The purpose of this thesis was to develop a means of predicting future injury among nursing personnel working in a hospital system. Nursing has one of the highest incidence rates of musculoskeletal injuries among U.S. occupations. Endemic to the job are tasks such as rolling, sitting, standing, and transferring large, and often times, uncooperative patients. These tasks often place large biomechanical stresses on the musculoskeletal system and, in some cases, contribute to or cause a musculoskeletal injury. Given the current nursing shortage, it is imperative to keep nurses injury-free and productive so they can provide patient care services. Even though a large number of nursing personnel are injured every year and most are exposed to these high levels of biomechanical stress, the majority of nurses are injury-free. The question then arises "Why do some nurses have injuries while others do not?" The purpose of this thesis was to determine whether individual attributes in a population of nurses were associated with risk of future injury. The subject population was comprised of 140 nursing personnel at a local hospital system hired between April 1995 and February 1999. Data on individual attributes, such as patient demographics, previous injuries, posture, joint range of motion, flexibility, and muscular strength, was ascertained during a post-offer screening on these personnel. Twenty six (19%) nurses experienced an injury associated with the axial skeleton. Chi square test for homogeneity for the categorical predictor variables, and the Student's T-test for continuous predictor variables were used to determine if any individual attributes were associated with future injuries. None of the variables were associated with a risk of future axial skeletal injury. Practical application of these results for St. Joseph Regional Health Center, and possibly other acute care facilities, directs us to stop costly pre-employment/post-offer testing for the purpose of identifying injury prone nurse applicants. Secondly, it allows the focus of limited resources to be on making the job safer through administrative and engineering controls.

The purpose of this thesis was to develop a means of predicting future injury among nursing personnel working in a hospital system. Nursing has one of the highest incidence rates of musculoskeletal injuries among U.S. occupations. Endemic to the job are tasks such as rolling, sitting, standing, and transferring large, and often times, uncooperative patients. These tasks often place large biomechanical stresses on the musculoskeletal system and, in some cases, contribute to or cause a musculoskeletal injury. Given the current nursing shortage, it is imperative to keep nurses injury-free and productive so they can provide patient care services. Even though a large number of nursing personnel are injured every year and most are exposed to these high levels of biomechanical stress, the majority of nurses are injury-free. The question then arises "Why do some nurses have injuries while others do not?" The purpose of this thesis was to determine whether individual attributes in a population of nurses were associated with risk of future injury. The subject population was comprised of 140 nursing personnel at a local hospital system hired between April 1995 and February 1999. Data on individual attributes, such as patient demographics, previous injuries, posture, joint range of motion, flexibility, and muscular strength, was ascertained during a post-offer screening on these personnel. Twenty six (19%) nurses experienced an injury associated with the axial skeleton. Chi square test for homogeneity for the categorical predictor variables, and the Student's T-test for continuous predictor variables were used to determine if any individual attributes were associated with future injuries. None of the variables were associated with a risk of future axial skeletal injury. Practical application of these results for St. Joseph Regional Health Center, and possibly other acute care facilities, directs us to stop costly pre-employment/post-offer testing for the purpose of identifying injury prone nurse applicants. Secondly, it allows the focus of limited resources to be on making the job safer through administrative and engineering controls.

Injuries to the head and neck are potentially the most severe injuries in humans, since they may damage the nervous system. In accidents, the cervical musculature stabilizes the neck in order to prevent injury to the spinal column and is also a potential site for acute muscle strain, resulting in neck pain. The musculature is consequently an important factor in the understanding of neck injuries. There is however a lack of data on muscle response and little is known about the dynamics of the individual muscles. In this thesis the numerical method of Finite Elements (FE) is used to examine the importance of musculature in accidental injuries. In order to study the influence of a continuum musculature, a 3D solid element muscle model with continuum mechanical material properties was developed. It was hypothesized that a 3D musculature model would improve the biofidelity of a numerical neck model by accounting for the passive compressive stiffness, mass inertia, and contact interfaces between muscles. A solid element representation would also enable the study of muscle tissue strain injuries. A solid element muscle model representing a 50th percentile male was created, based on the geometry from MRI, and incorporated into an existing FE model of the spine. The passive material response was modeled with nonlinear-elastic and viscoelastic properties derived from experimental tensile tests. The active forces were modeled with discrete Hill elements. In the first version of the model the passive solid element muscles were used together with separate active spring elements. In the second version the active elements were integrated in the solid mesh with coincident nodes. This combined element, called the Super-positioned Muscle Finite Element (SMFE), was evaluated for a single muscle model before it was incorporated in the more complex neck muscle model. The main limitation of the SMFE was that the serial connected Hill-type elements are unstable due to their individual force-length relationship. The instabilities in the SMFE were minimized by the addition of passive compressive stiffness from the solid element and by the decreased gradient of the force-length relation curve.  