ACL Injuries: The Evolution of ACL Injury Prevention: From its nascency to the 11+ Holly Silvers-Granelli, MPT,

Transcription

1 ACL Injuries: The Evolution of ACL Injury Prevention: From its nascency to the 11+ Holly Silvers-Granelli, MPT,

2 Disclosures I have no financial disclosures or corporate conflicts of interest to report at this time

3 BACKGROUND Soccer is the most widely played sport amongst men & women 300 million registered players globally Dvorak 2005, FIFA rd most popular sport in the USA: 13 million participants US Census 2012, Jeffrey 2014 High School: 787,000 players: 412,000 male & 375,000 female College: 60,000 players: 27,000 male & 33,000 female Morris, 2015 Soccer injuries are not uncommon Depends on age, level of play, player position, field type, timing of injury and gender Arendt, Agel, Dick, 1995 & 1999

4 BACKGROUND 3 rd most popular female HS sport 375,000 players ( ) Morris, B, 2015:

5 BACKGROUND NCAA: Over 8,000 Female and 5,600 Male Division I Players McErlain, E, 2010:

6 INJURY RATES IN COLLEGIATE SOCCER Women s soccer: Game IR: 16.4 Practice IR: 5.2 Highest injury rate for any women s sport

7 Women s soccer: Game IR: 16.4 Practice IR: 5.2 Highest injury rate for any women s sport Men s soccer: Game IR: 18.8 Practice IR: rd Highest injury rate for any men s sports Hootman, 2007 INJURY RATES IN COLLEGIATE SOCCER

8 ACL INJURIES IN SOCCER High School ACL Injury Rate: Girl s soccer #1, Boy s soccer #3 LaBella, /5/e1437 Yard, Comstock R, Collins C, ,544 injuries in soccer annually < 18 years of age 43,125 ACL injuries occur annually in HS sports in the US Joseph, 2013

9 ACL INJURIES IN SOCCER ACL Injury rates in the NCAA Arendt and Dick, 1995 Data collected over a 5 year period ( ) 0.31 Women 0.13 Male NCAA ACL injury rates in females are nearly 3 times greater than males Arendt, 1999 Within 7 years after an ACL injury, 65% of individuals no longer play soccer Brophy, 2012 ACL Injury Rates in NCAA Soccer Women Men

10 ACL RE-INJURY RATES Risk of ACL re-injury ranges from 2.3 to 40% Significant risk in athletes < 25 y.o. Higher rates in athlete returning to cutting/pivoting sport (soccer, basketball, handball, American football) Study of ACLR s over 4 years (10-25 years of age): IR = 1.39/1000 AE in ACLR vs in control Equates to 6x > risk of re-injury Paterno, M. 2014, Wasserstein, D. 2015, Shelbourne, KD. 2009, Hagglund, M. 2006

11 % OF PROFESSIONALS THAT RTP 78 teams over 15 seasons ( ) Game IR > Practice IR by 20x (0.340 vs /1000 hours) Initial RTP rate: 97%, but 7% reinjured during training RTP after 3 years: only 65% players still playing Walden, M., Hagglund, M., et. al. BJSM, 2016:0;1-7

12 PERFORMANCE & CAREER LENGTH 71 ACL injuries over 3 seasons ( ) Compared to age-skill matched control ACL Group Uninjured 45.2% 38.7% Started Subbed 31% 16.1% Unused 8.9% 60.1% Significant difference in Starting % 60% uninjured athletes Started vs. 38.7% of ACL injured athletes Career Length: 1.4 ± 1.4 years vs. 2.5 ± 1.4 p< Effect size: 0.91 Arundale, A & Silvers-Granelli, H, OJSM, 2018

13 2 STUDIES: RISK OF SECONDARY INJURY Risk of re-injury to the ACL in approximately 20% - Risk Factors include < 19 years old Female Triple hop for distance = times height Triple hop for distance limb symmetry index (LSI) <98.5% Paterno, 2017 Risk Graft ruptures occurred in 18% patients 1.8 years after surgery. 47% within 1 year & 74% within the first 2 years Young males <18 years had the highest graft rupture rate of 28.3% Contralateral ACL injuries occurred in 17.7% 3.7 years after surgery. Webster & Feller, 2016

