Date of Degree
MS (Master of Science)
First Committee Member
Second Committee Member
Third Committee Member
With American football becoming increasingly popular in the United States, more efforts have been made on technology and advancements to reduce the number of injuries sustained by athletes. However, few studies have been conducted to evaluate how the field type, field condition, and shoe type interact with each other to induce injuries. To fill in the gap in the literature, this thesis used epidemiological methods to investigate the effect of field condition and shoe type on lower extremity injuries, specifically knee and ankle injuries, using real player data from the 2008, 2009, and 2010 football seasons from The University of Iowa. Results showed that over three seasons, 189 athletes experienced approximately 38,000 football exposures in 312 days. The athletes endured 250 injuries, in which 129 (51%) occurred in the lower extremity. Of all injuries, 34 (14%) involved the knee and 30 (12%) involved the ankle. Most of the lower extremity injuries, specifically knee injuries and ankle injuries, were of the joint (non-bone) and ligament type. Practices contributed to 73% of exposures, 11% for games, and 16% for other over the three sessions studied. 65% of all exposures occurred on an artificial surface, compared to 36% of all exposures that occurred on a natural surface. Most games were played on a natural surface (56%), while most practices occurred on an artificial surface (56%). For surface condition, 89% of all exposures were categorized as a normal condition compared to the 11% categorizes as a not normal condition. Most athletes used shoes with 9-12 cleats compared to shoes with 7, more than 12, or no cleats. In addition, most athletes used shoes with a high top at the shoe opening compared to a low top and shoes with short cleat lengths compared to long cleat lengths. The field condition variable (not normal vs. normal) was the only unadjusted GLM with significant results for all lower extremity injuries (Chi-square p-value=0.0307) and ankle injuries specifically (Chi-square p-value=0.0253). When the predictor variables were adjusted for team activity (i.e., games and practices) only the playing surface model was significant for all terms, including team activity (Chi-square p-value=0.0018), surface (Chi-square p-value=0.029), and the interaction term (Chi-square p-value=0.0189). This model was further analyzed for practice and games separately, and it was found that surface was significant in predicting lower extremity injuries in a game setting (Chi-square p-value=0.005). For all lower extremity injuries, the odds of having a lower extremity injury on an artificial surface in a game setting was 2.89 times more likely than on a natural surface. For the condition, top height, and number of cleat models, only the team activity term was found significant (Chi-square p-value=0.0143, <.0001, and 0.0038, respectively). When these models were further analyzed for practice and games separately, only field condition was found to be significant in a practice setting. For all lower extremity injuries, the odds of having a lower extremity injury in a not normal condition in a practice setting was 2.04 times more likely than in a normal condition. The cleat length model was not found to be significant when adjusting for team activity. The results of this analysis provide a foundation for future studies to understand why several extrinsic risk factors may be associated with lower extremity injuries.
American Football, Injury, Lower Extremity
viii, 91 pages
Includes bibliographical references (pages 87-91).
Copyright 2011 Jaclyn Iacovelli