The solid element musculature stabilized the vertebral column and reduced the predicted ligament strains during simulated impacts. The solid element compressive stiffness added to the passive stiffness of the cervical model. This decreased the need for additional active forces to reproduce the kinematic response of volunteers during impact. The active response of the SMFE improved model biofidelity and reduced buckling of muscles in compression. The solid element model predicted forces, strains, and energies for individual muscles and showed that the muscle response is dependent on impact direction and severity. For each impact direction, the model identified a few muscles as main load carriers that corresponded to muscles generating high EMG signals in volunteers. The single largest contributing factor to neck injury prediction was the muscle active forces. Muscle activation reduced the risk of injury in ligaments in high-energy impacts. The most urgent improvements of the solid element muscle model concerns: the stability of the SMFE; the boundary conditions from surrounding tissues; and more detailed representations of the myotendinous junctions. The model should also be more extensively validated for the kinematical response and for the muscle load predictions. It was concluded that a solid muscle model with continuum mechanical material properties improves the kinematical response and injury prediction of a FE neck model compared to a spring muscle model. The solid muscle model can predict muscle loads and provide insight to how muscle dynamics affect spinal stability as well as muscle acute strain injuries. / QC 20100809

Purpose: To investigate if the Functional Movement Screen (FMSâ„¢) total score, individual component test scores or number of asymmetries can predict noncontact injury risk or player performance over three consecutive seasons of NCAA Division I football. Methods: As football teams are comprised of individuals with vastly different physical characteristics and playing responsibilities, we divided the subjects into three homogeneous groups based on position (Big, Combo and Skill). Each FMSâ„¢ score was assessed with regard to the total team score as well as by individual position groups. For our injury analysis we also controlled for exposure. For player performance we controlled for plays played.Participants: 286 NCAA Division I athletes participated over three consecutive seasons, yielding a total of 344 observations.Results: We found no significant relationship between total FMSâ„¢ score and likelihood of injury when analyzed by the total team or by position group. These findings were the same for all groups, for both the total number of injuries as well as injuries weighted by injury exposure. The only significant findings occurred when we considered individual Test Item scores to injury by position group. We only found a significant relationship in the expected direction with Push-Up Stability in the Combo group. Regarding performance, total FMSâ„¢ was only significant for the Big group, but this effect was not practically significant.Conclusion: FMSâ„¢ was not a good predictor of noncontact injury or performance based on possible playing time.

Background: Football (Soccer) is arguably the most popular sport in the international sporting arena. A survey conducted by FIFA (Fédération International de Football Association) (FCPA, 2000) indicated that there are 240 million people who regularly play soccer around the world. Internationally, there are 300 000 clubs with approximately 1.5 million teams. In South Africa, there were 1.8 million registered soccer players in 2002/2003 (Alegi, 2004). Although youth players are predominantly amateurs and have no financial value for their clubs or schools, their continued health and safety are still of vital importance. There are some clubs which contract development players at 19 years of age in preparation for playing in their senior sides and these young players should be well looked after, to ensure a long career playing soccer. Being able to predict injuries and prevent them would be of great value to the soccer playing community. Aims: The main aim of this research was to create a statistical predictive equation combining biomechanics, balance and proprioception, plyometric strength ratios of ND/Bil (Non dominant leg plyometrics/ Bilateral plyometrics), D/Bil (Dominant leg plyometrics/ Bilateral plyometrics) and ND+D/Bil (Non dominant leg + dominant leg plyometrics/ Bilateral plyometrics) and previous injuries to determine a youth soccer player's risk of the occurrence of lower extremity injuries. In the process of reaching this aim it was necessary to record an epidemiological profile of youth soccer injuries over a two season period. It was also necessary to record a physical profile of, and draw comparisons between, school and club youth soccer players. Following the creation of the prediction model a preventative training programme was created for youth soccer players, addressing physical shortcomings identified with the model. Design: A prospective cohort study Subjects: Schoolboy players from two schools in the North West Province, as well as club players from three age groups were used for this study. Players from the U/16 and U/18 teams in the two schools were tested prior to the 2007 season. Players from the U/17, U/18 and U/19 club development teams were tested prior to the 2008 season. The combined total number of players in the teams amounted to 110 players. Method: The test battery consisted of a biomechanical evaluation, proprioceptive and plyometric testing and an injury history questionnaire. The Biomechanical evaluation was done according to the protocol compiled by Hattingh (2003). This evaluation was divided into five regions with a dysfunction score being given for each region. A single limb stance test was used to test proprioception. A Sergeant jump test was utilised using the wall mark method to test plyometric jumping height. A previous injury questionnaire was also completed on all players prior to testing. Test subjects from the schools were tested with the test battery prior to commencement of the 2007 season. The testing on the club teams was undertaken prior to the 2008 season. Injuries were recorded on the prescribed injury recording form by qualified Physiotherapists at weekly sports injury clinics at each of the involved schools and clubs. The coaching staff monitored exposure to training activities and match play on the prescribed recording forms. These training and match exposure hours were used, along with the recorded injuries for creating an epidemiological profile. Injuries were expressed as the amount of injuries per 1000 play hours. Logistical regression was done by using the test battery variables as independent variables and the variable injured/not injured as dependent variable (Statsoft, 2003). This analysis created prediction functions, determining which variables predict group membership of injured and non injured players. Results: There were 110 youth players involved in the research study from seven teams and four different age groups. There were two groups of U/16 players, an U/17 group, three U/18 groups and an U/19 group. The players were involved in a total of 7974 hours of exposure to training and match play during the seasons they were monitored. The average age of the players was 16.6 years. The majority of players were right limb dominant (83.6%) and 65.7% of players failed a single limb stance test. The mean jump height for both legs combined was 33.77cm, with mean heights of 22.60cm for dominant leg jump and 22.66cm for the non dominant leg. In the biomechanical evaluation of the lower leg and foot area, the average youth player presented with adaptation of toes, normal or flat medial foot arches, a normal or pronated rear foot in standing and lying and a normal or hypomobile mid-foot joint. Between 42.7% and 51.8% of players also presenting with decreased Achilles tendon suppleness and callusing of the transverse foot arch. The youth profile for the knee area indicated that the players presented with excessive tightness of the quadriceps muscles, normal patella tilt and squint, normal knee height, a normal Q-angle, a normal VMO: VL ratio and no previous injuries. This profile indicated very little dysfunction amongst youth players for the knee area. For the hip area, the youth profile was described as follows: There was shortening of hip external rotators, decreased Gluteal muscles length, normal hip internal rotation and no previous history of injury. Between 38.2% and 62.7% of players also exhibit shortened muscle length of the adductor and Iliopsoas muscles and decreased length of the ITB (Iliotibial Band). In the Lumbo-pelvic area there was an excessive anterior tilt of the pelvis with normal lumbar extension, side flexion, rotation and lumbar saggital view without presence of scoliosis. Between 58.18% and 65.45% of players presented with an abnormal coronal view and decreased lumbar flexion. Between 41.81% and 44.54% of players also presented with leg length, ASIS, PSIS, Cleft, Rami and sacral rhythm asymmetry. The similarity of the results for these tests in all players contributed to a new variable called 'SIJ dysfunction'. This was compiled from the average of the scores for Leg length, ASIS, PSIS, Cleft, Rami and Sacral rhythm, which was also considered for inclusion in the prediction model. The neurodynamic results of youth players indicated that approximately between 44.54% and 50.91% of players presented with decreased Straight leg raise and prone knee bend tests. The total combined dysfunction scores for the left and right sides were 17.091 and 17.909 respectively, indicating that there were higher levels of dysfunction on the right side than the left. This increased unilateral dysfunction could probably be attributed to limb dominance and increased use of the one leg for kicking and passing during the game. In the epidemiological study on youth players, there were a total of 49 training injuries and 52 match injuries. The total injury rate for youth players was 12.27 injuries/1000 hours, with a total match injury rate of 37.12 injuries/1000 match hours. The combined training injury rate was 7.17 injuries/1000 training hours. 87.13% of injuries were of the lower limb area and the individual areas with the highest percentage of injuries were the Ankle (25.74%), Knee (19.80%), Thigh (15.84%) and Lower leg (14.85%).The totals for youth players indicated that sprains (30.69% of total), strains (27.72% of total) and contusions (27.72% of total) were the most common causative mechanism of injuries. The severity of injuries show 'zero day' (no time off play) injuries to be the most common type (35.64%), followed by 'slight' (1 to 3 days off play) (33.66%) and 'minor' (4 to 7 days off play) (14.85%). School players had higher injury rates than club players but the severity of injuries to club players was higher, with longer absences from play. Non-contact injuries accounted for 52.47% of the total with 46.53% being contact injuries. School players had lower levels of non-contact injuries than club players, which correlated well with lower dysfunction scores recorded for school players during the biomechanical evaluations. This demonstrated that there was a definite relationship between levels of biomechanical dysfunction and the percentage of non-contact injuries in youth players, which formed the premise of the creation of a prediction model for non-contact youth soccer injuries. The next step in the creation of a prediction model was to identify the variables that discriminated maximally between injured and non-injured players. This was done using stepwise logistic regression analysis. After the analysis, ten variables with the largest odds ratios were selected