14 VIDEO ANALYSIS Two video analysis studies for mechanism of ACL injury 73% while defending (Female 87%, Male 63%, p =0.045) Tackling (51%) & cutting (15%): Hip & knee at/or near extension, knee valgus, foot planted, & unanticipated event 44% defending (n=11) & 20% landing after heading (n=5) 24% direct contact with leg or knee (n=6) Brophy, R & Walden, M. 2015

15 VIDEO ANALYSIS Two video analysis studies for mechanism of ACL injury 27 y.o. midfielder, USMNT 2010 R Tib FX 2011 L distal MFC OCD 2011 L MFC OCD Grade IV (microfracture) 2013 R ACL injury with Gr III OCD (over existing OATS repair)

16 Combined ACL / AC Injury

17 Case Study: US Soccer Footage courtesy of Bolton FC

18 SECONDARY INJURY

19 % Decrease in ACL Tears INJURY PREVENTION ACL Injury programs have been scientifically vetted (Sportsmetrics, Oslo Program, PEP, FIFA 11+) % 88 % % 73 % 74 % % 60 % Caraffa Ettlinger Heidt Henning Hewett Mandelbaum Gilchrist

20 Santa Monica ACL Prevention Project : INJURY PREVENTION PEP Program Prevent injury and Enhance Performance 1. Avoidance 2. Agilities 3. Strengthening 4. Plyometrics 5. Flexibility

21 Santa Monica ACL Prevention Project : INJURY PREVENTION PEP Program Prevent injury and Enhance Performance 1. Avoidance 2. Agilities 3. Strengthening 4. Plyometrics 5. Flexibility

22 Methods and Materials LA 84, FIFA & NCAA funded project Athletes from Coast Soccer League & NCAA (Div I) Perform 2 to 3 times a week 20 minutes to complete Program Dissemination: instructional video, folders and web: Quality assurance and compliance measured

23 Results: Year 1 Per 1,000 Exposures Mandelbaum, Silvers et. al, Am J Sports Med Jul;33(7): Control Enrolled # of Reported ACL Tears (p value <.05) Control Enrolled Control: 32 ACL s in 1901 athletes = 1.7 incidence rate Enrolled: 2 ACL s in 1041 athletes =.2 incidence rate Overall 88% reduction in ACL tears per 1,000 exposures

24 Results: Year 2 Per 1,000 Exposures Mandelbaum, Silvers et. al, Am J Sports Med Jul;33(7): Control Enrolled # of Reported ACL Tears (p value <.05) 35 Control Enrolled Control: 35 ACL s in 1913 athletes = 1.8 incidence rate Enrolled: 4 ACL s in 844 athletes =.47 incidence rate Overall 74% reduction in ACL tears per 1,000 exposures

25 ACL Prevention in the Soccer Athlete (Gilchrist, Mandelbaum, Silvers, Am J Sports Med Aug;36(8): ) NCAA Div. I women s soccer - PEP 61 Teams (833 Control / 561 Intervention) Injury Rate: 0.04 Intervention vs Control Non Contact ACL Injuries occurred over three times more frequently in control vs. intervention

26 Results of NCAA Study (Gilchrist, Mandelbaum, Silvers Am J Sports Med Aug;36(8): ) Control (N) Rate/1000AE Intervention (N) Rate/1000AE % decrease P-value Total (practice & games) ACL (18) 0.25 (7) % NC-ACL (10) 0.14 (2) % Practice ACL (6) 0.10 (0) % NC-ACL (3) 0.05 (0) % 0.083

27 Results of NCAA Study (Gilchrist, Mandelbaum, Silvers Am J Sports Med Aug;36(8): ) Control (N) Rate/1000AE Intervention (N) Rate/1000AE % decrease P-value Total (practice & games) ACL (18) 0.25 (7) % NC-ACL (10) 0.14 (2) % Practice ACL (6) 0.10 (0) % NC-ACL (3) 0.05 (0) % 0.083

28 Results of NCAA Study (Gilchrist, Mandelbaum, Silvers Am J Sports Med Aug;36(8): ) Control Intervention % (N) Rate/1000AE (N) Rate/1000AE decrease P-value Late in Season (weeks 6-11) ACL (5) 0.18 (0) % NC-ACL (3) 0.11 (0) % History of ACL ACL (7) 0.10 (1) % NC-ACL (4) 0.06 (0) % 0.046

