M.S. - Kinesiology and Rehabilitation Science
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Item Subjective readiness in American football: Evaluating the effects of travel on player readiness(2025) Blanchette, Grace; Yamada, Paulette; Kinesiology and Rehabilitation ScienceItem The influence of injury status on neuromuscular status monitoring in collegiate beach volleyball athletes(2025) Gangle, Hailie Meeshel; Yamada, Paulette; Kinesiology and Rehabilitation ScienceItem THE EFFECT OF ACL RECONSTRUCTION ON TOTAL JOINT WORK AND JOINT WORK CONTRIBUTIONS(2024) Tanuvasa, Eliki; Stickley, Christopher; Kinesiology and Rehabilitation ScienceItem STROBOSCOPIC VISUAL DISRUPTION EFFECTS ON RUNNING BIOMECHANICS BETWEEN POST-OPERATIVE ACLR PATIENTS AND HEALTHY CONTROLS(2024) Merrill, Alexis; Stickley, Christopher; Kinesiology and Rehabilitation ScienceItem Sex Differences in Long-term Running Knee Loading Mechanics Between Anterior Cruciate Ligament Reconstructed and Healthy Controls(2024) Gin, Malia Xiao; Stickley, Christopher; Kinesiology and Rehabilitation ScienceItem Effects of Head Impact Exposure in High School Football Athletes on Cognitive Performance and Symptom Prevalence(2024) Meyer, Lauren; Freemyer, Bret; Kinesiology and Rehabilitation ScienceItem Head Impact Agreement on Video Review Differs Across Rater Experience in High School Football.(2023) Roblero, Elissa J.; Freemyer, Bret; Kinesiology and Rehabilitation ScienceItem Positional Differences In Helmet Impact Exposure Rates In Hawaiian High School Football Athletes(University of Hawaii at Manoa, 2023) Glodowski, Kiera; Freemyer, Bret; Kinesiology and Rehabilitation ScienceContext: Previous research has shown the importance of analyzing head impact exposure (HIE) among high school football players, yet few studies explore the difference across position groups. Therefore, the purpose of this study was to analyze head impact frequency between position groups.Objective: Quantify the difference of impacts per exposure (Imp/E) between position groups in high school football during the 2019 and 2021 seasons. Design: Cross-sectional study. Setting: Three high school varsity football teams on O'ahu. Patients and Participants: 200 varsity football players featuring 69 offensive/defensive linemen ( 16.0 ± 0.9 yrs, 180.4 ± 7.7 cm, 87.0 ± 22.4 kg; linemen), 51 linebackers/running backs/tight ends (16 ± 0.8 yrs, 176.6 ± 7.1 cm, 83.7 ± 13.8 kg; backers) and 80 wide receivers/defensive backs (16.1 ± 1.0 yrs, 175.4 ± 6.3 cm, 71.72 ± 10.32 kg; skills). Main Outcome Measures: Games, practice, and total Imp/E analyzed across positions, teams, and years. Results: A difference was observed across position groups (P<0.001). Season was not found to be a factor (P<0.446). A significant interaction (P<0.002) exists between team B and C (P=0.021), but not between A and C. Post-hoc analysis confirms that the backers (3.37 [95% CI: 2.80, 3.949]) experience a higher HIE compared to linemen (1.55 [95%CI: 1.067, 2.049]) and skill players (1.57 [95%CI: 1.124, 2.035]). Conclusion: To our knowledge, this is the first study of this size comparing position groups in terms of Imp/E of the given population across two seasons. These data demonstrate that linebackers and running backs experience more impacts per exposure, indicating the influence of running the ball or directly defending the ball has on increased head impacts. Future research, such as player risk-compensation, impact location across position groups in the given population, or alteration of coaching techniques and style of play could contribute to overall risk reduction of head impact exposure.Key Words: Head impact frequency, Riddell Insite, traumatic brain injury Word Count: 349 Key Points: Running backs, tight ends, and linebackers experienced a greater number of impacts per exposure in high school football players. Analyzing head impacts per exposure has the potential to identify factors contributing to increased frequency of head impacts and patterns across position groups within a team.Item Utilizing Force-Velocity Profiles to Improve Athletic Performance in American Football Players(University of Hawaii at Manoa, 2023) Ishihara, Ryan; Yamada, Paulette M. M.; Kinesiology and Rehabilitation ScienceIntroduction: American football requires strength, power, and speed. Force-velocity (FV) profiling describes the relationship between muscular force production and contraction velocity, and when multiplied together, the resulting product is power. FV profiling uses loaded jumps in the vertical plane and running speed in the horizontal direction to predict the imbalance between velocity and force development. Purpose: The first purpose of this study was to determine if FV profiling and optimized training improves performance metrics in American collegiate football players (i.e., countermovement vertical jump (CMJ), flying 10’s sprinting speed, 1-repetition maximum (1-RM) barbell back