Effects of Recent Concussion and Injury History on Instantaneous Relative Risk of Lower Extremity Injury in Division I Collegiate Athletes

Fino, Peter C.; Becker, Lauren N.; Fino, Nora; Griesemer, B. A.; Goforth, Michael; Brolinson, P. Gunnar

doi:10.1097/jsm.0000000000000502

Cited by 54 publications

(57 citation statements)

References 33 publications

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“…This hypothesis was correct as those with a history of SRC had a 1.88 times greater likelihood of incurring an LEMSK injury compared with the control group (Table 1). These data support earlier findings 14,15 that SRC was associated with an increased risk of LE injury. Furthermore, our result closely matched that of previous authors 14 (relative risk ratio ¼ 1.88 versus 1.97) and thus supports the growing body of literature indicating that a history of SRC is associated with LE injury.…”

Section: Discussionsupporting

confidence: 92%

“…Lastly, a history of SRC could be detected using the CI, and a history of SRC was associated with a 1.88 times elevated risk of LEMSK injury. In agreement with prior research, [14][15][16][17][18][19] a history of SRC was associated with an increased LE injury risk; however, baseline postural-control metrics did not predict LEMSK injury before injury occurrence among those with a history of SRC. This may suggest that postural-control metrics do not play a role in predicting LEMSK injury incidence after full clinical recovery from SRC or that quiet upright stance is not sufficiently challenging to detect prospective LEMSK.…”

Section: Discussionsupporting

confidence: 88%

“…Current evidence [14][15][16][17][18][19] suggests that lower extremity musculoskeletal (LEMSK) injuries are more prevalent among those with an SRC history. Specifically, within the first year after SRC recovery, affected athletes are 1.9 to 3.5 times more likely to sustain an acute lower extremity (LE) injury.…”

mentioning

confidence: 99%

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Baseline Postural Control and Lower Extremity Injury Incidence Among Those With a History of Concussion

Murray

Belson

Szekely

et al. 2020

Journal of Athletic Training

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Context Lower extremity musculoskeletal (LEMSK) injury may be more prevalent among those with a history of sport-related concussion (SRC). Objective To investigate the relationship between baseline postural control metrics and the LEMSK injury incidence in National Collegiate Athletic Association Division I student-athletes with a history of SRC. Setting National Collegiate Athletic Association Division I athletes. Design Cohort study. Patients or Other Participants Of 84 total athletes (62 males), 42 had been previously diagnosed with an SRC, and 42 were matched controls based on age, sex, height, weight, and sport. Main Outcome Measure(s) During the preseason baseline evaluation, all participants performed 3 trials of eyes-open and eyes-closed upright quiet stance on a force platform. Medical charts were assessed for all the LEMSK injuries that occurred from preseason baseline to 1 year later. Center-of-pressure data in the anteroposterior and mediolateral directions were filtered before we calculated root mean square and mean excursion velocity; the complexity index was calculated from the unfiltered data. Factorial analysis-of-variance models were used to examine differences between groups and across conditions for root mean square; mean excursion velocity, complexity index, and tests of association to examine between-groups LEMSK differences; and logistic regression models to predict LEMSK. Results Concussion history and injury incidence were related in the SRC group (P = .043). The complexity index of the SRC group was lower with eyes closed (14.08 ± 0.63 versus 15.93 ± 0.52) and eyes open (10.25 ± 0.52 vs 11.80 ± 0.57) in the mediolateral direction than for the control participants (P < .05). Eyes-open root mean square in the mediolateral direction was greater for the SRC group (5.00 ± 0.28 mm) than the control group (4.10 ± 0.22 mm). Logistic regression models significantly predicted LEMSK only in control participants. Conclusions These findings may suggest that LEMSK after SRC cannot be predicted from postural-control metrics at baseline.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 88%

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Baseline Postural Control and Lower Extremity Injury Incidence Among Those With a History of Concussion

Murray

Belson

Szekely

et al. 2020

Journal of Athletic Training

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“…Finally, a recent study by Fino et al. () also demonstrated the use of Cox regression to analyze lower extremity injury occurrence following a concussion.…”