29 Results of NCAA Study (Gilchrist, Mandelbaum, Silvers Am J Sports Med Aug;36(8): Control Intervention % (N) Rate/1000AE (N) Rate/1000AE decrease P-value Late in Season (weeks 6-11) ACL (5) 0.18 (0) % NC-ACL (3) 0.11 (0) % History of ACL ACL (7) 0.10 (1) % NC-ACL (4) 0.06 (0) % 0.046

30 % Decrease in ACL Tears INJURY PREVENTION Numerous attempts have been made to decrease sports related injury, namely ACL injury Caraffa 1996, Ettlinger 1995, Heidt 2000, Hewett 1999, Mandelbaum 2005, Myklebust 2003 & 2007, Gilchrist 2008 However, many other deleterious injuries remain exceedingly common in the sport of soccer Is there an effective intervention method to decrease all soccer related injury? 100 ACL injury Prevention Program Efficacy (% decrease) % 62 % 73 % to Caraffa Ettlinger Heidt Henning Hewett Mandelbaum Gilchrist 89 % 60 % 88 % 74 %

31 DEVELOPMENT OF FIFA 11+ International group: Oslo, Switzerland and USA in 2005 The FIFA 11+: dynamic warm-up designed to ALL injury On-field warm-up: minutes with no additional equipment necessary Imparts physiological & neuromuscular preparedness Addresses musculature not directly associated w/ sport Warm-up Agility Training Game/Training Dissertation Defense Silvers-Granelli

32 FIFA 11+ PROGRESSION 3 Levels for optimal training progression (individual &/or per team) increasing complexity

33 DEVELOPMENT OF FIFA 11+ Biomechanical goals: hip & knee flexion, hip IR, knee valgus & abduction, and vgrf during landing (proper technique) Correct Incorrect

34 RESEARCH 11+ Initially tested in large RCT in Norwegian female soccer players: N = 1892, aged % in all injuries 53% in overuse injury and a 45% in severe injury Soligard, 2008

35 ROLE OF COMPLIANCE Teams that were highly compliant to the FIFA 11+ program had fewer injuries: inverse correlation (p<.05) Soligard, 2008

36 Prospective RCT (Level I) NCAA Member Institutions = 396 Division I 204 teams Division II 192 teams Every Athletic Director, Certified ATC & head coach contacted Letter, and phone contact to each institution METHODOLOGY: AIM 1 & 2

37 METHODOLOGY: AIM 1 & 2 DVD s, instructional manuals, PDF posters were distributed to each intervention (INT) team Conference call with all of the intervention ATC s scheduled to review intervention protocol Warm-up performed 2-3 times/week Injury/compliance tracked weekly for both INT and CON teams CON teams performed their traditional warm-up received 11+ materials at the end of the season

38 RESULTS: AIM 1.1 Text 46.2% in Injury Control: 34 Teams, N = 850 Intervention: 27 Teams, N = 675 Age (mean) /- 1.5 years /-1.7 years # of exposures (AE) # of Injuries/Total % Incidence Rate (IR) 44,212 AE s Game: 13,624, Practice: 30, injuries (70%) M: ± 11.01* 15.04/1,000 AE* G, 8.93 P 35,226 AE s Game:10,935 Practice: 24, injuries (30%) M: ± 3.64* 8.09/1,000 AE* G, 4.01 P Days lost to injury 8790 (M = 13.2±1.09 days)* 2944 (M = 9.94±0.96 days)* * Denotes statistical significance (p < 0.05)

39 RESULTS: AIM 1.1 Text 32.8% in time loss Control: 34 Teams, N = 850 Intervention: 27 Teams, N = 675 Age (mean) /- 1.5 years /-1.7 years # of exposures (AE) # of Injuries/Total % Incidence Rate (IR) 44,212 AE s Game: 13,624, Practice: 30, injuries (70%) M: ± 11.01* 15.04/1,000 AE* G, 8.93 P 35,226 AE s Game:10,935 Practice: 24, injuries (30%) M: ± 3.64* 8.09/1,000 AE* G, 4.01 P Days lost to injury 8790 (M = 13.2±1.09 days)* 2944 (M = 9.94±0.96 days)* * Denotes statistical significance (p < 0.05)

