Independent standards organizations have created basic safety criteria that helmets must meet prior to commercial sale, but the focus of helmet regulations is to reduce head impact forces to minimize catastrophic injury risk.1 Current helmet guidelines do not strongly consider how the helmet design may influence the visual performance of the wearer, which is critical to sport safety and performance. Vision is important for sports generally, because advanced ability to identify and react to peripheral stimuli, quickly shift gaze, and track objects while in motion all contribute to superior athletic performance. Enhanced visual reaction time, visual memory, and visual discrimination have been shown to directly translate to improved sport performance2 and reduce musculoskeletal injury risk.3
Vision is also an important factor to consider in relation to ability to respond to potentially injurious stimuli, and specifically the role it plays in sport concussion. Sharp visual ability, especially heightened peripheral vision, is key to an athlete's ability to anticipate an impending collision.4 Sufficient anticipation allows athletes to properly position themselves to reduce the forces imparted to the head or move to avoid the collision,4,5 thereby reducing injury risk. When an athlete does not anticipate a collision, his or her head tends to be the first point of contact and resulting head impact forces are more severe.6 Concussions are particularly common in collision sports, with the highest incidence rates seen in men's football, lacrosse, and ice hockey.7–10 Although helmets are instrumental for preventing potentially catastrophic injuries such as skull fractures, facial fractures, and brain bleeds, they are unable to prevent concussions.11
The design of certain helmets may pose a hindrance to users' vision,12 which can be a detriment to both sport safety and performance. Few studies have investigated the effect of helmets on visual performance. Motorcycle helmets were detrimental to peripheral vision,12 whereas there was no evidence of impaired vision from ski helmets.13 New regulations in cricket led to adjustments in helmet design; the new helmet (compared to the previous design) resulted in a significant reduction in the visual field of the wearer.14 To date, no known testing has been performed to determine the implications of football, lacrosse, or ice hockey helmets on vision performance. A comprehensive investigation of helmet use on the athletes' vision performance is necessary to optimize athlete performance and enhance athlete safety.
Therefore, the purpose of this study was to explore vision performance outcomes in male football, lacrosse, and ice hockey athletes under helmeted and unhelmeted conditions. It was hypothesized that vision performance would be worse in the helmeted condition compared to the unhelmeted condition.
We studied a convenience sample of 24 healthy, recreationally active men (age: 21.2 ± 2.00 years) who had experience playing football (n = 14), lacrosse (n = 5), or ice hockey (n = 5). Football and ice hockey are male-only competitive sports at our institution, and only male lacrosse players wear full helmets; thus, a completely male sample appropriately represented the defined population of interest.
All participants had played football, lacrosse, or ice hockey at a minimum of a high school varsity level and remained recreationally active, defined as engaging in 30 minutes or more of physical activity on 3 or more days of the week. Participants were excluded if they had (1) sustained a concussion within the past year; (2) neurological deficits, skull fracture, brain bleed, or concussion resulting in 3 or more weeks of activity loss at any time; (3) permanent vision loss, strabismus, or corrective eye surgery; (4) diagnosed or self-reported psychological conditions; (5) dizziness or abnormal vestibular function; or (6) head, neck, shoulder, or back abnormalities affecting normal range of motion. The institutional review board at The University of North Carolina at Chapel Hill approved this study and all participants provided informed consent prior to participation.
We performed a crossover design in healthy men in which they completed assessments of vision performance under counterbalanced helmeted and unhelmeted conditions. Testing was completed in a single 90-minute session. Participants completed all assessments with best-corrected vision. Before completing the assessments, participants filled out a questionnaire to confirm that inclusion criteria were met and to collect demographic information, sport history, and level of comfort in a helmet. Participants were fitted and provided an appropriate helmet by the researchers at the time of testing. Participants were allowed to provide their own helmet if the researchers deemed the fit appropriate and if it was the same make and model as the other helmets used. The make, model, and size of the helmet used were recorded for each participant. The participant performed the helmeted condition only with the helmet type (ie, football, hockey, or lacrosse) that matched their sport history.
