A hierarchical clustering approach for examining potential risk factors for bone stress injury in runners

Factors Associated With High-Risk and Low-Risk Bone Stress Injury in Female Runners: Implications for Risk Factor Stratification and Management

Background

Bone stress injury (BSI) is a common overuse injury in active women. BSIs can be classified as high-risk (pelvis, sacrum, and femoral neck) or low-risk (tibia, fibula, and metatarsals). Risk factors for BSI include low energy availability, menstrual dysfunction, and poor bone health. Higher vertical load rates during running have been observed in women with a history of BSI.

Purpose/Hypothesis

The purpose of this study was to characterize factors associated with BSI in a population of premenopausal women, comparing those with a history of high-risk or low-risk BSI with those with no history of BSI. It was hypothesized that women with a history of high-risk BSI would be more likely to exhibit lower bone mineral density (BMD) and related factors and less favorable bone microarchitecture compared with women with a history of low-risk BSI. In contrast, women with a history of low-risk BSI would have higher load rates.

Study Design

Cross-sectional study; Level of evidence, 3.

Methods

Enrolled were 15 women with a history of high-risk BSI, 15 with a history of low-risk BSI, and 15 with no history of BSI. BMD for the whole body, hip, and spine was standardized using z scores on dual-energy x-ray absorptiometry. High-resolution peripheral quantitative computed tomography was used to quantify bone microarchitecture at the radius and distal tibia. Participants completed surveys characterizing factors that influence bone health-including sleep, menstrual history, and eating behaviors-utilizing the Eating Disorder Examination Questionnaire (EDE-Q). Each participant completed a biomechanical assessment using an instrumented treadmill to measure load rates before and after a run to exertion.

Results

Women with a history of high-risk BSI had lower spine z scores than those with low-risk BSI (-1.04 ± 0.76 vs -0.01 ± 1.15; P < .05). Women with a history of high-risk BSI, compared with low-risk BSI and no BSI, had the highest EDE-Q subscores for Shape Concern (1.46 ± 1.28 vs 0.76 ± 0.78 and 0.43 ± 0.43) and Eating Concern (0.55 ± 0.75 vs 0.16 ± 0.38 and 0.11 ± 0.21), as well as the greatest difference between minimum and maximum weight at current height (11.3 ± 5.4 vs 7.7 ± 2.9 and 7.6 ± 3.3 kg) (P < .05 for all). Women with a history of high-risk BSI were more likely than those with no history of BSI to sleep <7 hours on average per night during the week (80% vs 33.3%; P < .05). The mean and instantaneous vertical load rates were not different between groups.

Conclusion

Women with a history of high-risk BSI were more likely to exhibit risk factors for poor bone health, including lower BMD, while load rates did not distinguish women with a history of BSI.

Running-Related Injury Incidence: Does It Correlate with Kinematic Sub-groups of Runners? A Scoping Review

BACKGROUND

Historically, kinematic measures have been compared across injured and non-injured groups of runners, failing to take into account variability in kinematic patterns that exist independent of injury, and resulting in false positives. Research led by gait patterns and not pre-defined injury status is called for, to better understand running-related injury (RRI) aetiology and within- and between-group variability.

OBJECTIVES

Synthesise evidence for the existence of distinct kinematic sub-groups across a population of injured and healthy runners and assess between-group variability in kinematics, demographics and injury incidence.

DATA SOURCES

Electronic database search: PubMed, Web of Science, Cochrane Central Register of Controlled Trials (Wiley), Embase, OVID, Scopus.

ELIGIBILITY CRITERIA

Original, peer-reviewed, research articles, published from database start to August 2022 and limited to English language were searched for quantitative and mixed-methods full-text studies that clustered injured runners according to kinematic variables.

RESULTS

Five studies (n = 690) were included in the review. All studies detected the presence of distinct kinematic sub-groups of runners through cluster analysis. Sub-groups were defined by multiple differences in hip, knee and foot kinematics. Sex, step rate and running speed also varied significantly between groups. Random injury dispersal across sub-groups suggests no strong evidence for an association between kinematic sub-groups and injury type or location.

CONCLUSION

Sub-groups containing homogeneous gait patterns exist across healthy and injured populations of runners. It is likely that a single injury may be represented by multiple movement patterns, and therefore kinematics may not predict injury risk. Research to better understand the underlying causes of kinematic variability, and their associations with RRI, is warranted.