for inclusion in the prediction model to predict non-contact injuries in youth soccer players. The prediction model created from the stepwise analysis presented as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.9460i-0.5193j) l + exp(-8.2483-1.2993a + 1.8418b+ 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.94601-0.5193J) a = Toe dysfunction b = Previous ankle injury c = Ankle dysfunction d = SIJ dysfunction e = Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = e x , with e the constant 2.7183 In the ankle area, the toe positional test, previous ankle injury history and combined ankle dysfunction score were included in the prediction model. In the knee area, the patella squint test was included in the model. In the hip area, the Psoas component of the Thomas test was included, along with the Gluteal muscle length test. In the Lumbo-pelvic area, the SIJ dysfunction (average of Leg length, ASIS, PSIS, Rami, Cleft and Sacral rhythm tests), lumbar extension test and lumbar dysfunction scores were included in the prediction model. In the neurodynamic area, the Straight leg raise test was included in the prediction model. The prediction model therefore contained tests from all five the bio mechanical areas of the body. Overall, this model correctly predicted 86.91% of players as either injured or not-injured. The I value (effect size index for improvement over chance) of the prediction model (1=0.67), along with the sensitivity (65.52%), specificity (94.87%), overall correct percentage of prediction (86.91%) and Hosmer and Lemeshow interferential goodness-to-fit value (X 2(8) = 0.7204), all demonstrated this prediction model to be a valid and accurate prediction tool for non-contact youth soccer injuries A second prediction model, for the prediction of hip and groin injuries amongst youth players, was also created. The prediction model created from the stepwise analysis for groin injuries presents as follows: P (Groin injury)^ exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d+14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyometric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 The prediction model for hip and groin injuries included the variables of SIJ dysfunction, previous knee injury, previous hip injury, lumbar extension, straight leg raise, limb dominance and the ratio of non-dominant leg to bilateral legs plyometric height. When all the validifying tests were examined, the I-value (0.64868), sensitivity (66.67%), specificity (98.01%), false negatives (1.98%), false positives (33.33%), Hosmer and Lemeshow goodness-to-fit value (X2(8) = 0.77) and the overall percentage of correct prediction (96.26%) all reflected that this model was an accurate prediction tool for hip and groin injuries amongst youth soccer players. Conclusion: This study showed that it was possible to create a prediction model for non-contact youth soccer injuries based on a pre-season biomechanical, plyometric and proprioceptive evaluation along with a previous injury history questionnaire. This model appears as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f - 1.1566g + 1.8273h - 0.9460i - 0.5193J) l + exp(-8.2483-1.2993a+ 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g+1.8273h-0.94601-0.5193J) a = Toe dysfunction b=Previous ankle injury c = Ankle dysfunction d= SIJ dysfunction e=Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 Using the hip and groin prediction model, combined with the injury prediction model, injuries in youth soccer players can be predicted. The data for each player should first be substituted into the injury prediction model, to determine the chance of getting injured during the season. The data should then be substituted into the hip and groin injury prediction model, determining the chance of hip and groin injuries during the season. The results from the groin injury prediction model could then be used to exclude groin injuries amongst players. A negative result for the hip and groin injury, which showed a false negative percentage of 1.98%, could be used to determine that an injury that was predicted using the overall injury prediction model, would not be a hip and groin injury. A positive result in the groin injury test could, however, not exclude injuries to other body areas that were predicted by the overall injury prediction model, so the groin injury prediction model could only be used to exclude hip and groin injuries. / Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2009.

Background: Football (Soccer) is arguably the most popular sport in the international sporting arena. A survey conducted by FIFA (Fédération International de Football Association) (FCPA, 2000) indicated that there are 240 million people who regularly play soccer around the world. Internationally, there are 300 000 clubs with approximately 1.5 million teams. In South Africa, there were 1.8 million registered soccer players in 2002/2003 (Alegi, 2004). Although youth players are predominantly amateurs and have no financial value for their clubs or schools, their continued health and safety are still of vital importance. There are some clubs which contract development players at 19 years of age in preparation for playing in their senior sides and these young players should be well looked after, to ensure a long career playing soccer. Being able to predict injuries and prevent them would be of great value to the soccer playing community. Aims: The main aim of this research was to create a statistical predictive equation combining biomechanics, balance and proprioception, plyometric strength ratios of ND/Bil (Non dominant leg plyometrics/ Bilateral plyometrics), D/Bil (Dominant leg plyometrics/ Bilateral plyometrics) and ND+D/Bil (Non dominant leg + dominant leg plyometrics/ Bilateral plyometrics) and previous injuries to determine a youth soccer player's risk of the occurrence of lower extremity injuries. In the process of reaching this aim it was necessary to record an epidemiological profile of youth soccer injuries over a two season period. It was also necessary to record a physical profile of, and draw comparisons