40 Injury Rate per 1,000 AE RESULTS Text Injuries rates (IR) stratified by body part: Injury by 46.2% Type: reduction IR/1000 (p Athletic = 0.002) Exposures Control Intervention Injury Type

41 RESULTS Days lost to injury Control Intervention Significantly higher number of days missed in the CG (13.02 CG vs IG, p=.049) For each day missed in the IG, 1.4 days were missed in the CG (OR = 1.40) To reduce 1 injury, 3 players needed to be exposed (NNT = 3)

42 Injuries stratified by Division and Game vs. Practice RESULTS Significant difference in DI game, practice and DII practice injuries

43 # of days RESULTS % of Injuries by Days Missed No time Intervention Control For each day missed in the IG, 1.4 days were missed in the CG (OR = 1.40, p=0.007) CG had higher % of no time loss injuries and lower % of injuries > 30 days

44 Days missed due to injury RESULTS 12 Days missed due to injury: FIFA 11+ vs. Traditional Warm-up 10.65± ±10.44 FIFA 11+ Used Traditional Warm-up Used 2 0 FIFA 11+ Used Traditional Warm-up Used There were significantly fewer days missed when the FIFA 11+ was used on the day of injury in the IG, solely Program used: (M=6.56 ±10.44 days) Program not used: (M ±15.35 days, p =.049)

45 ACL INJURY Does the FIFA 11+ Program decrease the rate of ACL injury in male soccer players?

46 RESULTS Analysis of ACL Injury Rate Control Intervention RR (95% CI) P value N / % IR N / % IR RR (95% CI) P value Total Injuries Total 665/100% Total 285/100% ( ) <0.001* Game 392/58.9% Game 185/64.9% ( ) <0.001* Practice 273/41.1% 8.93 Practice 100/35.1% ( ) <0.001* Knee Injuries N / % IR N / % IR RR (95% CI) P value Total 102/15.3% Total 34/11.9% ( ) <0.001* N / % IR N / % IR RR (95% CI) P value Mechanism of ACL Total 16 /2.41% Total 3/1.05% ( ) 0.021* Contact 6/0.90% Contact 1/0.35% ( ) Non-contact 10/1.50% Non-contact 2/0.70% ( ) 0.049* Significant decrease in Total Knee Injury Rate (58%, p<0.001)

47 RESULTS Analysis of ACL Injury Rate Control Intervention RR (95% CI) P value N / % IR N / % IR RR (95% CI) P value Total Injuries Total 665/100% Total 285/100% ( ) <0.001* Game 392/58.9% Game 185/64.9% ( ) <0.001* Practice 273/41.1% 8.93 Practice 100/35.1% ( ) <0.001* Knee Injuries N / % IR N / % IR RR (95% CI) P value Total 102/15.3% Total 34/11.9% ( ) <0.001* N / % IR N / % IR RR (95% CI) P value Mechanism of ACL Total 16 /2.41% Total 3/1.05% ( ) 0.021* Contact 6/0.90% Contact 1/0.35% ( ) Non-contact 10/1.50% Non-contact 2/0.70% ( ) 0.049* Athletes using the FIFA 11+ demonstrated a 46.2% reduction in all injury types 41% decrease in Game IR (p<0.001) 54% decrease in Practice IR (p<0.001)

48 RESULTS: AIM 1.4 Analysis of ACL Injury Rate Control Intervention RR (95% CI) P value N / % IR N / % IR RR (95% CI) P value Total Injuries Total 665/100% Total 285/100% ( ) <0.001* Game 392/58.9% Game 185/64.9% ( ) <0.001* Practice 273/41.1% 8.93 Practice 100/35.1% ( ) <0.001* Knee Injuries N / % IR N / % IR RR (95% CI) P value Total 102/15.3% Total 34/11.9% ( ) <0.001* N / % IR N / % IR RR (95% CI) P value Mechanism of ACL Total 16 /2.41% Total 3/1.05% ( ) 0.021* Contact 6/0.90% Contact 1/0.35% ( ) Non-contact 10/1.50% Non-contact 2/0.70% ( ) 0.049* Significant decrease in Total Knee Injury Rate (58%, p<0.001)

