk during military training.

Furthermore, self-reported mean weekly running distance before cadet basic training and prior history of injury in the previous 12 months are subject to recall bias. For prospective injuries occurring during cadet basic training, we chose to only include medically diagnosed RRIs due to the ease of medical access during cadet basic training and to acquire precise date of injury information, although this may have led to a lower injury incidence. As many as 86% of runners continue to train despite pain or injury,³⁴ and this may lead to underreporting of injuries, especially in a highly competitive environment such as cadet basic training. Although only RRIs were included during this study and those who experienced acute or non-RRIs were excluded, we cannot account for the role of cumulative load from prolonged standing, ruck marching with additional loads of up to 16 kg, other military training requirements,⁴² or the potential of reduced sleep hours¹² that could predispose cadets to overuse musculoskeletal injuries. Next, runs were conducted as group runs and may not be indicative of a runner’s self-selected speed and/or gait characteristics. As previously mentioned, questionable validity of the wearable sensor with regard to kinetic variables, as well as the inability to measure kinematic variables, limits a thorough running biomechanics assessment. Gait analysis with force plate data and motion capture would have been valuable, although not feasible given the time constraints of cadets during training. Our final and greatest limitation is that 338 sensors were lost, damaged, or never returned, limiting our overall viable data set and possibly leading to missed injury data.

Conclusion

In the current prospective study, non-modifiable risk factors such as female sex and prior injury history increased the risk of RRIs in cadets undergoing military training. After adjusting for prior injury, female sex persisted as a prominent risk factor for RRIs. Running biomechanical variables measured by a wearable sensor were not associated with injury in this study. As new wearable sensor technology emerges and improves, future prospective studies investigating RRI risk should assess running kinetics and kinematics in addition to spatiotemporal variables in a runner’s natural training environment.

Footnotes

Acknowledgements

The authors acknowledge the Baylor University–Keller Army Community Hospital Division 1 Sports Physical Therapy faculty and fellows as well as the Keller Army Community Hospital physical therapy staff for their assistance with data collection.

Final revision submitted June 17, 2024; accepted July 24, 2024.

One or more of the authors has declared the following potential conflict of interest or source of funding: This work was supported by the Military Operational Medicine Research Program (MOMRP), Defense Health Program JPC-5 (grant No. W911QY-16-1-0003). Additional support was provided by the Musculoskeletal Injury Rehabilitation Research for Operational Readiness (MIRROR), Department of Physical Medicine & Rehabilitation, Uniformed Services University, Bethesda, Maryland (HU00011920011). The opinions and assertions expressed herein are those of the author(s) and do not necessarily reflect the official policy or position of the Uniformed Services University or the Department of Defense. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

Ethical approval for this study was obtained from the Department of the Army, Regional Health Command–Atlantic (ref No. RHCA18014_905170).

References

Anderson

Grier

Dada

Canham-Chervak

Jones

BH.

The role of gender and physical performance on injuries: an Army study. Am J Prev Med. 2017;52(5):e131-e138.

Brindle

Taylor

Rajek

Weisbrod

Ford

KR.

Association between temporal spatial parameters and overuse injury history in runners: a systematic review and meta-analysis. Sports Med. 2019;50(2):331-342.

Burke

Dillon

O’Connor

, et al. Aetiological factors of running-related injuries: a 12 month prospective “Running Injury Surveillance Centre” (RISC) study. Sports Med Open. 2023;9(1):46.

Burke

Dillon

O’Connor

, et al. Risk factors for injuries in runners: a systematic review of foot strike technique and its classification at impact. Orthop J Sports Med. 2021;9(9):23259671211020283.

Ceyssens

Vanelderen

Barton

Malliaras

Dingenen

Biomechanical risk factors associated with running-related injuries: a systematic review. Sports Med. 2019;49(7):1095-1115.

Chapman

Laymon

Wilhite

, et al. Ground contact time as an indicator of metabolic cost in elite distance runners. Med Sci Sports Exerc. 2012;44(5):917-925.

Chidi-Ogbolu

Baar

Effect of estrogen on musculoskeletal performance and injury risk. Front Physiol. 2019;9:1834.

Chumanov

Wall-Scheffler

Heiderscheit

BC.

Gender differences in walking and running on level and inclined surfaces. Clin Biomech (Bristol, Avon). 2008;23(10):1260-1268.

Davis

Bowser

Mullineaux

DR.

Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. Br J Sports Med. 2016;50(14):887-892.

10.

de Almeida

Saragiotto

Yamato

Lopes

AD.

Is the rearfoot pattern the most frequently foot strike pattern among recreational shod distance runners?

Phys Ther Sport. 2015;16(1):29-33.

11.

DeJong Lempke

Hart

Hryvniak

Rodu

Hertel

Use of wearable sensors to identify biomechanical alterations in runners with exercise-related lower leg pain. J Biomech. 2021;126:110646.

12.

