Academic literature on the topic 'Australian youth soccer players'

This thesis examined the physical development and match performance of elite youth academy soccer players some of whom were likely to progress to become professional soccer players. Physical characteristics such as standing height, body mass and estimated body fat composition, physical performance and match performance were explored. Furthermore, the relationships between physical performance and match running performance were examined in players from the U9 to U18 age group squads. Finally, the influence of biological maturity on physical characteristics, physical performance and match running performance in these elite youth soccer players was investigated and recommendations are made concerning talent identification and player development. One hundred and eighty-three elite soccer players (chronological age: 8.9 to 18.7 years; age grouping U9-U18) from an English Premier League Academy in the East Midlands were assessed for standing height, body mass, skinfolds, 30 m sprint, slalom and 505 agility, squat jump, counter movement jump with and without arms, Yo-Yo intermittent recovery test (level 1) and Multi-stage fitness test. All physical and performance variables measured in the study developed over time with chronological age except for the sum of 4 skinfold sites and estimated body fat composition (squad mean ± SD, U9 vs. U17: standing height, 139.4 ± 4.8 cm vs. 181.3 ± 5.6 cm; body mass, 33.6 ± 3.9 kg vs. 72.6 ± 5.7 kg; 30 m sprint, 5.26 ± 0.25 vs. 4.15 ± 0.11 s; slalom agility test, 4.83 ± 0.25 vs. 3.96 ± 0.09 s; counter movement jump with arms, 30 ± 3 cm vs. 48 ± 6 cm; the Yo-Yo intermittent recovery test (level 1), 787 ± 333 vs. 2617 ± 573 m). Standing height, body mass, 10, 15, and 30 m sprint times, performance on both agility tests, performance of squat jump and counter movement jump with arms; performance on the Yo-Yo intermittent recovery test (level 1) and on Multi-stage fitness test continued developing until the players reached the U17 squad. Moreover, the highest rate of development in standing height, body mass and all physical fitness tests occurred between the U9-U13 squads. Distance run during match play by 9 to 16 year old boys varied from 4056 (U9) to 7697 (U16) m per match (p < 0.05), and varied from 4675 to 6727 m·hour-1 of a match (p < 0.05). The U11-U16 squads covered a greater distance by high speed running (range: 487-553 mhour-1) compared to the U9 (178 m·hour-1) and U10 (219 m·hour-1) squads (p < 0.05 for all). Similarly, the percentage of time spent in high speed running by the U9 (1.1 %) and U10 (1.3 %) squads was less than that seen in the U11-U16 (2.6-3.0 %) squads (p < 0.05 for all). Chronological age accounted for 43% (p < 0.01), and the Multi-stage fitness test performance explained 7% (p < 0.05) of the variance in total distance covered per hour of a match in the U11-U16 group. Chronological age (p < 0.01) and the Multi-stage fitness test performance (p < 0.05) accounted for 10% and 11% respectively of the variance in percentage of time spent in moderate speed running. Chronological age accounted for 11 % of the variance in the percentage of time spent in high speed running (p < 0.01), whereas 30 m sprint and the Multi-stage fitness test performances explained 15% and 8% respectively of the variance in percentage of time spent in high speed running (p < 0.05 for both). The U9 and U10 squads showed a positive relationship between 20 m sprint time and distance covered in moderate speed running per hour of a match (r = 0.54, p < 0.05). In the U11-U13 squads relationships were evident between performance in 5, 10, 15, 20 and 30 m sprint (r = -0.67 to -0.46), the 3 standing vertical jumps (r = 0.46 to 0.73) and the 2 endurance tests (r = 0.45 to 0.60), and distance covered by moderate and high speed running per hour of a match (p < 0.05 for all). However, in the U14-U16 squads no significant relationships were evident. When stage of genital development was used to categorise players, standing height and body mass in the U12, U13 and U14 squads were positively influenced by biological maturity (p < 0.05 for all). The more mature players in the U13 squad also performed better in counter movement jump without arms and the Multi-stage fitness test (p < 0.05 for both). When stage of pubic hair development was used to categorise players, maturity status showed a positive influence on standing height and slalom agility test performance in the U12 squad (p < 0.05 for both) and on standing height and body mass in the U14 squad (p < 0.05 for both). When estimated chronological age at peak height velocity was used to categorise players, earlier maturing players were heavier (p < 0.01) and performed worse in counter movement jump without arms (p < 0.05) than later maturers in the U9 and U10 squads. Earlier maturers were taller (p < 