between, school and club youth soccer players. Following the creation of the prediction model a preventative training programme was created for youth soccer players, addressing physical shortcomings identified with the model. Design: A prospective cohort study Subjects: Schoolboy players from two schools in the North West Province, as well as club players from three age groups were used for this study. Players from the U/16 and U/18 teams in the two schools were tested prior to the 2007 season. Players from the U/17, U/18 and U/19 club development teams were tested prior to the 2008 season. The combined total number of players in the teams amounted to 110 players. Method: The test battery consisted of a biomechanical evaluation, proprioceptive and plyometric testing and an injury history questionnaire. The Biomechanical evaluation was done according to the protocol compiled by Hattingh (2003). This evaluation was divided into five regions with a dysfunction score being given for each region. A single limb stance test was used to test proprioception. A Sergeant jump test was utilised using the wall mark method to test plyometric jumping height. A previous injury questionnaire was also completed on all players prior to testing. Test subjects from the schools were tested with the test battery prior to commencement of the 2007 season. The testing on the club teams was undertaken prior to the 2008 season. Injuries were recorded on the prescribed injury recording form by qualified Physiotherapists at weekly sports injury clinics at each of the involved schools and clubs. The coaching staff monitored exposure to training activities and match play on the prescribed recording forms. These training and match exposure hours were used, along with the recorded injuries for creating an epidemiological profile. Injuries were expressed as the amount of injuries per 1000 play hours. Logistical regression was done by using the test battery variables as independent variables and the variable injured/not injured as dependent variable (Statsoft, 2003). This analysis created prediction functions, determining which variables predict group membership of injured and non injured players. Results: There were 110 youth players involved in the research study from seven teams and four different age groups. There were two groups of U/16 players, an U/17 group, three U/18 groups and an U/19 group. The players were involved in a total of 7974 hours of exposure to training and match play during the seasons they were monitored. The average age of the players was 16.6 years. The majority of players were right limb dominant (83.6%) and 65.7% of players failed a single limb stance test. The mean jump height for both legs combined was 33.77cm, with mean heights of 22.60cm for dominant leg jump and 22.66cm for the non dominant leg. In the biomechanical evaluation of the lower leg and foot area, the average youth player presented with adaptation of toes, normal or flat medial foot arches, a normal or pronated rear foot in standing and lying and a normal or hypomobile mid-foot joint. Between 42.7% and 51.8% of players also presenting with decreased Achilles tendon suppleness and callusing of the transverse foot arch. The youth profile for the knee area indicated that the players presented with excessive tightness of the quadriceps muscles, normal patella tilt and squint, normal knee height, a normal Q-angle, a normal VMO: VL ratio and no previous injuries. This profile indicated very little dysfunction amongst youth players for the knee area. For the hip area, the youth profile was described as follows: There was shortening of hip external rotators, decreased Gluteal muscles length, normal hip internal rotation and no previous history of injury. Between 38.2% and 62.7% of players also exhibit shortened muscle length of the adductor and Iliopsoas muscles and decreased length of the ITB (Iliotibial Band). In the Lumbo-pelvic area there was an excessive anterior tilt of the pelvis with normal lumbar extension, side flexion, rotation and lumbar saggital view without presence of scoliosis. Between 58.18% and 65.45% of players presented with an abnormal coronal view and decreased lumbar flexion. Between 41.81% and 44.54% of players also presented with leg length, ASIS, PSIS, Cleft, Rami and sacral rhythm asymmetry. The similarity of the results for these tests in all players contributed to a new variable called 'SIJ dysfunction'. This was compiled from the average of the scores for Leg length, ASIS, PSIS, Cleft, Rami and Sacral rhythm, which was also considered for inclusion in the prediction model. The neurodynamic results of youth players indicated that approximately between 44.54% and 50.91% of players presented with decreased Straight leg raise and prone knee bend tests. The total combined dysfunction scores for the left and right sides were 17.091 and 17.909 respectively, indicating that there were higher levels of dysfunction on the right side than the left. This increased unilateral dysfunction could probably be attributed to limb dominance and increased use of the one leg for kicking and passing during the game. In the epidemiological study on youth players, there were a total of 49 training injuries and 52 match injuries. The total injury rate for youth players was 12.27 injuries/1000 hours, with a total match injury rate of 37.12 injuries/1000 match hours. The combined training injury rate was 7.17 injuries/1000 training hours. 