Physiological and Biomechanical Monitoring in American Football Players: History
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Antonio Nocera
,
Agnese Sbrollini
,
Sofia Romagnoli
,
Micaela Morettini
,
Ennio Gambi
,
Laura Burattini

American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. 

  • American football
  • wearable sensor
  • portable sensor
  • Sport Medicine
  • Sport Biomechanics
  • Biomedical Engineering
  • Injury Prevention

1. Introduction

American football is a sport of wide interest due to the high number of athletes involved, from youth to professionals. It is the most popular sport in the United States, with over 70,000 collegiate athletes in the last five years [1]. There is an increasing interest in American football outside of its country of origin. Indeed, there are examples in Europe, with 17 teams playing in a professional league (European League of Football) [2], in China, with 33 teams (China National Football League) [3], and in Japan, with 20 teams playing between the first and second divisions of American football (X League) [4].

This sport presents high rates of injuries [5], with a study reporting 5.5 injuries per 1000 for practice and 37.2 injuries per game for competition [5][6]. One of the typical injuries reported in American football is a concussion, with a rate of occurrence of 2.9 and 0.43 injuries per 1000 in games and practice, respectively [6]. A concussion is a form of mild traumatic brain injury, which is defined as a head trauma that impairs brain functions for a limited duration and severity [7]. Subjects with mild traumatic brain injury can experience a wide variety of symptoms, such as headache, dizziness, confusion, loss of consciousness, and amnesia [8][9]. American football athletes exposed to different brain traumas during their careers may experience mood and behavioral alterations and small cognitive impairments [10]. For these reasons, concussions are a controversial topic with significant research regarding their monitoring and prevention.

Considering the many issues related to American football, biomedical engineering may be helpful in supporting the monitoring of athletes, evaluating used equipment, and leading industrial research on sports. Heart rate (HR) monitoring could be a non-invasive and simple solution to screen the internal loads of players in non-invasive and simple ways [11], aiming at preventing situations of overtraining, classifying training phases, or describing the cardiovascular fitness of the athletes [12] or describing the cardiovascular fitness of the athletes [13]. In recent years, research regarding heart rate monitoring has made significant advancements, and contactless alternatives have gained attention thanks to the opportunity to remove cumbersome wearables [14][15]. On the other hand, external loads are monitored through global positioning systems (GPS) and inertial sensors, which integrate accelerometers and gyroscopes and aim at preventing injury and enhancing performance. Recently, new wearable technologies that integrate GPS and heart rate sensors have been proposed for contact sports to prevent excessive training loads, which can cause severe fatigue and stress in athletes [16]. 

2. Physiological and Biomechanical Monitoring in American Football Players

2.1. Biomechanics of Concussion

A concussion is a severe injury with long-term outcomes, and the analysis of the biomechanics of concussions aims to limit, prevent, and understand the biomechanical causes behind their occurrence. With the objective of studying the biomechanics of concussions, the main research sub-categories include laboratory reconstruction (LAB) [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], monitoring with the head impact telemetry system (HIT) [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], wearable-sensor monitoring (WSM) [51][52][53][54][55][56][57][58][59], and computer modeling (CM) [60][61][62][63][64][65][66][67][68][69][70][71].

2.1.1. Laboratory Reconstruction

Studies on LAB aimed to compute the severity of concussive and sub-concussive head impacts [17][28][29][30], evaluate the performances, or develop new helmet technologies [18][19][20][21][24][25][29][31], to develop new procedures, tools, and metrics for better simulations and reconstructions [22][23][26][27].
The first experimental procedure on the biomechanics of concussion in professional American football athletes were based on video recordings of concussive or non-concussive impacts. These scenarios were then recreated in a laboratory using Hybrid III anthropomorphic test devices. The typical laboratory configuration was based on video analysis, which aimed to retrieve the initial kinematic description, including the impact location and velocity. The impact was then reconstructed using anthropomorphic test devices, which were instrumented with 9 accelerometers in a 3-2-2-2 configuration, capable of acquiring both linear and rotational accelerations.
Generally, the estimated errors ranged between 7% and 16% for linear acceleration and between 4% and 25% for rotational acceleration [17]. For this reason, Bailey et al. [23] proposed an optimized video analysis technique called videogrammetry, considering cameras with high frame rates. Videogrammetry was later employed in the reconstruction of helmet-to-helmet concussions [28] and helmet-to-ground concussions [29].