0.01), heavier (p < 0.01) and possessed a thicker sum of 4 skinfold sites (p < 0.05) and higher estimated body fat (p < 0.01) compared to the later maturers in the U11 and U12 squads. Moreover, early maturers covered a greater distance than late maturers in the multi-stage fitness test (p < 0.05) in the U13 and U14 squads. In the U15 and U16 squads, early maturers were heavier and possessed thicker sum of 4 skinfold sites and higher estimated body fat compared to the late maturers (p < 0.01 for all). Furthermore, early maturers possessed a thicker sum of 4 skinfold sites (p < 0.05), higher estimated body fat (p < 0.01) and covered a shorter distance during the Yo-Yo intermittent recovery test (p < 0.01) compared to later maturers in the U17 and U18 squads. When stage of genital development was used to categorise players, the U12 and U13 players in stage 4 covered a greater distance in high speed running during a match than players in stage 3 (p < 0.05). There was a tendency for this still to be the case when distance was standardised into per hour of a match (p = 0.065). In the U9 and U10 squads, compared to later maturers, earlier maturers were given greater playing time during a match (p < 0.05), and consequently covered a greater distance during match play (p < 0.05). In the U13 and U14 squads, earlier maturers covered more distance per hour of a match and spent a higher percentage of time in high speed running when compared to their later maturing counterparts (p < 0.05 for both). In summary this research has provided the most extensive description yet of the physical characteristics, field test performance and match performance of elite youth soccer players. In addition, for the first time the effect of biological maturity (using 3 different methods of assessment) on a wide range of field tests and on match performance has been reported. The major changes in physical characteristics, field test performance and match performance between 10 and 14 years of age suggest that coaches should avoid as many selection decisions as possible during this age period, that they should take into account the fact that match distances covered at high speeds will be affected by maturity at these ages and that they should be aware that at present, coaches choose to give more mature players additional pitch time which obviously gives them an advantage in terms of playing development. An enhanced awareness of these findings in the coaching community could lead to an improved development and more appropriate selection decisions for elite youth soccer players in England.

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.<br>Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2009.

Sleep is a recurring state that is considered a critical process in the optimal attainment of daily functions and recovery in athletes. However individuals from elite sports, such as soccer, may be exposed to a number of situations that may impact sleep within the competitive season (such as inconsistent schedules and travel), which may result in sub-optimal sleeping patterns. However, research documenting the sleep of soccer players is at present limited. Therefore it would seem important to investigate how soccer players sleep to further the understanding of how sleep may be impacted. On this basis, the aim of the current thesis was to examine the typical sleeping patterns of youth soccer players and the factors effecting sleep. This was completed through a series of investigations conducted during the competitive youth soccer season. The aim of the first study (Chapter 3) was to validate a commercially available wireless sleep-monitoring device (WS). This was done in an attempt to provide a viable methodology to measure sleep within the habitual environment of soccer players. Nine randomly selected male participants were monitored over 3 nights and comparisons were made between the WS and other established field measures of sleep (Wristwatch actigraphy, sleep diary and Firstbeat bodyguard heart rate system). The relationships between the WS and the other sleep devices, indicated strong to very strong correlations (r > 0.6) and no significant differences between a range of outputs; total sleep time (Actigraphy assumed sleep time [0.97] & Sleep Diary [0.87] p > 0.05), sleep onset latency (Actigraphy [0.69] p > 0.05) and number of awakenings (Sleep Diary [0.69], p > 0.05). There were also low bias and narrow limits of agreement (LOA) within the comparison of mean differences with the WS for assumed sleep time (2 ± 17 min 95% LOA: -30 to 34 min [Actigraphy]), sleep onset latency (7 ± 17 min, 95% LOA -28 to 40 min [Actigraphy]), and number of awakenings (0.05 ± 1, 95% LOA -3 to 3 [Sleep Diary]). These results suggested that the WS is a viable device for the detection of these selected sleep variables. Chapter 4 looked to provide a comparison of sleep measures between