Section: Methodsmentioning

confidence: 99%

“…The methodological approaches used in this study are robust and have been extensively used. For example, Flynn Fino et al (2017) also demonstrated the use of Cox regression to analyze lower extremity injury occurrence following a concussion. To make the model useful for decision-making purposes, continuous values of injury variables are first converted to binary categorical variables (i.e., high-risk vs. low-risk classification) that can be directly compared to one another in terms of strength of association with injury occurrence (e.g., Pepe, Janes, Longton, Leisenring, & Newcomb, 2014;Bahr, 2016).…”

Section: Methodsmentioning

confidence: 99%

Who is More Injury‐Prone? Prediction and Assessment of Injury Risk

et al. 2018

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Injuries are the primary determinant of an individual's mobility, which affect not just their workplace productivity in intensive environments such as manufacturing, but also their decision‐making ability and quality of life. Managers typically assign workers to projects or tasks without having knowledge about their functional capabilities or current state of injury risk as injuries remain highly underreported at workplaces for fear of reprisal and other reasons. Therefore, high‐quality research on injury prevention is nearly nonexistent. Procedures that we use in this study for developing a prediction model for identification of college football players at an elevated injury risk could also be used to quantify injury risk in various occupational settings. Using a number of measurements and models, we arrive at an estimate of an individual's injury likelihood. Our measures include ratings of movement efficiency through physical performance tests, acceleration using Internet of Things (IoT) devices, functional role classifications, and recorded exposures to high‐risk conditions. Findings prescribe several approaches and decision rules for prediction of injury risk and suggest that training programs need to consider an individual's injury risk rather than offer a ‘one‐size‐fits‐all’ approach. The analytics models derived from a combination of injury risk screening and surveillance data can be used for making decisions about targeting employee‐centric risk‐reduction interventions, improved matching of tasks to individuals, or deciding job rotation for improved performance, all while enhancing the quality of life of individuals and reducing the escalating costs of work‐related injuries borne by employers. These models can also be developed for smartphones.

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Injury patterns in U15 rugby players in Ulster schools: A Rugby Injury Surveillance (RISUS) Study

Archbold

Rankin²,

Webb

et al. 2021

Transl Sports Med

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Participation in collision sports such as rugby union has inherent risk of injury. The aim of this study was to examine injury patterns and risk factors in adolescent rugby players (U15 years). A prospective injury surveillance study was undertaken involving 26 schools (599 male rugby players, mean age 14.7years). Data were collected over the 2016/17 playing season, with injuries classified by body part and diagnosis. We explored the association between risk factors (demographic/biometric; injury history, protective equipment, training profile, level of play) using cox proportional hazard models with time to injury as the dependent variable. Nearly, 30% of players (178/599) suffered at least one injury. Injury incidence is estimated at 14.7 injuries/1000 match hours. Most injuries occurred in the tackle situation (64.6%, 135/209). Common injury sites were head/face (n = 95, 45.5%), ankle (n = 21, 10.0%), or wrist/hand (n = 15, 7.2%). Concussion (n = 79, 37.8%), wrist/ hand fractures (n = 19, 9.1%), or ankle sprains (n = 16, 7.6%) were the most common diagnoses. Seven concussions (8.9%) resulted in more than 28 days absence from play. A higher risk of injury was associated with: previous concussion (AHR 1.45; 95% CI 1.02 to 2.06), and any previous injury (AHR 1.85; 95% CI 1.23 to 2.78). One in three U15 rugby players sustained an injury over a single playing season. Concussion remains a concern in this age group and should be prioritized in future research.

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Effects of Recent Concussion and Injury History on Instantaneous Relative Risk of Lower Extremity Injury in Division I Collegiate Athletes

Cited by 54 publications

References 33 publications

Baseline Postural Control and Lower Extremity Injury Incidence Among Those With a History of Concussion

Baseline Postural Control and Lower Extremity Injury Incidence Among Those With a History of Concussion

Who is More Injury‐Prone? Prediction and Assessment of Injury Risk

Injury patterns in U15 rugby players in Ulster schools: A Rugby Injury Surveillance (RISUS) Study

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