87.13% of injuries were of the lower limb area and the individual areas with the highest percentage of injuries were the Ankle (25.74%), Knee (19.80%), Thigh (15.84%) and Lower leg (14.85%).The totals for youth players indicated that sprains (30.69% of total), strains (27.72% of total) and contusions (27.72% of total) were the most common causative mechanism of injuries. The severity of injuries show 'zero day' (no time off play) injuries to be the most common type (35.64%), followed by 'slight' (1 to 3 days off play) (33.66%) and 'minor' (4 to 7 days off play) (14.85%). School players had higher injury rates than club players but the severity of injuries to club players was higher, with longer absences from play. Non-contact injuries accounted for 52.47% of the total with 46.53% being contact injuries. School players had lower levels of non-contact injuries than club players, which correlated well with lower dysfunction scores recorded for school players during the biomechanical evaluations. This demonstrated that there was a definite relationship between levels of biomechanical dysfunction and the percentage of non-contact injuries in youth players, which formed the premise of the creation of a prediction model for non-contact youth soccer injuries. The next step in the creation of a prediction model was to identify the variables that discriminated maximally between injured and non-injured players. This was done using stepwise logistic regression analysis. After the analysis, ten variables with the largest odds ratios were selected for inclusion in the prediction model to predict non-contact injuries in youth soccer players. The prediction model created from the stepwise analysis presented as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.9460i-0.5193j) l + exp(-8.2483-1.2993a + 1.8418b+ 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g + 1.8273h-0.94601-0.5193J) a = Toe dysfunction b = Previous ankle injury c = Ankle dysfunction d = SIJ dysfunction e = Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = e x , with e the constant 2.7183 In the ankle area, the toe positional test, previous ankle injury history and combined ankle dysfunction score were included in the prediction model. In the knee area, the patella squint test was included in the model. In the hip area, the Psoas component of the Thomas test was included, along with the Gluteal muscle length test. In the Lumbo-pelvic area, the SIJ dysfunction (average of Leg length, ASIS, PSIS, Rami, Cleft and Sacral rhythm tests), lumbar extension test and lumbar dysfunction scores were included in the prediction model. In the neurodynamic area, the Straight leg raise test was included in the prediction model. The prediction model therefore contained tests from all five the bio mechanical areas of the body. Overall, this model correctly predicted 86.91% of players as either injured or not-injured. The I value (effect size index for improvement over chance) of the prediction model (1=0.67), along with the sensitivity (65.52%), specificity (94.87%), overall correct percentage of prediction (86.91%) and Hosmer and Lemeshow interferential goodness-to-fit value (X 2(8) = 0.7204), all demonstrated this prediction model to be a valid and accurate prediction tool for non-contact youth soccer injuries A second prediction model, for the prediction of hip and groin injuries amongst youth players, was also created. The prediction model created from the stepwise analysis for groin injuries presents as follows: P (Groin injury)^ exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d+14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyometric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 The prediction model for hip and groin injuries included the variables of SIJ dysfunction, previous knee injury, previous hip injury, lumbar extension, straight leg raise, limb dominance and the ratio of non-dominant leg to bilateral legs plyometric height. When all the validifying tests were examined, the I-value (0.64868), sensitivity (66.67%), specificity (98.01%), false negatives (1.98%), false positives (33.33%), Hosmer and Lemeshow goodness-to-fit value (X2(8) = 0.77) and the overall percentage of correct prediction (96.26%) all reflected that this model was an accurate prediction tool for hip and groin injuries amongst youth soccer players. Conclusion: This study showed that it was possible to create a prediction model for non-contact youth soccer injuries based on a pre-season biomechanical, plyometric and proprioceptive evaluation along with a previous injury history questionnaire. This model appears as follows: P (injury)= exp(-8.2483 -1.2993a + 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f - 1.1566g + 1.8273h - 0.9460i - 0.5193J) l + exp(-8.2483-1.2993a+ 1.8418b + 0.2485c + 4.2850d + 1.3845e + 1.3004f-1.1566g+1.8273h-0.94601-0.5193J) a = Toe dysfunction b=Previous ankle injury c = Ankle dysfunction d= SIJ dysfunction e=Lumbar Extension f = Straight Leg Raise g = Psoas length h = Patella squint i = Gluteal muscle length j = Lumbar dysfunction P = probability of non contact injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 It was also possible to create a prediction model for non contact hip and groin injuries, which appears as follows: P (Groin injury)= exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) l + exp(-116.2 + 33.5383d + 14.5108k + 4.1972m + 1.9330e + 10.7006f-14.4028n + 48.8751p) d = SIJ dysfunction k = Previous knee injury m = Previous hip injury e = Lumbar extension f = Straight leg raise n = Limb dominance p = ND/Bil plyo metric ratio P = probability of groin injury exp(x) = ex, with e the constant 2.7183 Using the hip and groin prediction model, combined with the injury prediction model, injuries in youth soccer players can be predicted. The data for each player should first be substituted into the injury prediction model, to determine the chance of getting injured during the season. The data should then be substituted into the hip and groin injury prediction model, determining the chance of hip and groin injuries during the season. The results from the groin injury prediction model could then be used to exclude groin injuries amongst players. A negative result for the hip and groin injury, which showed a false negative percentage of 1.98%, could be used to determine that an injury that was predicted using the overall injury prediction model, would not be a hip and groin injury. A positive result in the groin injury test could, however, not exclude injuries to other body areas that were predicted by the overall injury prediction model, so the groin injury prediction model could only be used to exclude hip and groin injuries. / Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2009.