DeJong Lempke

Whitney

Collins

dHemecourt

Meehan

. Intrinsic and extrinsic factors contributing to running-related lower limb injuries among adolescent runners. J Sports Sci. 2022;40(22):2468-2474.

13.

Desai

Jungmalm

Börjesson

Karlsson

Grau

Recreational runners with a history of injury are twice as likely to sustain a running-related injury as runners with no history of injury: a 1-year prospective cohort study. J Orthop Sports Phys Ther. 2021;51(3):144-150.

14.

De Smet

Segers

Lenoir

De Clercq

. Spatiotemporal characteristics of spontaneous overground walk-to-run transition. Gait Posture. 2009;29(1):54-58.

15.

Dillon

Burke

Whyte

, et al. Running towards injury? A prospective investigation of factors associated with running injuries. PLoS One. 2023;18(8):e0288814.

16.

Doyle

Bonacci

Fuller

JT.

The effectiveness of gait retraining on running kinematics, kinetics, performance, pain, and injury in distance runners: a systematic review with meta-analysis. J Orthop Sports Phys Ther. 2022;52(4):192-A5.

17.

Francis

Whatman

Sheerin

Hume

Johnson

MI.

The proportion of lower limb running injuries by gender, anatomical location and specific pathology: a systematic review. J Sports Sci Med. 2019;18(1):21.

18.

Fredette

Roy

Perreault

, et al. The association between running injuries and training parameters: a systematic review. J Athl Train. 2022;57(7):650-671.

19.

Garnock

Witchalls

Newman

Predicting individual risk for medial tibial stress syndrome in Navy recruits. J Sci Med Sport. 2018;21(6):586-590.

20.

Gaudette

Bradach

de Souza Junior

, et al. Clinical application of gait retraining in the injured runner. J Clin Med. 2022;11(21):6497.

21.

Goss

Gross

MT.

A comparison of negative joint work and vertical ground reaction force loading rates in Chi runners and rearfoot-striking runners. J Orthop Sports Phys Ther. 2013;43(10):685-692.

22.

Hauret

Bedno

Loringer

, et al. Epidemiology of exercise- and sports-related injuries in a population of young, physically active adults: a survey of military servicemembers. Am J Sports Med. 2015;43(11):2645-2653.

23.

Helton

Cameron

Zifchock

, et al. Association between running shoe characteristics and lower extremity injuries in United States Military Academy cadets. Am J Sports Med. 2019;47(12):2853-2862.

24.

Hirschhorn

Yeargin

Mensch

Dompier

TP.

Injuries and referral patterns during basic combat training: an examination of data from the certified athletic trainer-forward program. Mil Med. 2023;188(1-2):e190-e197.

25.

Hollander

Johnson

Outerleys

Davis

IS.

Multifactorial determinants of running injury locations in 550 injured recreational runners. Med Sci Sports Exerc. 2021;53(1):102-107.

26.

Hollander

Rahlf

Wilke

, et al. Sex-specific differences in running injuries: a systematic review with meta-analysis and meta-regression. Sports Med. 2021;51(5):1011-1039.

27.

Johnson

Tenforde

Outerleys

Reilly

Davis

IS.

Impact-related ground reaction forces are more strongly associated with some running injuries than others. Am J Sports Med. 2020;48(12):3072-3080.

28.

Kakouris

Yener

Fong

DT.

A systematic review of running-related musculoskeletal injuries in runners. J Sport Health Sci. 2021;10(5):513-522.

29.

Kliethermes

Stiffler-Joachim

Wille

, et al. Lower step rate is associated with a higher risk of bone stress injury: a prospective study of collegiate cross country runners. Br J Sports Med. 2021;55(15):851-856.

30.

Krabak

Roberts

Tenforde

, et al. Youth running consensus statement: minimising risk of injury and illness in youth runners. Br J Sports Med. 2021;55(6):305-318.

31.

Kulmala

Avela

Pasanen

Parkkari

Forefoot strikers exhibit lower running-induced knee loading than rearfoot strikers. Med Sci Sports Exerc. 2013;45(12):2306-2313.

32.

Kulmala

Kosonen

Nurminen

Avela

Running in highly cushioned shoes increases leg stiffness and amplifies impact loading. Sci Rep. 2018;8(1):1-7.

33.

Leetun

Ireland

Willson

Ballantyne

Davis

IM.

Core stability measures as risk factors for lower extremity injury in athletes. Med Sci Sports Exerc. 2004;36(6):926-934.

34.

Linton

Valentin

Running with injury: a study of UK novice and recreational runners and factors associated with running related injury. J Sci Med Sport. 2018;21(12):1221-1225.

35.

Luedke

Heiderscheit

Williams

Rauh

MJ.

Influence of step rate on shin injury and anterior knee pain in high school runners. Med Sci Sports Exerc. 2016;48(7):1244-1250.

36.

Malisoux

Delattre

Urhausen

Theisen

Shoe cushioning influences the running injury risk according to body mass: a randomized controlled trial involving 848 recreational runners. Am J Sports Med. 2020;48(2):473-480.