Vision performance was assessed with the Senaptec Sensory Station (Senaptec, Inc., Beaverton, OR), a computerized evaluation and training tool. It consists of a 55-inch touch screen monitor, a 14-inch touch-screen tablet, and a Motorola touch-screen phone acting as a remote to interact with the technology. The Senaptec Sensory Station is a direct successor to the version originally developed by Nike, Inc., which is a reliable15,16 and valid17 device. The Senaptec Sensory Station assesses domains of visual clarity, contrast sensitivity, depth perception, near-far quickness, target capture, perception span, multiple object tracking, eye-hand coordination, go/no go tasks, and hand reaction time. Participants completed visual clarity, contrast sensitivity, depth perception, near-far quickness, and target capture at 10 feet away from the displays using the touch-screen phone, and completed the remaining five assessments at 2 feet away from the displays. Assessments are summarized in Table A (available in the online version of this article). Participants completed all ten Senaptec Sensory Station assessments under two counterbalanced conditions (helmeted or unhelmeted) to account for fatigue or potential short-term learning effects.16
Description of Senaptec Sensory Station Assessments
All football helmets worn during the study were Riddell Speed (BRG Sports) models, with standard football facemasks. All lacrosse helmets were STX Stallion models with a full facemask. Hockey helmet models varied (Bauer, Bauer Hockey, LLC; and CCM, Sport Maksa, Inc.), but all included a full, wire cage (ie, not a clear shield). Representative images of the helmet types are provided in Figure 1.
Helmet types employed in this study, manufactured by BRG Sports, STX, Sport Maksa, Inc., and Bauer Hockey, LLC, respectively.
The following measurements were used to quantify the Senaptec Sensory Station vision performance domains: visual clarity (measured as logarithm of the minimum angle of resolution [logMAR]), contrast sensitivity (threshold for 18 degrees/cycle frequencies as a logarithm of contrast sensitivity), depth perception (threshold in arcsec), near-far quickness (number of near-far switches in fixation completed in 30 seconds), target capture (threshold response time in milliseconds), perception span (total number of dots correctly identified), multiple object tracking (tracking capacity), eye-hand coordination (average response time in milliseconds over 80 trials), go/no go tasks (correct stimuli responses minus incorrect stimuli responses, with 25% credit given to near-miss correct stimuli hit within the next 500 ms), and hand reaction time (average response time in milliseconds over 10 trials).
General descriptive statistics were used for participant demographics and each of the Senaptec Sensory Station outcomes. Descriptive statistics for helmet versus unhelmeted conditions are presented overall and by sport. Dependent samples t tests were run for normally distributed variables (multiple object tracking, go/no go tasks, and hand reaction time) to determine whether there was an overall effect of helmet condition on visual performance outcomes, whereas Wilcoxon signed-rank tests were performed for non-normally distributed variables (visual clarity, contrast sensitivity, depth perception, near-far quickness, target capture, perception span, and eye-hand coordination). The mean and standard deviation of the differences scores between conditions were used to calculate effect sizes (Cohen's d) for all outcomes. Data were analyzed using SAS software (version 9.3; SAS, Inc., Cary, NC) and an a priori alpha level of 0.05 was used.
Demographic information for the participants is reported in Table 1. All 24 participants completed the full Senaptec Sensory Station battery for both conditions. Overall mean differences and effect sizes for the helmeted versus unhelmeted conditions are presented in Table 2. Participants performed significantly worse on eye-hand coordination and go/no go tasks during the helmeted condition compared to the unhelmeted condition. For eye-hand coordination, participants responded to each target slower when they were wearing a helmet (mean difference = 38.57 ± 53.01 ms; 95% confidence interval [CI]: 16.19 to 60.95; Cohen's d = 1.03; P < .001). Additionally, eye-hand coordination response times were significantly different to peripheral, but not central, targets under helmeted conditions. Participants responded slower to peripheral (mean difference = 45.55 ± 60.31 ms; 95% CI: 20.09 to 71.02; Cohen's d = 1.07, P < .0001) targets when wearing a sport helmet. For the go/no go assessment, participants scored fewer points when wearing a helmet (mean difference = −3.54 ± 3.79 points; 95% CI: 1.94 to 5.14; Cohen's d = 1.32; P = .001). There were no significant effects of helmet (P > .05) on performance for visual clarity, contrast sensitivity, depth perception, near-far quickness, target capture, perception span, multiple object tracking, or hand reaction time.