Increasing Step Rate Reduces Peak and Cumulative Insole Force in Collegiate Runners

PURPOSE

The primary goal of this study was to examine changes in peak insole force and cumulative weighted peak force (CWPF)/km with increased step rate in collegiate runners. The secondary goal was to determine whether sacral acceleration correlates with insole force when increasing step rate.

METHODS

Twelve collegiate distance runners ran 1000 m outdoors at 3.83 m·s -1 at preferred and 10% increased step rates while insole force and sacral acceleration were recorded. Cumulative weighted peak force/km was calculated from insole force based on cumulative damage models. The effects of step rate on peak insole force and CWPF·km -1 were tested using paired t tests or Wilcoxon tests. Correlation coefficients between peak axial (approximately vertical) sacral acceleration times body mass and peak insole force were calculated on cohort and individual levels.

RESULTS

Peak insole force and CWPF·km -1 decreased ( P < 0.001) with increased step rate. Peak axial sacral acceleration did not correlate with peak insole force on the cohort level ( r = 0.35, P = 0.109) but did within individuals (mean, r = 0.69-0.78; P < 0.05).

CONCLUSIONS

Increasing step rate may reduce peak vGRF and CWPF·km -1 in collegiate runners. Therefore, clinicians should consider step rate interventions to reduce peak and cumulative vGRF in this population. Individual-specific calibrations may be required to assess changes in peak vGRF in response to increasing step rate using wearable accelerometers.

Predicting overstriding with wearable IMUs during treadmill and overground running

Running injuries are prevalent, but their exact mechanisms remain unknown largely due to limited real-world biomechanical analysis. Reducing overstriding, the horizontal distance that the foot lands ahead of the body, may be relevant to reducing injury risk. Here, we leverage the geometric relationship between overstriding and lower extremity sagittal segment angles to demonstrate that wearable inertial measurement units (IMUs) can predict overstriding during treadmill and overground running in the laboratory. Ten recreational runners matched their strides to a metronome to systematically vary overstriding during constant-speed treadmill running and showed similar overstriding variation during comfortable-speed overground running. Linear mixed models were used to analyze repeated measures of overstriding and sagittal segment angles measured with motion capture and IMUs. Sagittal segment angles measured with IMUs explained 95% and 98% of the variance in overstriding during treadmill and overground running, respectively. We also found that sagittal segment angles measured with IMUs correlated with peak braking force and explained 88% and 80% of the variance during treadmill and overground running, respectively. This study highlights the potential for IMUs to provide insights into landing and loading patterns over time in real-world running environments, and motivates future research on feedback to modify form and prevent injury.

Risk Factors for Running-Related Injury in High School and Collegiate Cross-country Runners: A Systematic Review

OBJECTIVE: To summarize and describe risk factors for running-related injuries (RRIs) among high school and collegiate cross-country runners. DESIGN: Descriptive systematic review. LITERATURE SEARCH: Four databases (Scopus, SPORTDiscus, CINAHL, Cochrane) were searched from inception to August 2023. STUDY SELECTION CRITERIA: Studies assessing RRI risk factors in high school or collegiate runners using a prospective design with at least 1 season of follow-up were included. DATA SYNTHESIS: Results across each study for a given risk factor were summarized and described. The NOS and GRADE frameworks were used to evaluate quality of each study and certainty of evidence for each risk factor. RESULTS: Twenty-four studies were included. Overall, study quality and certainty of evidence were low to moderate. Females or runners with prior RRI or increased RED-S (relative energy deficiency in sport) risk factors were most at risk for RRI, as were runners with a quadriceps angle of >20° and lower step rates. Runners with weaker thigh muscle groups had increased risk of anterior knee pain. Certainty of evidence regarding training, sleep, and specialization was low, but suggests that changes in training volume, poorer sleep, and increased specialization may increase RRI risk. CONCLUSION: The strongest predictors of RRI in high school and collegiate cross-country runners were sex and RRI history, which are nonmodifiable. There was moderate certainty that increased RED-S risk factors increased RRI risk, particularly bone stress injuries. There was limited evidence that changes in training and sleep quality influenced RRI risk, but these are modifiable factors that should be studied further in this population. J Orthop Sports Phys Ther 2024;54(2):1-13. Epub 16 November 2023. doi:10.2519/jospt.2023.11550.