37.

Malisoux

Gette

Delattre

Urhausen

Theisen

Spatiotemporal and ground-reaction force characteristics as risk factors for running-related injury: a secondary analysis of a randomized trial including 800+ recreational runners. Am J Sports Med. 2022;50(2):537-544.

38.

Mason

Pearson

Barry

, et al. Wearables for running gait analysis: a systematic review. Sports Med. 2023;53(1):241-268.

39.

Meeuwisse

Assessing causation in sport injury: a multifactorial model. Clin J Sport Med. 1994;4(3):166-170.

40.

Messier

Martin

Mihalko

, et al. A 2-year prospective cohort study of overuse running injuries: The Runners and Injury Longitudinal Study (TRAILS). Am J Sports Med. 2018;46(9):2211-2221.

41.

Miller

Crowell

Morris

Mason

Zifchock

Goss

DL.

Gait retraining improves running impact loading and function in previously injured US military cadets: a pilot study. Mil Med. 2021;186(11-12):e1071-e1076.

42.

Molloy

Pendergrass

Lee

Chervak

Hauret

Rhon

DI.

Musculoskeletal injuries and United States Army readiness part I: overview of injuries and their strategic impact. Mil Med. 2020;185(9-10):e1461-e1471.

43.

Morris

Goss

Miller

Davis

IS.

Using real-time biofeedback to alter running biomechanics: a randomized controlled trial. Transl Sports Med. 2020;3(1):63-71.

44.

Napier

MacLean

Maurer

Taunton

Hunt

MA.

Kinetic risk factors of running-related injuries in female recreational runners. Scand J Med Sci Sports. 2018;28(10):2164-2172.

45.

Noehren

Hamill

Davis

Prospective evidence for a hip etiology in patellofemoral pain. Med Sci Sports Exerc. 2013;45(6):1120-1124.

46.

Pairot de Fontenay

Roy

Dubois

Bouyer

Esculier

Validating commercial wearable sensors for running gait parameters estimation. IEEE Sensors J. 2020;20(14):7783-7790.

47.

Preacher

. Calculation for the chi-square test: an interactive calculation tool for chi-square tests of goodness of fit and independence. April 2001. Accessed December 12, 2024. http://quantpsy.org

48.

Roy

Faller

Richardson

Taylor

KM.

Characterization of limited duty neuromusculoskeletal injuries and return to duty times in the US Army during 2017-2018. Mil Med. 2022;187(3-4):e368-376.

49.

Stoltenberg

Miller

Dolbeer

Pickens

Goss

DL.

Validity of an instrumented sock and on-shoe sensor to provide biometric feedback to runners. Footwear Sci. 2019;11(3):147-152.

50.

Suydam

Evans

Willy

RW.

Community-based running measures associated to pain. Presented at: American Physical Therapy Association’s Combined Sections Meeting; February 15-18, 2017; San Antonio, TX.

51.

Szymanek

Miller

Weart

Morris

Goss

DL.

Is step rate associated with running injury incidence? An observational study with 9-month follow up. Int J Sports Phys Ther. 2020;15(2):221-228.

52.

The R Project for Statistical Computing. 2018. Accessed December 12, 2024. https://www.R-project.org

53.

van der Worp

Vrielink

Bredeweg

SW.

Do runners who suffer injuries have higher vertical ground reaction forces than those who remain injury-free? A systematic review and meta-analysis. Br J Sports Med. 2016;50(8):450-457.

54.

Weart

Miller

Brindle

Ford

Goss

DL.

Wearable technology assessing running biomechanics and prospective running-related injuries in Active Duty Soldiers. Sports Biomech. 2023:1-17.

55.

Willson

Sharpee

Meardon

Kernozek

TW.

Effects of step length on patellofemoral joint stress in female runners with and without patellofemoral pain. Clin Biomech (Bristol, Avon). 2014;29(3):243-247.

56.

Willy

Buchenic

Rogacki

Ackerman

Schmidt

Willson

JD.

In-field gait retraining and mobile monitoring to address running biomechanics associated with tibial stress fracture. Scand J Med Sci Sports. 2016;26(2):197-205.

57.

Winter

Gordon

Brice

Lindsay

Barrs

A multifactorial approach to overuse running injuries: a 1-year prospective study. Sports Health. 2020;12(3):296-303.

58.

Yuan

Wang

Liu

Zhou

Effects of foot strike techniques on running biomechanics: a systematic review and meta-analysis. Sports Health. 2020;13(1):71-77.

59.

Yamato

Saragiotto

Lopes

AD.

A consensus definition of running-related injury in recreational runners: a modified Delphi approach. J Orthop Sports Phys Ther. 2015;45(5):375-380.

60.

Yates

White

The incidence and risk factors in the development of medial tibial stress syndrome among naval recruits. Am J Sports Med. 2004;32(3):772-780.