Demographic Information for Study Sample
Visual Performance by Helmet Condition Between Helmet Conditions
This study explores the ways in which sport helmets may impact vision performance. We found that eye-hand coordination and go/no go performance were impacted by wearing a helmet. For the eye-hand coordination assessment, the degradation in performance associated with the helmeted condition was driven by slowed responses to peripheral targets. This may be due to the bars of the facemask and enclosure of the helmet, which hinder the ability to see and respond to visual targets in the periphery. This agrees with the participants' self-reported task assessment, stating that they had difficulty seeing the stimuli in the corners of the screen. The data output provided by the Senaptec Sensory Station did not allow for the same analyses of central and peripheral stimuli for the go/no go assessment. However, given the similar task design to the eye-hand coordination, it is likely our observed peripheral results would have been consistent across these two tasks. Previous research shows that motorcycle and cricket helmets degrade visual performance,12,14 with motorcycle helmets notably affecting lateral vision.12 The current study takes this a step further by demonstrating vision is limited by wearing football, lacrosse, and ice hockey helmets, which has been linked to more severe head impacts4 and musculoskeletal injury risk.3
Our data suggest that hockey helmets are largely driving main effects of worse eye-hand coordination during the helmet on condition. Additionally, hockey helmets appear to negatively affect performance on hand reaction time. This evidence may suggest that hockey helmets are affecting visual performance differently, which could be due to the unique design of the cage worn on hockey helmets. In line with this, the literature suggests that having a distractor before one's eyes can negatively impact visual saccades.18 The cage of a hockey helmet has more protective bars covering the face and eyes than does the facemask of a lacrosse or football helmet (Figure 1). Although neither of these tasks are saccadic assessments, it is possible that the bars of the hockey facemask acted as distractors and affected other aspects of visual performance. It would also be valuable to assess the effect of helmets on tasks with saccadic demand, such as tracking a single object, because this could also have great implications in sports. Conclusions drawn from these observations are limited because the sample of hockey players tested was small (n = 5). Notwithstanding, our data may indicate the effects of hockey facemasks are more pronounced than football and lacrosse helmets; this warrants further exploration in future studies.
In general, there were some overarching limitations to our study. The most prominent limitations are the small sample size and uneven distribution across sports. With only 24 participants, particularly the 5 participants in each of the lacrosse and hockey groups, we are limited in the definitive conclusions that can be drawn, particularly with respect to the subgroups. Nonetheless, we observed several significant main effects of helmets and the effect sizes we observed were large. The use of multiple helmet types is both a strength and limitation of our study; the presence of differences between helmet designs likely led to higher variability in our results, but provided higher external validity when considering the numerous helmet styles used in sport. Future studies should employ a larger sample size and more even distribution across sports to investigate the nuanced effect of specific sport helmets on vision performance. Additionally, there were varying levels of experience and a range in the number of years since the participants last played the sport in our sample. Although all participants played in high school, some were still competing in that sport at a collegiate club or varsity level, whereas others had not played since high school several years ago, which could have influenced the results. Our study included adult men to try to limit the variability that may be caused by sex and age; however, our findings may not be generalizable to other samples. Further research should include investigating the impact of helmets on vision performance in youth athletes, female athletes, and other populations, as well as further investigating how these specific deficits translate to safety and performance.
Implications for Clinical Practice
Beyond these limitations, the real-world implications of these findings are significant. The apparent detriments to visual performance caused by helmets are concerning and should be considered moving forward; they could affect not only athlete performance, but also safety if the athletes are unable to properly anticipate incoming hits. Simple hand reaction times are predictive of the time it takes for athletes to initiate a protective response.19 In other words, the additional time it takes an individual to respond to a target when wearing a helmet translates to a slower response to an incoming threat, such as a ball coming toward the head or an impending hit, which can reduce an athlete's ability to avoid or brace for an impact. The translation between reaction time and anticipation is direct, because they are regulated by one continuous neural system.20 Additionally, there is strong evidence that poor anticipation is linked with an increased risk of injury, with respect to both concussion and lower extremities.3,5,6,21 Similarly, the deficit caused by helmets on the go/no go assessment reflects an athlete's ability to interpret and respond accordingly to a stimulus. This is a significant reduction in performance compared to without a helmet, and could be translated as a reduced ability to see an incoming opponent and subsequently decide whether to continue as before or stop to brace for an impact, thereby increasing potential for injury.
The findings of our study indicate that helmets are producing significant visual deficits that may affect athlete safety and performance. Wearing a helmet resulted in eye-hand coordination and go/no go deficits, both of which have been linked to incurring more severe head impacts.4 Although the purpose of helmets is to reduce injury risks, detriments to visual performance should be considered. This could also have implications for other populations that wear helmets to protect themselves in dangerous environments, such as construction workers and military personnel. Additionally, these effects should be noted when considering implementation of helmet use in female sports, such as women's lacrosse.