a sample of youth soccer players (N=8) and non-athletes (N=8). Both groups were monitored over a period of 6 days within the habitual setting using the WS. The findings showed the soccer player group attained greater amounts of sleep quantity in comparison to the non-athlete group (504 ± 22 vs. 433 ± 46 min [+71 min] total sleep time, ES: 2.0, Large, p < 0.01). This may have been as a result of a later time of final awakening (08:54 ± 00:14 vs. 07:34 ± 00:46 [+77 min], ES: 1.7, Large, p < 0.01). Such an observation suggested that the soccer players were afforded greater time in bed as a result of the imposed soccer schedule. The soccer players also displayed a statistically greater time spent in wake (13(13) vs. 3(5) min [+10 min], PS: 0.86 ES: 1.5, Large, p < 0.05) on average each night. This data suggested that the sleep of the youth soccer players might be less efficient (as a consequence of greater levels of disturbance), despite desirable quantities of sleep being attained than non-athlete controls. Chapter 5 provided a daily comparison of sleep measures conducted over a 14-day assessment period. It is apparent that youth soccer players attained more sleep quantity in the nights preceding the match day (M-2: 488 ± 53 min [ES: 0.91, Moderate; p = 0.06] & M-1: 486 ± 64 min [ES: 0.84, Moderate; p = 0.02] respectively) in comparison to the night of the day after the match day (M+1: 422 ± 61 min). Such a finding suggested that youth soccer players adopt behaviours that reduce their sleep quantity on the designated recovery day (M+1) by >60 min, which may impact the recovery processes associated to this day. Relationships between sleep parameters and training and match load indicated a 100 au rise in RPELOAD (RPE * Duration) increased the time spent in wake (42 s [90% CI: 0 to 84 s]; ES: 0.36, Small; p = 0.098). It was also observed that an increase of 1000 m total distance increased the time spent in wake (40 s [90% CI: 5 to 75 s]; ES: 0.33, small; p = 0.06) A 100 m rise in high-speed running distance increased the number of awakenings observed (0.14 [90% CI: 0.03 to 0.25]; ES: 0.28, p =0.04) and the time spent in wake on average each night (1.5 min [90% CI: 0.78 to 2.3 min]; ES: 0.57, Small; p = 0.04). A similar outcome was observed in Chapter 6 were a 100 m rise in average high-speed running distance across three different 14-day training periods during the youth soccer season showed a 5 min increase in the time spent in wake on average (ES: 0.88, moderate; p = 0.04). Such outcomes provided a potential link between increases in training intensity (i.e. high-speed running distance) and sleep disturbances within youth soccer players. Increases in high-speed running distance also related to an increase of 24 min (90% CI: 12 to 36 min) on average for total sleep time (ES: 1.3, large; p < 0.01). Similarly increased high intensity heart rate (>85% max HR) was shown to effect total sleep time +20 min (90% CI: 6 to 32 min; ES: 0.87, moderate; p = 0.035). This may suggest that increases in intensity also may impact the amount of sleep quantity within youth soccer players. At present the mechanism for this response largely remains unknown. Within Chapter 7, a practical sleep hygiene strategy (10 min showering at ~40 °C, 20 min before time of lights out) was investigated. A group of ten youth soccer players were evaluated under normal sleeping conditions (control) and a shower intervention period, each consisting of three days within a randomized cross over trial design. Sleep information was collected using the WS. In addition to skin temperature, which was evaluated using iButton skin thermistors. The iButtons were used to establish both distal and proximal skin temperatures. This data was also used to create the distal to proximal gradient (average of distal measures – average of proximal measures = DPG). The data demonstrated that the shower intervention elevated distal skin temperature by (+1.1 °C [95% CI: 0.1 to 2.1 °C]; ES: 0.44, Small; p = 0.04) on average during a 10-minute period prior to lights out in comparison to the control condition. This elevation was also present during the first 30 minutes following lights out (1.0 °C [95% CI: 0.4 to 1.6 °C]; ES: 0.65, Moderate; p < 0.01), which was also accompanied by an increased DPG between conditions (0.7 °C [95% CI: 0.3 to 1.2 °C]; ES: 0.45, Small; p < 0.01). Additionally it was observed that on average the sleep onset latency of the youth soccer players was lower in the shower intervention condition (-7min [95% CI: -13 to -2 min]; ES -0.55, Moderate; p = 0.007). However no other sleep variable was affected as a result of the intervention. These findings demonstrate that a warm shower performed before lights out may offer a practical strategy to alter the thermoregulatory properties of distal skin temperature, which may advance sleep onset latency within youth soccer players.

