1. No Concussion Upon Evaluation
Diagnostic Accuracy of Instrumented Helmets to Assess Concussions in High School Football Athletics Irby A
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Concussions are the 3rd most common injury for youth football players.1,2
300,000 HS students experience a concussion a year.1,2
Concussions are important to recognize in youth and adolescents because of long-term impact on the brain.3,4
Concussion monitoring systems are subjective, relying on patients self-reporting symptoms.5
Helmet telemetry systems have become popular to assist in detecting concussions, yet limited data exists to determine their effectiveness.6-7
To determine the diagnostic accuracy of a helmet telemetry system in a HS football population.
To determine if there are differences in impact detections among football players depending on playing position.
46 Varsity and Junior Varsity players (Table 1)
Players were categorized as skill (QB, WR, RB, CB, S, K) and non-skill (OL, TE, DE, DL, DB)
Background
Methodology
Results
Sensitivity: 0.50
Specificity: 0.98
Positive Predictive Value: 0.02
Negative Predictive Value: 0.99
Instrumented helmets demonstrated poor diagnostic accuracy to alert for concussions.
Non-skills players took significantly more hits throughout the season as compared to skills players, particularly to the front of the head.
Future studies should assess whether non-skill players are more at risk for sustaining concussions and the diagnostic accuracy with more robust sample sizes.
References
Datalys Center for Sports Injury Research and Prevention (2015). [Graph illustration of youth football facts from ISS data] ISS Data Access from the Datalys Center. Retrieved from .
Datalys Center for Sports Injury Research and Prevention (2015). [Graph illustration of ncaa football injury facts from ISS data] ISS Data Access from the Datalys Center. Retrieved from .
Harmon, KG, Drezner, JA, & Gammons, M Endorsed by the National Trainers’ Athletic Association and the American College of Sports Medicine, et al (2013) American medical society for sports medicine position statement: concussion in sport Br J Sports Med, 47,15-26.
McCrory, P, Meeuwisse, W, & Dvořák, J, et al. (2017) Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016; Br J Sports Med, 5(1),
Broglio, S. P., Ferrara, M. S., Macciocchi, S. N., Baumgartner, T. A., & Elliott, R. (2007). Test-Retest Reliability of Computerized Concussion Assessment Programs. Journal of Athletic Training, 42(4), 509–514.
Crisco, J. J., Fiore, R., Beckwith, J. G., Chu, J. J., Brolinson, P. G., Duma, S., & Greenwald, R. M. (2010). Frequency and Location of Head Impact Exposures in Individual Collegiate Football Players. Journal of Athletic Training, 45(6), 549–559.
Beckwith JG, Greenwald RM, Chu JJ, et al. Timing of Concussion Diagnosis is Related to Head Impact Exposure Prior to Injury. Medicine and science in sports and exercise. 2013;45(4): doi: /MSS.0b013e
Quantitative, prospective cohort study.
All players wore a Riddell Insite Instrumented Helmet system throughout the season.
Players were tracked throughout the season to show the location and level of impact was also tracked through the system.
Alerts registered at a 99th percentile level of impact and were compared to all hits of varying levels.
Exposures included both games and practices.
Statistical Analysis
2x2 contingency tables to calculate sensitivity, specificity, and likelihood ratios to compare the times helmets registered alerts with the incidence in which players suffered concussions.
Independent samples t-tests (p<0.05) were utilized to assess differences in hit impacts and locations between skill and non-skill players.
Concussion
No Concussion Upon Evaluation
Alert 2
110
No Alert
6674
Table 2: 2x2 contingency table to show diagnostic accuracy of instrumented helmets
Objectives
Total hits*
Front*
Top
Right
Back
Left
Skill
90.7± 68.2
27.0± 26.4
14.8±22.4
20.0±23.5
13.5±15.3
17.1±24.5
Non-Skill
204.3±177.9
118.5±123.8
26.2±31.0
20.4±38.0
12.4±17.1
30.6±42.0
Table 3: Hits between skill and non-skill players. *indicates significant difference (p<0.05)
Conclusion
Participants
Figure 1: instrumented helmet insert
Figure 2: Handheld helmet monitor
Anterior Posterior
Height (m)
Mass (kg)
BMI
Skill (n=23)
1.8±0.1
77.0±5.4
23.2±1.6
Non-Skill (n=23)
102.3±19.59
29.65±5.45
Medial Lateral
Table 1: Demographic Data
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