- Hernandez F, Shull PB, Camarillo DB. Evaluation of a laboratory model of human head impact biomechanics. J Biomech. 2015;48(12):3469–3477. doi:10.1016/j.jbiomech.2015.05.034 [CrossRef]
- Poltavski D, Biberdorf D. The role of visual perception measures used in sports vision programmes in predicting actual game performance in Division I collegiate hockey players. J Sports Sci. 2015;33(6):597–608. doi:10.1080/02640414.2014.951952 [CrossRef]
- Wilkerson GB, Simpson KA, Clark RA. Assessment and training of visuomotor reaction time for football injury prevention. J Sport Rehabil. 2017;26(1):26–34. doi:10.1123/jsr.2015-0068 [CrossRef]
- Harpham JA. A Study of Visual and Sensory Performance, Collision Anticipation, and Head Impact Biomechanics in College Football Players [dissertation]. The University of North Carolina at Chapel Hill; 2013. ProQuest Dissertations Publishing 1538112 https://search.proquest.com/openview/26b4b1bf4718e755cdabe439bfe727c5/1?pq-origsite=gscholar&cbl=18750&diss=y. Accessed July 19, 2019.
- Mihalik JP, Blackburn JT, Greenwald RM, Cantu RC, Marshall SW, Guskiewicz KM. Collision type and player anticipation affect head impact severity among youth ice hockey players. Pediatrics. 2010;125(6):e1394–e1401. doi:10.1542/peds.2009-2849 [CrossRef]
- Lincoln AE, Caswell SV, Almquist JL, Dunn RE, Hinton RY. Video incident analysis of concussions in boys' high school lacrosse. Am J Sports Med. 2013;41(4):756–761. doi:10.1177/0363546513476265 [CrossRef]
- Gessel LM, Fields SK, Collins CL, Dick RW, Comstock RD. Concussions among United States high school and collegiate athletes. J Athl Train. 2007;42(4):495–503. http://www.ncbi.nlm.nih.gov/pubmed/18174937. Accessed July 19, 2019.
- Hootman JM, Dick R, Agel J. Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives. J Athl Train. 2007;42(2):311–319. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1941297/. Accessed July 19, 2019.
- Lincoln AE, Caswell SV, Almquist JL, Dunn RE, Norris JB, Hinton RY. Trends in concussion incidence in high school sports: a prospective 11-year study. Am J Sports Med. 2011;39(5):958–963. doi:10.1177/0363546510392326 [CrossRef]
- Zuckerman SL, Kerr ZY, Yengo-Kahn A, Wasserman E, Covassin T, Solomon GS. Epidemiology of sports-related concussion in NCAA athletes from 2009–2010 to 2013–2014: incidence, recurrence, and mechanisms. Am J Sports Med. 2015;43(11):2654–2662. doi:10.1177/0363546515599634 [CrossRef]
- McCrory P, Meeuwisse W, Dvorák J, et al. Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med. 2017;51(11):838–847. doi:10.1136/bjsports-2017-097699 [CrossRef]
- McKnight AJ, McKnight AS. The effects of motorcycle helmets upon seeing and hearing. Accid Anal Prev. 1995;27(4):493–501. doi:10.1016/0001-4575(95)00008-N [CrossRef]
- Ruedl G, Herzog S, Schöpf S, et al. Do ski helmets affect reaction time to peripheral stimuli?Wilderness Environ Med. 2011;22(2):148–150. doi:10.1016/j.wem.2010.12.010 [CrossRef]
- Wilkins L, Mann D, Dain S, Hayward T, Allen P. Out with the old, in with the new: how changes in cricket helmet regulations affect the vision of batters. J Sports Sci. 2019;37(1):13–19. doi:10.1080/02640414.2018.1479944 [CrossRef]
- Erickson GB, Citek K, Cove M, et al. Reliability of a computer-based system for measuring visual performance skills. Optometry. 2011;82(9):528–542. doi:10.1016/j.optm.2011.01.012 [CrossRef]
- Gilrein TE. Reliable Change Indices of Visual and Sensory Performance Measures [dissertation]. The University of North Carolina at Chapel Hill; 2014. ProQuest Dissertations Publishing 1557110. https://search.proquest.com/openview/bc7b824af35caa84e01546606063a78f/1?pq-origsite=gscholar&cbl=18750&diss=y. Accessed October 1, 2019.