<p> As youth sport has become increasingly professionalized, many believe that the route to elite level play is through early specialization. Early specialization is a contentious issue, and many risk factors have been associated with high levels and intensities of training in youth. Youth today participate in highly competitive sport in pursuit of elite levels of play, recognition, and financial gain. Early specialization is thought to be a requirement for advancement, yet little is known about the early experiences of team sport athletes who grew up in the US. This is the story of 15 elite female athletes who &ldquo;made it&rdquo; to Division I soccer. The study offers us a window into the professionalized and commercialized world of youth soccer. It is a description of the childhood and adolescent journeys through sport and spans 10+ years of development. Through its telling, we learn about the expensive pay-to-play pipeline in soccer, we hear of the differences in opportunities that exist between social classes, and we confirm theories of expertise development. We learn that whether a young athlete specializes early or chooses to play multiple sports has little relevance to her progression to Division I. Through our thematic analysis of injury, we see how young athletes routinely play through injury, hide injury from coaches, and carry injury forward into their collegiate playing careers.</p><p>

Background Organized sports offer an opportunity to promote physical activity and healthy eating. However, current data suggest that youth sports settings may not necessarily provide these benefits. In one study, youth were sedentary nearly half of a soccer match and in another foods and beverages offered at different youth sporting events were found to be energy-dense with little nutritional value. Parents, coaches and their respective sports organizations have the capacity to support a positive sports environment by promoting nutritious foods and beverages as well as optimal movement. To date, there is little research available on physical activity and sports trends of younger audiences, as well as perceptions of coaches and parents of young children toward sports snacks and policies to support healthy eating. The goal of these three studies were to better understand the youth soccer setting as an opportunity to address healthy eating and physical activity. Study 1 Objective: Assess snack offerings of parents and coaches of young soccer players, and policies. Methods: Beverage and Snack Questionnaires were distributed among all parents (n=120) and coaches (23) participating in recreational under five (U5) and under six (U6) soccer. The questionnaires assessed: socio-demographic information; types, as well as frequency, of snacks and beverages offered to children; reasons for snack and beverage choices; and attitudes toward snack policies. Results: Of the 44 parents and 23 coaches that participated, nutrition was ranked as the number one factor in choosing snacks and beverages for children participating in soccer. Yet parents and coaches reported offering many low-nutrient dense foods to their children as snacks. Coaches were receptive to limiting snack options and recommending healthy alternatives. Study 2 Objective: Observe snack offerings for young, recreational soccer players at combined practices and games to determine nutrient content and energy density of the foods and beverages provided. Methods: Snack observations for multiple, randomly selected teams were recorded using an observational checklist by trained researchers following all scheduled combined practices/games. Mean values across all snack foods and beverages were computed for the following key nutrients: calories, protein, fat, carbohydrates, fiber, sugars, and sodium. Results: Offered snacks were high in sugar, contributing nearly 77% of recommended total sugar intake per day, and low in sodium, fiber, and protein. Study 3 Objective: Determine the level of physical activity among young soccer players. Methods: Six random U5 and U6 teams were selected with 36 eligible players to participate in accelerometer collection data. Participants wore magnetic running pouches containing an accelerometer for a combined practice/game totaling 60 minutes. Informed, voluntary consent was obtained from each child and parent. Results: For the entire recorded session, average speed was 2.2 km/hour, average distance was 1.3 miles. Children were considered sedentary 55.0% of the recorded time. Discussion and Conclusions Organized sports settings offer an ideal avenue for promoting health and wellness among youth athletes. The current culture unfortunately promotes unhealthy snacking and sub-optimal physical activity. While the location and sample sizes limit generalizability, our results support research conducted with older children and highlight the importance of nutrition education for parents and coaches, as well as the potential for snack policies and strategies to encourage more vigorous physical activity in youth sports settings.<br>Master of Science