49 RESULTS Analysis of ACL Injury Rate Control Intervention RR (95% CI) P value N / % IR N / % IR RR (95% CI) P value Total Injuries Total 665/100% Total 285/100% ( ) <0.001* Game 392/58.9% Game 185/64.9% ( ) <0.001* Practice 273/41.1% 8.93 Practice 100/35.1% ( ) <0.001* Knee Injuries N / % IR N / % IR RR (95% CI) P value Total 102/15.3% Total 34/11.9% ( ) <0.001* N / % IR N / % IR RR (95% CI) P value Mechanism of ACL Total 16 /2.41% Total 3/1.05% ( ) 0.021* Contact 6/0.90% Contact 1/0.35% ( ) Non-contact 10/1.50% Non-contact 2/0.70% ( ) 0.049* Significant decrease in Total ACL IR (76%, p=0.021) Significant decrease in Non-contact ACL IR (75%, p=0.049) No statistical difference in contact ACL IR (p=0.148)

50 % Injury Reduction FIFA 11+ INJURY RATE Average decrease in overall injury IR for the current peerreviewed FIFA 11+ interventions = 53.2% 80 FIFA 11+ % Injury Reduction Soligard Grooms Owoeye Steffen Longo Silvers

51 RATIONALE A variable that is very worthy of discussion is the role of compliance Compliance to an injury prevention program is inversely correlated to injury rate; the more regularly the training programs are implemented, the lower the IR. Soligard, 2008 High adherence to injury prevention programs (FIFA 11+) resulted in lower IR in Canadian youth female soccer (IRR=0.28, 95% CI: 0.10 to 0.79). Steffan, 2013 In contrast, when compliance and adherence to a program is diminished, the propensity of the prevention program to be effective is limited as well. Steffan, 2008

52 STUDY COMPLIANCE Average use of the FIFA 11+: ± sessions per team 2.34 sessions/team/week Three compliance categories/tertiles were created Low compliance (LC): 1-19 doses/season, 9 teams Moderate (MC): doses/season, 14 teams High (HC): >40 doses per season, 4 teams Compliance Teams 1st half of season 2nd half of season High (n=9 teams / 225 athletes) Moderate (n=14 teams / 50 athletes) Low (n=4 teams / 100 athletes) Mean±SD Range Mean±SD Range Mean±SD Range 26.56± ± ± ± ± ± Entire season 45.78± ± ±

53 STUDY COMPLIANCE Average use of the FIFA 11+: ± sessions per team 2.34 sessions/team/week Use of program was higher in 1 st half of the season vs. 2 nd half Variation based on success of campaign Schools with extended season continued to utilize the program Compliance Teams 1st half of season 2nd half of season High (n=9 teams / 225 athletes) Moderate (n=14 teams / 50 athletes) Low (n=4 teams / 100 athletes) Mean±SD Range Mean±SD Range Mean±SD Range 26.56± ± ± ± ± ± Entire season 45.78± ± ±

54 COMPLIANCE AND INJURY RATE Does compliance impact injury rate? High Compliance group had statistically fewer injuries than Low and Moderate compliance groups (p=0.034) Compliance # Teams / # athletes # of Injuries Injury Rate Rate Ratio 95% CI p Value High 9/225 athletes ± Moderate 14/350 athletes ± ( ) Low 4/100 athletes ± ( ) >.001 High compliance group served as the reference group

55 FIFA 11+ Dose/season Injury Rate COMPLIANCE AND INJURY RATE Does compliance impact injury rate? Statistical difference between High compliance and Low/Moderate groups (p =.034, R 2 =.29) Injury Rate in relationship to compliance Low (1-19) Mod (20-39) High (>40) Compliance FIFA 11+ Injury rate

56 FIFA 11+ Dose/season Injury Rate FEMALE VS MALE COHORT Comparison of FIFA 11+ Female cohort & Male cohort NCAA Male Data FIFA 11+ Norwegian Female Data FIFA Injury Rate in relationship to compliance Low (1-19) Mod (20-39) High (>40) FIFA 11+ Compliance Injury rate Soligard, BMJ, 2009 & Silvers-Granelli, AJSM, 2015

57 COMPLIANCE AND TIME LOSS Does compliance impact time loss due to injury? LC group (N=4 teams) = ± 54.13, 9.60 days MC group (N= 14) = ± days, days HC group (N=9 teams) = ± days, 7.59 days. Low Moderate High Total 95% CI SE p Teams/Athletes 4 / / / / Utilization 15.75± ± ± ± < Injuries 13.25± ± ± ± < Time lost By Team ± ± ± ± < Per Injury 9.60± ± ± ± < 0.001

58 COMPLIANCE AND TIME LOSS Does compliance impact time loss due to injury? Statistical difference between High compliance and Low/Moderate groups (p =.004, R 2 =.29)

59 COMPLIANCE & PERFORMANCE Does improved compliance impact team performance? and Are highly compliant teams more successful?

