Injury Rates in Age-Only Versus Age-and-Weight Playing Standard Conditions in American Youth Football

BACKGROUND: American youth football leagues are typically structured using either age-only (AO) or age-and-weight (AW) playing standard conditions. These playing standard conditions group players by age in the former condition and by a combination of age and weight in the latter condition. However, no study has systematically compared injury risk between these 2 playing standards. PURPOSE: To compare injury rates between youth tackle football players in the AO and AW playing standard conditions. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: Athletic trainers evaluated and recorded injuries at each practice and game during the 2012 and 2013 football seasons. Players (age, 5-14 years) were drawn from 13 recreational leagues across 6 states. The sample included 4092 athlete-seasons (AW, 2065; AO, 2027) from 210 teams (AW, 106; O, 104). Injury rate ratios (RRs) with 95% CIs were used to compare the playing standard conditions. Multivariate Poisson regression was used to estimate RRs adjusted for residual effects of age and clustering by team and league. There were 4 endpoints of interest: (1) any injury, (2) non-time loss (NTL) injuries only, (3) time loss (TL) injuries only, and (4) concussions only. RESULTS: Over 2 seasons, the cohort accumulated 1475 injuries and 142,536 athlete-exposures (AEs). The most common injuries were contusions (34.4%), ligament sprains (16.3%), concussions (9.6%), and muscle strains (7.8%). The overall injury rate for both playing standard conditions combined was 10.3 per 1000 AEs (95% CI, 9.8-10.9). The TL injury, NTL injury, and concussion rates in both playing standard conditions combined were 3.1, 7.2, and 1.0 per 1000 AEs, respectively. In multivariate Poisson regression models controlling for age, team, and league, no differences were found between playing standard conditions in the overall injury rate (RRoverall, 1.1; 95% CI, 0.4-2.6). Rates for the other 3 endpoints were also similar (RRNTL, 1.1 [95% CI, 0.4-3.0]; RRTL, 0.9 [95% CI, 0.4-1.9]; RRconcussion, 0.6 [95% CI, 0.3-1.4]). CONCLUSION: For the injury endpoints examined in this study, the injury rates were similar in the AO and AW playing standards. Future research should examine other policies, rules, and behavioral factors that may affect injury risk within youth football

Injury Rates in Age-Only Versus Age-and-Weight Playing Standard Conditions in American Youth Football

Background: American youth football leagues are typically structured using either age-only (AO) or age-and-weight (AW) playing standard conditions. These playing standard conditions group players by age in the former condition and by a combination of age and weight in the latter condition. However, no study has systematically compared injury risk between these 2 playing standards. Purpose: To compare injury rates between youth tackle football players in the AO and AW playing standard conditions. Study Design: Cohort study; Level of evidence, 2. Methods: Athletic trainers evaluated and recorded injuries at each practice and game during the 2012 and 2013 football seasons. Players (age, 5-14 years) were drawn from 13 recreational leagues across 6 states. The sample included 4092 athlete-seasons (AW, 2065; AO, 2027) from 210 teams (AW, 106; O, 104). Injury rate ratios (RRs) with 95% CIs were used to compare the playing standard conditions. Multivariate Poisson regression was used to estimate RRs adjusted for residual effects of age and clustering by team and league. There were 4 endpoints of interest: (1) any injury, (2) non–time loss (NTL) injuries only, (3) time loss (TL) injuries only, and (4) concussions only. Results: Over 2 seasons, the cohort accumulated 1475 injuries and 142,536 athlete-exposures (AEs). The most common injuries were contusions (34.4%), ligament sprains (16.3%), concussions (9.6%), and muscle strains (7.8%). The overall injury rate for both playing standard conditions combined was 10.3 per 1000 AEs (95% CI, 9.8-10.9). The TL injury, NTL injury, and concussion rates in both playing standard conditions combined were 3.1, 7.2, and 1.0 per 1000 AEs, respectively. In multivariate Poisson regression models controlling for age, team, and league, no differences were found between playing standard conditions in the overall injury rate (RRoverall, 1.1; 95% CI, 0.4-2.6). Rates for the other 3 endpoints were also similar (RRNTL, 1.1 [95% CI, 0.4-3.0]; RRTL, 0.9 [95% CI, 0.4-1.9]; RRconcussion, 0.6 [95% CI, 0.3-1.4]). Conclusion: For the injury endpoints examined in this study, the injury rates were similar in the AO and AW playing standards. Future research should examine other policies, rules, and behavioral factors that may affect injury risk within youth football

Shannon and Renyi Entropies to Classify Effects of Mild Traumatic Brain Injury on Postural Sway

Background: Mild Traumatic Brain Injury (mTBI) has been identified as a major public and military health concern both in the United States and worldwide. Characterizing the effects of mTBI on postural sway could be an important tool for assessing recovery from the injury. Methodology/Principal Findings: We assess postural sway by motion of the center of pressure (COP). Methods for data reduction include calculation of area of COP and fractal analysis of COP motion time courses. We found that fractal scaling appears applicable to sway power above about 0.5 Hz, thus fractal characterization is only quantifying the secondary effects (a small fraction of total power) in the sway time series, and is not effective in quantifying long-term effects of mTBI on postural sway. We also found that the area of COP sensitively depends on the length of data series over which the COP is obtained. These weaknesses motivated us to use instead Shannon and Renyi entropies to assess postural instability following mTBI. These entropy measures have a number of appealing properties, including capacity for determination of the optimal length of the time series for analysis and a new interpretation of the area of COP. Conclusions: Entropy analysis can readily detect postural instability in athletes at least 10 days post-concussion so that it appears promising as a sensitive measure of effects of mTBI on postural sway