No description available.

A traumatic brain injury is usually caused by a direct impact to the head and is a common cause of disability and death all around the world. The most effective method to predict brain injury today, is to use a finite element head model. In this investigation, the three injury predictors strain, strain rate, and the product of strain and strain rate were investigated using a rat brain finite element model. The main goal was to find which injury predictor most effectively would predict injury. To find the injury predictor with the highest area under curve value, comparisons between experimental results obtained from simulations and results from previously performed experiments on rats were made. To better understand how different factors can affect the severity of symptoms from a traumatic brain injury, a parametric study with a focus on rotational direction and rotational duration was conducted. Simulations were run on a rat brain finite element model for three rotational directions and three rotational durations.  The statistical analysis was completed for six experiments and nine brain regions. The three injury predictors were extracted from 26 simulations completed on a rat brain finite element model, and the maximum values of the 95th percentile for each brain region were extracted. The results showed that the product of the strain and the strain rate was the most effective injury predictor for four out of six experiments (unconscious time, EPM arm change, EPM open duration, and MWM session 3). The parametric study investigated rotation in the axial, coronal, and sagittal plane against the three rotational durations 1.5 ms, 3 ms, and 6 ms. The parametric study revealed that both the direction and duration of rotation importantly influence the extent of damage in traumatic brain injuries. The results showed that rotation in the axial plane and a 3 ms duration caused the most brain damage. It was also concluded that the results need to undergo additional verification to further define the relationships between the rotational direction, the rotational duration, and the injury predictors. / En traumatisk hjärnskada orsakas vanligtvis av våld mot huvudet och är en vanlig orsak till både funktionsnedsättningar och dödsfall världen över. Den effektivastemetoden för att kunna förutsäga en hjärnskada idag är att använda en finit elementmetodmodell av en hjärna.  I denna undersökning har de tre skadeprediktorerna belastning, belastningshastighet och produkten av belastningen och belastningshastigheten undersöktes med hjälp av simuleringar genomförda på en modell av en råtthjärna, byggd med hjälp av finita elementmetoden. Målet var att ta reda på vilken skadeprediktor som mest effektivt kunde förutsäga hjärnskada. För att hitta skadeprediktorn med högst area under curve-värde gjordes jämförelser mellan experimentella resultat från simuleringar mot resultat från tidigare utförda experiment på råttor. För att få en djupare förståelse för vilka parametrar som kan påverka graden av symptom från en traumatisk hjärnskada genomfördes en parametrisk studie med fokus på rotationsriktning och rotationstid. Nya simuleringar genomfördes på en finit elementmodell av en råtthjärna i tre rotationsriktningar och under tre rotationstider.  Den statistiska analysen utfördes på sex experiment och för nio regioner i hjärnan. Belastningen, belastningshastigheten samt produkten av belastningen och belastningshastigheten extraherades från 26 simulerade finita element råtthjärnor och maximumvärdet från den 95.e percentilen sparades. Resultatet av den statistiska analysen visade att produkten av belastningen och belastningshastigheten var den skadeprediktorn med bäst skadeförutsägelse för fyra av sex experiment(medvetslös tid, EPM arm förflyttning, EPM varaktighet i öppet utrymme och MWM session 3). Under den parametriska studien undersöktes axial, koronal och sagittal rotationsriktning mot de tre rotationstiderna 1.5 ms, 3 ms och 6 ms. Resultatet av den parametriska studien visade att både rotationsriktning och rotationstid spelar viktiga roller när det kommer till omfattningen av symptom som kan uppstå vid en traumatisk hjärnskada. För de undersökta delarna av hjärnan var den rotationsriktning som orsakade störst skada rotation i det axiala planet och den rotationstid som orsakade mest skada var vid 3 ms. Slutsatsen att resultatet bör genomgå ytterligare verifiering drogs. Detta för att ytterligare definiera sambanden mellan rotationsriktning, rotationstid och skadeprediktorerna.