60 Games RESULTS Division I win/loss record compared to FIFA 11+ utilization * * 8.476* * FIFA11+ 6 CONTROL * p< WINS LOSSES TIES

61 Games RESULTS Division II win/loss record compared to FIFA 11+ utilization * * FIFA11+ CONTROL * p< WINS LOSSES TIES

62 Total win/loss record compared to FIFA 11+ utilization RESULTS * * 7.905* * FIFA CONTROL * p< WINS LOSSES TIES

63 COMPLIANCE AND PERFORMANCE Does compliance impact performance? Statistical difference between High and moderate (for Wins & Losses) and low (for Losses) with CG (p<0.001)

64 SUMMARY OF FINDINGS Compliance is inversely correlated to injury rate: utilization of FIFA 11+ resulted in injuries High compliance is correlated to time loss due to injury FIFA 11+ teams performed favorably: more wins and fewer losses Useful to increase compliance with coaches, players and managers. Improve program implementation rate and fidelity. More Wins & Fewer Losses Lower Injury Rates Decreased Time Loss/ Severity

65 AIM 3: METHODOLOGY Six Screening Test used in Pre/Post season Testing Protocol Step Down Drop Jump Deceleration Cutting Lateral Shuffle Triple Hop Thorough scripted instruction and demonstration by research staff 8- camera Vicon motion analysis system sampling at 240 Hz 2 - Bertec Force Plates sampling at 1,080 Hz Three trials of each movement (6) were tested 6 movements: Step-down, DJ, deceleration, cut, lateral shuffle, triple hop Step-down and Triple hop analyzed for this investigation Dissertation Defense Silvers-Granelli

66 AIM 3: METHODOLOGY MPI Scoring System Exercise Score Hip Stability Pelvis Stability Trunk Stability Hip Strategy Shock Absorption Step Down 0-8 X X X X Drop Jump 0-6 X X X Lateral Shuffle Deceleratio n 0-6 X X X 0-10 X X X X X Triple Jump 0-10 X X X X X Side Step Cut 0-10 X X X X X 2 point maximum / category for 50 point maximum score Powers, unpublished Dissertation Defense Silvers-Granelli

67 Single leg squat test/step Down (SLST) SLST valid tool to screen for NM deficits associated with knee valgus and LE injury Herrington, 2014, Graci, 2012 knee abduction, hip adduction, hip IR, hip anteversion, hip and knee flexion angles & medio-lateral displacement SINGLE LEG SQUAT SLST is a reliable testing tool to identify at risk athletes. Munro, 2012, Ageberg, 2010 Dissertation Defense Silvers-Granelli

68 TRIPLE HOP Triple Hop (TH) TH can be a valid tool to screen for deleterious knee flexion excursion following a single leg landing task Cacolice, 2015 Cacolice, et. al. Int J Sports PT Aug; 10(4): Inc. peak knee abduction angle/moments are correlated to poor performance in change of direction tasks (cutting/pivoting) TH is a reliable testing tool to identify at risk athletes. Herrington, 2014 Dissertation Defense Silvers-Granelli

69 AIM 3: DEMOGRAPHICS No significant differences in demographic variables Age of starting soccer play was older in the control group (p=0.004) Age of sport designation was older in intervention group (not significant) Dissertation Defense Silvers-Granelli

70 AIM 3: DEMOGRAPHICS No significant differences in year of eligibility, player position or leg dominance between groups Dissertation Defense Silvers-Granelli

71 AIM 3: RESULTS SLST Interactions (group*time) found for 7 variables: Hip Flex A, Hip Ext M, Hip Add A, Hip Abd M, Hip IR A, Knee Flex A, Knee Abd A Partial η2 within and b/w groups: 0.01 = small effect, 0.06 = medium effect, and 0.14 = large effect