Preseason Vertical Center of Mass Displacement During Running and Bone Mineral Density Z-Score Are Risk Factors for Bone Stress Injury Risk in Collegiate Cross-country Runners

OBJECTIVES: To (1) assess relationships between running biomechanics, bone health, and bone stress injuries (BSIs), and (2) determine which variables constitute the most parsimonious BSI risk model among collegiate cross-country runners. DESIGN: Prospective, observational cohort study. METHODS: Running gait and bone mineral density (BMD) data from healthy collegiate cross-country runners were collected at preseason over 6 seasons. A generalized estimating equation model with backward selection was used to develop the most parsimonious model for estimating BSI risk, controlling for sex, running speed, and prior BSI. The variables assessed were spatiotemporal, ground reaction force, and joint kinematics, based on previous literature. Quasi-likelihood under the independence model criterion values and R2 values were used to select the best-fitting model. RESULTS: Data from 103 runners were included in the analysis. The best-fitting model included vertical center of mass (COM) displacement and BMD z-score. Injury risk increased with greater vertical COM displacement (unit = 0.5 cm; relative risk [RR] = 1.14; 95% confidence interval [CI]: 1.01, 1.29; P = .04) and decreased with greater BMD z-score (unit = 0.5; RR = 0.83; 95% CI: 0.72, 0.95; P = .007). The model performed similarly when step rate was included instead of vertical COM displacement. CONCLUSION: Vertical COM displacement and BMD z-score contributed to the best model for estimating risk the risk of bone stress injury in cross-country runners. Step rate was also an important variable for assessing injury risk. J Orthop Sports Phys Ther 2023;53(12):1-8. Epub 20 October 2023. doi:10.2519/jospt.2023.11860.

A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners

Bone stress injury (BSI) risk in runners is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by identifying groups of similar runners within a population which differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners based on 2-dimensional frontal plane pelvis and proximal femur geometry, which was extracted from dual-energy X-ray absorptiometry scans and dimensionally reduced by principal component analysis. Seven distinct groups were identified using the cluster tree, with the initial split being highly related to female-male differences. Visual inspection revealed clear differences between groups in pelvis and proximal femur geometry, and groups were found to differ in lower body BSI incidence during the subsequent academic year (Rand index = 0.53; adjusted Rand index = 0.07). Linear models showed between-cluster differences in visually identified geometric measures. Geometric measures were aggregated into a pelvis shape factor based on trends with BSI incidence, and the resulting shape factor was significantly different between clusters (p < 0.001). Lower shape factor values, corresponding with lower pelvis height and ischial span, and greater iliac span and trochanteric span, appeared to be related to increased BSI incidence. This trend was dominated by the effect observed across clusters of male runners, indicating that geometric effects may be more relevant to BSI risk in males, or that other factors masked the relationship in females. More broadly, this work outlines a methodological approach for distilling complex geometric differences into simple metrics that relate to injury risk.

The "impacts cause injury" hypothesis: Running in circles or making new strides?

Some of the earliest biomechanics research focused on running and the ground reaction forces generated with each step. Research in running gait accelerated in the 1970's as the growing popularity in running increased attention to the musculoskeletal injuries sustained by runners. Despite decades of high-quality research, running remains the most common cause of exercise-related musculoskeletal injuries and rates of overuse running-related injuries (RRI) have not appreciably declined since the research began. One leading area of running gait research focuses on discrete variables derived from the vertical ground reaction force, such as the vertical loading rate. Across sub-disciplines of running gait research, vertical loading rate is often discussed as the primary and undisputed variable associated with RRI despite only low to moderate evidence that retrospectively or prospectively injured runners generate greater vertical loading rates than uninjured counterparts. The central thesis of this review is that relying on vertical loading rate is insufficient to establish causal mechanisms for RRI etiology. To present this argument, this review examines the history of the 'impacts cause injury' hypothesis, including a historical look at ground reaction forces in human running and the research from which this hypothesis was generated. Additionally, a synthesis of studies that have tested the hypothesis is provided and recommendations for future research are discussed. Although it is premature to reject or support the 'impacts cause injury' hypothesis, new knowledge of biomechanical risk factors for RRI will remain concealed until research departs from the current path or adopts new approaches to previous paradigms.

Unsupervised machine learning methods and emerging applications in healthcare

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.