Traumatic brain injury (TBI) is a worldwide public health problem. It is often caused by impacts to the head, which can cause translational and rotational motions. During impacts to the head, the neck serves as one of the boundary conditions for determining its kinematic response. In today’s helmet assessment standards, the dynamics of the neck are not included, and in most standards only translational accelerations are examined within a short time interval around 20- 30 ms. However, to understand the risk of brain injury, it is also important to account for the rotational motions and the influence of the neck on head kinematics.  In this thesis the influence of the neck on head kinematics was investigated by comparing 5 different finite element (FE) models of the human. By using finite element analysis, simulations of four different accident scenarios were conducted. Most models are produced for the automotive industry and are not validated in vertical impacts with forces acting on the head. The accident scenarios included vertical and horizontal impacts to the head with different striking objects. The models included two anthropomorphic test devices (ATD) and three human body models (HBM). Furthermore, an isolated head was also used. The models were equipped with an industrial safety helmet, with and without a low friction layer (LFL). Additionally, the helmet versions were used to investigate how the various FE models predict the difference in rotational kinematics.  The head kinematics showed considerable disparities between the ATDs and the HBMs. The ATDs mostly showed a stiffer, spring-like behavior with higher translational accelerations and lesser rotational motions. Furthermore, the HBMs showed responses that were assumed to have been in better proximity to biofidelic responses. The incorporation of the LFL led to a reduction in peak resultant rotational velocity (PRV) in most models and accident scenarios. Furthermore, the results were highly influenced by the choice of duration. It was seen that the differences between the models increased over time, as the boundary effects could influence the kinematics to a larger extent. Hence, the neck had more influence on head kinematics at longer time durations.  This thesis contributes to a comparison of different FE models and how various boundary conditions affect the kinematics of the head. The Hybrid III should only be used in cases involving pure flexion-extension. The attachment of the KTH neck to the Hybrid III torso led to large differences in kinematic responses to the other models, and therefore it should not be used in virtual testing. Due to the resemblance between head-only and the HBMs, as well as the short duration in bicycle helmet assessment, the use of only a headform is probably a better approximation as the ATD necks that could be used are not good representations of biofidelity. Before implementing surrogate necks in helmet assessment, more investigations on the influence of the neck on head kinematics are necessary as well as the development of neck models with high biofidelity. / Traumatiska hjärnskador är ett globalt folkhälsoproblem. Traumatiska hjärnskador orsakas ofta av slag mot huvudet, vilket kan orsaka både translations- och rotationsrörelser av hjärnan. Vid slag mot huvudet verkar halsen som ett av randvillkoren som styr kinematiken av huvudet. I dagens hjälmstandarder är halsens dynamik inte inkluderad och i majoriteten av standarder är det endast translationsaccelerationer som undersöks samt inom en kort tidsram, runt 20-30 ms. För att förstå risken för hjärnskador behöver man även beakta rotationsrörelser och då blir halsens inflytande på huvudets kinematik av vikt, liksom att utvärdera kinematiken under en längre tid.  I detta examensarbete studeras halsens inverkan på huvudets kinematik genom att jämföra fem olika finita element (FE) modeller av människan. Genom att använda finita elementmetoden, genomfördes simuleringar av 4 olika olycksscenarior. Olycksscenariorna inkluderade vertikala och horisontella islag med olika objekt. De modeller som användes var två krockdockor och tre humanmodeller samt ett isolerat huvud. De flesta modeller är framtagna för bilindustrin vilket påverkar dess användningsområde genom begränsade valideringar av vertikala slag med krafter som verkar direkt på huvudet. Alla modeller var utrustade med en industrihjälmsmodell, med respektive utan ett lågfriktionslager. Dessutom användes hjälmmodellerna till att undersöka hur de olika FE modellerna förutspådde skillnader i rotationskinematik.  Kinematiken av huvudet visade på signifikanta skillnader mellan krockdockorna och humanmodellerna. Krockdockorna hade generellt ett stelare, fjäderliknande beteende med högre translationsacceleration och mindre rotationsrörelse. Vidare hade humanmodellerna ett beteende som var mer likt den förväntade mänskliga responsen. Användandet av lågfriktionslagret ledde till reduktion i resulterande peak rotationshastighet bland de flesta modeller och olycksscenarior. Resultatet påverkades nämnvärt av valet av tidsintervall. Vid längre tidsintervall var skillnaderna i beteende större mellan modellerna. Därför hade halsen större inverkan på huvudets kinematik vid längre durationer.  Detta examensarbete bidrar till en jämförelse av olika FE modeller och förståelse för hur olika randvillkor påverkar huvudets kinematik. Hybrid III borde endast användas för horisontella islag med enbart flexion-extensions rörelser. Infästningen av KTH halsen till Hybrid III gav stora skillnader i kinematiken jämfört med de andra modellerna, och därför ska den inte användas vid virtuella tester. På grund av de likheter som sågs mellan enbart huvud och humanmodellerna samt på grund av de korta islagen vid cykelhjälmsbedömningar, är troligtvis användandet av ett isolerat huvud en bättre approximation än användandet av de tillgängliga krockdockornas halsar. Innan man använder halsmodeller vid hjälmbedömningar, krävs fler studier på halsens inverkan på kinematiken samt framtagande av halsmodeller med mer människoliknande respons.