Browsing by Author "Buckley, Thomas A"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    English professional football players concussion knowledge and attitude
    (Shanghai University Sport, 5/23/15) Williams,Joshua M.; Langdon,Jody L.; McMillan,James L.; Buckley,Thomas A.; Joshua M. Williams , Jody L. Langdon, James L. McMillan, Thomas A. Buckley; Buckley, Thomas A
    Background: Concussions are a common pathology in football and multiple misconceptions exist amongst the players and managers. To address these misconceptions, and potentially reduce concussion associated sequela, effective educational interventions need to be Developmenteloped. However, the current knowledge and attitude status must be ascertained to appropriately Developmentelop these interventions. The purpose of this study was to assess the concussion knowledge and attitude of English professional footballers. Methods: Twenty-six participants from one English Football League Championship club completed the study. A mixed methods approach included the Rosenbaum Concussion Knowledge and Attitudes Survey (RoCKAS) and a semi-structured interview. The RoCKAS contains separate knowledge (0-25) and attitude (15-75) scores and was followed by a semi-structured interview consisting of concussion knowledge, attitude, and behavior related questions. Results: The mean score on the RoCKAS knowledge was 16.4 +/- 2.9 (range 11-22) and the attitude score was 59.6 +/- 8.5 (range 41-71). The interview responses identified inconsistencies between the RoCKAS and the intended behaviors, endorsing multiple concussion misconceptions, and revealed barriers to concussion reporting. Conclusion: The results of this study suggest that Championship Level English footballers have moderate concussion knowledge, safe attitudes, and good concussion symptom recognition when assessed with pen and paper questionnaires. However, within the semi-structured interview many respondents reported unsafe concussion behaviors despite accurately identifying the potential risks. Further, multiple barriers to concussion reporting were identified which included perceived severity of the injury, game situations, and the substitution rule. These findings can help form the foundation of educational interventions to potentially improve concussion reporting behaviors amongst professional footballers. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.
  • Thumbnail Image
    Item
    Integrative data analysis to identify persistent post-concussion deficits and subsequent musculoskeletal injury risk: project structure and methods
    (BMJ Open Sport & Exercise Medicine, 2024-01-19) Anderson, Melissa; Claros, Claudio Cesar; Qian, Wei; Brockmeier, Austin; Buckley, Thomas A
    Concussions are a serious public health problem, with significant healthcare costs and risks. One of the most serious complications of concussions is an increased risk of subsequent musculoskeletal injuries (MSKI). However, there is currently no reliable way to identify which individuals are at highest risk for post-concussion MSKIs. This study proposes a novel data analysis strategy for developing a clinically feasible risk score for post-concussion MSKIs in student-athletes. The data set consists of one-time tests (eg, mental health questionnaires), relevant information on demographics, health history (including details regarding the concussion such as day of the year and time lost) and athletic participation (current sport and contact level) that were collected at a single time point as well as multiple time points (baseline and follow-up time points after the concussion) of the clinical assessments (ie, cognitive, postural stability, reaction time and vestibular and ocular motor testing). The follow-up time point measurements were treated as individual variables and as differences from the baseline. Our approach used a weight-of-evidence (WoE) transformation to handle missing data and variable heterogeneity and machine learning methods for variable selection and model fitting. We applied a training-testing sample splitting scheme and performed variable preprocessing with the WoE transformation. Then, machine learning methods were applied to predict the MSKI indicator prediction, thereby constructing a composite risk score for the training-testing sample. This methodology demonstrates the potential of using machine learning methods to improve the accuracy and interpretability of risk scores for MSKI.