- Wang L, Krasich K, Bel-Bahar T, Hughes L, Mitroff SR, Appelbaum LG. Mapping the structure of perceptual and visual-motor abilities in healthy young adults. Acta Psychol (Amst). 2015;157:74–84. doi:10.1016/j.actpsy.2015.02.005 [CrossRef]
- Walker R, Deubel H, Schneider WX, Findlay JM. Effect of remote distractors on saccade programming: evidence for an extended fixation zone. J Neurophysiol. 1997;78(2):1108–1119. doi:10.1152/jn.19188.8.131.528 [CrossRef]
- Eckner JT, Lipps DB, Kim H, Richardson JK, Ashton-Miller JA. Can a clinical test of reaction time predict a functional head-protective response?Med Sci Sports Exerc. 2011;43(3):382–387. doi:10.1249/MSS.0b013e3181f1cc51 [CrossRef]
- Engström DA, Kelso JAS, Holroyd T. Reaction-anticipation transitions in human perception-action patterns. Hum Mov Sci. 1996;15(6):809–832. doi:10.1016/S0167-9457(96)00031-0 [CrossRef]
- Borotikar BS, Newcomer R, Koppes R, McLean SG. Combined effects of fatigue and decision making on female lower limb landing postures: central and peripheral contributions to ACL injury risk. Clin Biomech (Bristol, Avon). 2008;23(1):81–92. doi:10.1016/j.clinbiomech.2007.08.008 [CrossRef]
Demographic Information for Study Sample
|Parameter||Football (n = 14)||Lacrosse (n = 5)||Ice Hockey (n = 5)||Overall (N = 24)|
|Years of experience||5.6||2.76||4.8||2.39||16||5.66||7.46||5.56|
|Years since competitive play||3.5||1.71||3.6||1.34||2.4||2.3||3.3||1.77|
Visual Performance by Helmet Condition Between Helmet Conditions
|Senaptec Sensory Station Outcome||Mean Differencea (95% Confidence Interval)||Effect Sizeb|
|Visual clarity (logMAR)||−0.02 (−0.10 to 0.05)||0.19|
|Contrast sensitivity (log CS)||0.07 (−0.04 to 0.17)||0.37|
|Depth perception (arcsec)||2.92 (−34.29 to 40.12)||0.05|
|Near-far quickness (no. of targets complete)||2.63 (−1.90 to 7.15)||0.35|
|Target capture (ms)||13.54 (−19.10 to 46.19)||0.25|
|Perception span (no. of correct dots)||1.83 (−3.75 to 7.41)||0.20|
|Multiple object tracking (tracking capacity)||−0.005 (−0.36 to 0.35)||0.01|
|Eye-hand coordination (ms)c||−38.57 (−60.95 to −16.19)||1.03|
|Go/no go (score of greens hit – reds hit)c||−3.54 (−5.14 to −1.94)||1.32|
|Hand reaction time (ms)||−3.25 (−12.14 to 5.64)||0.22|
Description of Senaptec Sensory Station Assessments
|Visual Clarity||Determine how well subject can see details (i.e., static acuity)||logMARa (value of 0 is equivalent to 20/20 vision) for both eyes|
|Contrast Sensitivity||Determine how well subject can detect differences in contrast||Log CS = −log1/CS for 18 cycles/degree frequency|
|Depth Perception||Determine how well subject can judge distance using both eyes (stereoacuity)||Threshold in arcsec|
|Near-Far Quickness||Determines how quickly subject can switch focus between a near and far target||Cycles completed in 30 seconds, average return times (ms)|
|Target Capture||Determines how well subject can shift gaze to recognize a peripheral target (measures dynamic visual acuity)||Threshold response time (ms)|
|Perception Span||Determine speed and scope of subjects' visual recognition accuracy (ability to recreate pattern of dots)||Number of dots correctly identified|
|Multiple Object Tracking||Determine ability of subject to divide attention by tracking multiple objects at once||Tracking capacity|
|Eye-Hand Coordination||Determine how quickly and accurately subject can respond to a changing target||Average time to hit each dot (ms)|
|Go/No Go||Determine how quickly and accurately subject can make a decision and respond to a changing target||Correct greens – incorrect reds (with 25% credit given to “late” greens)|
|Hand Reaction Time||Determine how quickly subject's hand can react to a visual stimulus||Average reaction time (time to remove hand after stimulus) in ms|