squat, 1-RM power clean). The second purpose was to determine if a 6-week optimized training regimen would correct FV imbalances. Methods: Eighty-two male, division-I collegiate American football athletes (20.7 ± 1.5 years old) provided written informed consent to participate in this study. Subjects were grouped by their position (i.e., offense or defense). Each subject participated in unloaded and loaded squat jumps (for vertical FV profiling) and unloaded sprints (for horizontal FV profiling). The following day, sprinting speed during a flying 10 and a full clean 1-repetition maximum (1-RM) were measured. Either 24 or 48 hours later, 2 additional performance metrics were measured: CMJ and a 1-RM back squat. Results: Of the 82 football players enrolled in the study, 50 subjects completed the protocol. The vertical FV profiling revealed that 9 athletes were velocity-deficient, 16 athletes were well-balanced, and 25 athletes were force-deficient. Descriptive statistics showed that the %FV imbalance of the velocity- and force-deficient groups decreased or increased, respectively, and each group’s imbalance moved toward the well-balanced category (90-110%). Results indicate that %FV imbalances improved as a result of the training in the velocity- and force-deficient groups, and the well-balanced group retained its well-balanced standing. Conclusion: Six weeks of individualized training was sufficient to improve performance metrics in collegiate American football players and notably, correct FV imbalances. Utilization of FV profiling catered to the individual needs of athletes as opposed to a one-size-fits-all approach, where all athletes utilize the same training program.Item Helmetless Tackling Training Intervention And Preseason Self-efficacy Effects On Head Impacts In Hawaiʻi High School Football(University of Hawaii at Manoa, 2023) Lloansi Rodriguez, Ivet; Freemyer, Bret; Kinesiology and Rehabilitation ScienceObjective: Head impacts in football may be influenced by many factors, such as proper tackling and blocking techniques, years of experience, or self-efficacy. However, how these factors affect or relate to each other has not been investigated. Therefore, examining how head impacts in football are influenced by preseason self-efficacy (SE), intervention participation (IP), and years of experience (YE) playing organized tackle football may provide stakeholders with information to reduce head injuries. Design: Quantitative research consisting of a cross-sectional self-efficacy survey, a tackling training intervention, and a head impact monitoring system. Setting: Local high schools’ football fields. Participants: Initially 232 participants were recruited from five high school football teams as part of a larger study investigating the effectiveness of a helmetless tackling intervention. Final sample size of (n=120: male; n=118, female; n=2, 15.57±1.23yrs) due to incomplete >80% of one self-efficacy survey factor (response rate of 42.2%) and had incomplete head impact data. Independent Variables: preseason SE, IP, and YE playing organized tackle football. Main Outcome Measures: Self-efficacy was measured using a 53-question survey, previously validated for face and content validity, and based on Bandura’s General Self-Efficacy Scale. Questions measured level of confidence (0=no confidence, 100=highly confident) and categorized into five subscales. The accumulation of total head impacts (THI) was measured using Riddell InSite Speedflex helmets (Elyria, OH) throughout the season. An on-site research assistant recorded start/end times and participant attendance (i.e., exposure) for practices, scrimmages, and games. Head impact exposure (HIE) was standardized as a ratio of impacts per session. Multiple regression analysis tested the relationship between two dependent variables THI or HIE with seven predictor variables IP, five SE categories, and YE. Results: For THI 22.1% of the variation in the regression model was explained by the predictors (r=0.470 r2=0.221). IP had a negative correlation with THI (B=-4.480, P=0.019), while SE1 (B=3.133, P=0.010) and >8 years of experience (B=135.9, P=0.009) had a positive correlation with THI. For HIE 25.4% of the variation in the regression model was explained by the predictors (r=0.504 r2=0.254). IP had a negative correlation with number of total head impacts (B=-0.077, P=0.007), while SE1(B=3.133, P=0.010) and >8 years of experience (B=2.735, P=<0.001) had a positive correlation with HIE. Conclusions: THI and HIE are associated with players that have more than eight years of experience, have a lower number of days in IP and increased SE1 confidence scores. Players with more confidence and experience tend to engage in more risky behaviors when playing football. An early increase in the amount of time athletes spend practicing proper tackling and blocking techniques may further decrease the amount of head impacts received over time.