2.1.2. Monitoring with Head Impact Telemetry System

In order to reduce the main issues related to laboratory reconstruction (LAB) (such as time-consumption and a limited number of trials), wearable sensors were exploited (to collect large amounts of data in vivo during training and games). HIT is the most commonly used technology that is fit inside helmets; it is composed of spring-mounted accelerometers and telemetry hardware.
Studies on HIT collected data about the magnitude [33][34][35][36][37][38][39][41][42][43][44][45][46][47][49][55], frequency [40][41][43][47][48][49][50][52] and impact locations [35][44][46][49] of head impacts.
The magnitude of most experienced head impacts by players was skewed toward low severities, with a median peak linear acceleration of 20.5 g and a median peak rotational acceleration of 1400 rad/s2
[41].
For mild traumatic brain injury cases, the largest cohort using HIT with 51 cases reported a median peak linear acceleration of 66.7 g and a median peak rotational acceleration of 2963 rad/s2
, whereas the largest cohort of LAB reported a mean peak linear acceleration of 72.2 g for concussions with loss of consciousness and 46.8 g for concussions without loss of consciousness. Moreover, the median peak rotational accelerations are 5247 rad/s2 concussions with loss of consciousness and 3457 rad/s2
for concussions without loss of consciousness. Concerning the frequency of impacts, in the cohort studied by Crisco et al. [40], the median number of head impacts per season experienced by a single player ranged from 257 to 438, and the maximum number of impacts ranged from 1022 to 1444 across 3 enrolled teams. The median impacts per game varied from 12.1 to 16.3, and the median impacts per practice varied from 4.8 to 6.6. In the largest reported cohort recently studied by Mccrea et al. [50], the median number of head impacts experienced by a player in a given season was 415 (interquartile range of 190–727), with the majority occurring during practices instead of games.
Regarding impact location, front impacts seem to be the most frequent [39][40] and are related to the highest peak rotational acceleration [44] and the lowest Gadd severity index [39]. Impacts to the top of the head are occurring at lower percentages and are related to higher peak linear acceleration [37][41][44] and the Gadd severity index [39].

2.1.3. Wearable-Sensor Monitoring

Alternative wearable sensors are instrumented mouthguard sensors [51][53][54][55][56][58][59] and skin path sensors [57]. Regarding instrumented mouthguards, four papers dealt with the validation and development of instrumented mouthguard sensors [51][56][58][59], whereas the remaining papers from 2017 to 2019 [53][54][55] used mouthguard sensors to study and understand the relationship between the biomechanics of sub-concussive head impacts and variations in blood biomarkers. Two blood biomarkers, s100-beta [53] and neurofilament ligaments [55], were found to be related to head impact severity. A skin patch sensor was used in vivo to collect the biomechanical data about head impacts and to characterize different events, such as blocking, being blocked, tackling, being tackled, and ground contact [57].

2.1.4. Computer Modeling

LAB, HIT, and WSM are useful for providing descriptions of the kinematics of head impacts, but they cannot be used to extract measures of the consequences of impacts on the brain. Therefore, the consequences of concussion are usually evaluated by CM.
Studies on CM aimed to create new test protocols and metrics to screen the safety of equipment [60][61][65][70], develop and validate new tools and technologies [59][64][66][67], study the interaction between neck muscle activation and head impacts [63][68], and study the strain on the brain during practices and games [71].
From 2013 to 2018, three articles [60][61][65] developed and validated a testing protocol involving a set of centric and non-centric impact locations justified by the different effects of the test configurations on the brain deformation metrics extracted by finite element modeling of the human brain. Finite element modeling applied to the brain was the most diffused modeling technique and it was also employed to compare concussive events with different clinical outcomes (loss of consciousness against non-loss of consciousness) [69].
Nevertheless, in recent years, finite element modeling research has expanded to equipment [66][67] and anthropomorphic devices [64], with good averages of similarities between the models, real-life helmets, and anthropomorphic devices. Additionally, computer modeling has been applied to models of neck muscle fibers and the skull during severe head impacts to understand the effect of muscle activation latency, muscle strength, and posture of the head-on injury metrics. Neck muscle strength does not seem to significantly affect head injury metrics [63][68], whereas early activation of neck muscles, representing an awareness of the impact [63], and a proper head posture [68], significantly decrease the injury metrics.