72 Degrees Degrees Degrees Flexion NM/kg*m SINGLE LEG SQUAT * Hip Flexion Angle * Sagittal Plane Hip Moment 0 Pre-test Control Post-test Intervention Pre-test Control Post-test Intervention B/W: p=0.001, CG: p=0.483 IG: p=0.003 B/W: p=0.002, CG: p=0.073 IG: p=0.002 * Hip Internal Rotation Angle Pre-test Post-test * Knee Flexion Angle Pre-test Post-test Control Intervention Control Intervention B/W: p=0.001, CG: p=0.004, IG: p=0.007 B/W: p=0.05, CG: p=0.046, IG: p=0.007 Dissertation Defense Silvers-Granelli

73 SINGLE LEG SQUAT During the post-hoc analysis, there were favorable statistical differences found: hip flexion angle (p<0.001, partial η2 = ) hip extensor moment (p=0.002, partial η2 = 0.125) hip internal rotation angle (p<0.001, partial η2 = 0.146) peak knee flexion angle (p=0.05, partial η2 = 0.061) A majority of the kinetic and kinematic changes occurring at the hip. No frontal plane differences. Only one variable demonstrated a main effect at the knee (peak knee flexion) Dissertation Defense Silvers-Granelli

74 TRIPLE HOP Interactions (group*time)found for 7 variables: Hip Flex A, Hip Ext M, Hip Add A, Hip IR A, Peak knee Flex A, PKAM, Knee Abd A Dissertation Defense Silvers-Granelli

75 Degrees Nm/kg*m Degrees Abduction Adduction Nm/kg*m AIM 3: TRIPLE HOP Hip Flexion Angle * Frontal Plane Hip Moment Pre-test Post-test Control Intervention B/W: p<0.001, CG: p=0.24 IG: p= Pre-test Post-test Control Intervention B/W: p=0.05, CG: p=0.01, IG: p=0.02 * Hip Internal Rotation Angle Pre-test Post-test Control Intervention B/W: p<0.001, CG: p=0.01 IG: p<0.001 * Knee Flexion Moment Pre-test Control Intervention Post-test B/W: p=0.005, CG: p=0.01, IG: p=0.09 Dissertation Defense Silvers-Granelli

76 AIM 3: TH & VGRF During the within-group pre/post analysis, there were favorable statistical differences found for the for IG: decreased hip IR angle (p=0.01,partial η2 = 0.178) decreased hip ADD moment (p<0.001, partial η2 = 0.168) and decreased knee flexion moment in CG (p=0.005, partial η2 = 0.214) Similar to SLST, a majority of the kinetic and kinematic changes occurring at the hip No significant differences in vgrf Dissertation Defense Silvers-Granelli

77 Negative Findings AIM 3: RESULTS No statistical difference in vgrf during triple hop No decrease in injury rate during biomechanical testing Minimal changes to knee kinetics and kinematics Minimal frontal plane changes Subclinical findings? Positive Findings Step Down: hip flexion angle, hip flex M, hip IR angle and peak knee flexion angle Triple Hop: hip add M, hip IR angle and Peak knee flexion M in CG Dissertation Defense Silvers-Granelli

78 INTERPRETATION The FIFA 11+ has a heavy emphasis on core/trunk and proximal control (hip). Biomechanical analysis showed majority of Δ occurring at hip in during single limb task analysis Does the program do enough to initiate an effective kinetic and kinematic change at the knee? Dissertation Defense Silvers-Granelli

79 ANALYSIS OF CHANGE Single Leg Squat hip flexion angle hip flexor moment hip IR angle peak knee flexion angle Triple Hop Maintain hip flexion hip adduction moment hip internal rotation angle peak knee flexion moment in CG Dissertation Defense Silvers-Granelli

80 FUTURE DIRECTIONS Determine if significant injuries can be detected in pretesting (i.e. deleterious pathokinematics are they predictive?) Determine if the screening tool has the intended specificity to identify high risk populations or ability to return to prior level of play testing battery Continue to address the issue of program adoption and team compliance throughout the course of the athletic season Refine existing injury prevention protocols and therapeutic interventions to reflect our new knowledge

81 Thank You! Holly Silvers, MPT, PhD