2.2. Biomechanics of Foot-Wearing

Research on the footwear worn by American football players focuses on improving the design of both the shoe and the football field in order to reduce injury rates. Metatarsophalangeal joint sprain, also known as turf toe, is an injury mainly caused by hyperextension of the joint [72]. Therefore, footwear plays an important role in limiting the torque forces applied to this joint. Consequently, a line of research focuses on the quantification of forefoot bending stiffness (FBS) with the aim of understanding if the footwear is protective against metatarsophalangeal injuries [73][74][75]. On the other hand, a second line of research regards field–footwear interactions (FFI), taking into consideration the evidence of increased lower extremity injuries in artificial grass when compared to natural turf [76].

2.2.1. Field–Footwear Interactions

The typical mechanism employed to simulate FFI is the Biocore Elite Athlete Shoe Turf [77][78][79], which is a machine that simulates a cleat moving on the turf. In 2015, a study by Kent et al. [77] quantified the differences in the mechanical interactions between artificial and natural surfaces using a cleated shoe used in both fields. The same methodological procedure was applied by Kent et al. [78], who employed 19 different kinds of cleated shoes on artificial and natural grass. In 2021, a study by Kent et al. [79] focused on the description of the mechanical response of the natural grass to the interaction with the cleated shoe. Only natural grass surfaces have inherent force-limiting qualities, which could explain the lower rates of injuries in these turf types [76]. On the contrary, on artificial turf, the footwear choice seems to be the most relevant characteristic to limit the occurrence of injuries.

2.2.2. Footwear Bending Stiffness

Crandall et al. [73] and Lessley et al. [74] conducted dynamic testing to measure the torque and stiffness of various cleats, reporting range values of peak torques and peak stiffness relative to the flexion angle. Moreover, Crandall et al. [73] reported a high linear correlation (0.91) between shoe stiffness and peak torque. In a more recent study by Wannop et al. [75], ten American football players were enrolled to investigate the effect of three types of footwear with increasing bending stiffness while performing sport-specific movements. The forefoot bending stiffness of American football shoes does not produce enough torque to counteract the torques experienced by professional athletes [80] and, thus, the footwear is not protective against metatarsophalangeal hyperextension.

2.3. Biomechanics of Sport-Related Movements

The biomechanics of sports-related movement (SM) employs a motion capture system comprising motion cameras and retroreflective markers to describe the kinematics and kinetics of the anatomical segments involved in specific sports actions. The common objective of these studies is to aid in the rehabilitation of athletes by providing medical staff with expected kinematics and kinetics in healthy individuals or to understand the mechanisms behind injuries.
Rash and Shapiro [81] studied the biomechanics of throwing in twelve quarterbacks in their senior year of college. The throwing motion was analyzed using motion cameras and manual digitization of the frames. Riley et al. [82] published a motion analysis of the foot kinematics of nine American football players while they were performing three typical combined drills. The researchers used eight reflective markers on each foot and employed force plates to complete the description of the ground reaction forces. 
Among all player roles, linemen are at high risk of articular cartilage injuries [83][84], which motivates research on typical linemen drills. Lambach et al. [85] reported knee joint loading for fifteen linemen performing blocking drills and did not report any significant difference in knee compressive forces and moments between unloaded blocking drills and jogging or walking. They concluded that the blocking motion itself is not responsible for the elevated cartilage injury risk encountered in linemen. 

2.4. Aerodynamics of the Football and Catch

The aerodynamics of football and catch (AFC) aims at improving the performance of players by understanding what are the variables that can have an important effect of the trajectory of the football. The possible implications to performance improvement drives the interest of sports engineers; many studies have described the dynamics of rotating balls to better comprehend how the rotation of the ball can be modeled [86].
In 2016, Guzman, Brownell, and Kommer [87] studied and quantified the drag and lift coefficient of the football while rotating around its short axis in a wind channel. In 2018, Pfeifer et al. [88] simulated a kick with a machine to understand the optimal impact point and impact angle to maximize the distance. The researchers found that kicking the ball at 5.5 cm from the ground would yield the maximum distance with small insignificant variations, depending on the impact angle. Striking the ball at lower heights would instead produce higher launch angles and decrease the range.

2.5. Injury Prediction

The majority of the injuries experienced by football players are a consequence of the collisions occurring due to tackles and blocks, accounting for half of the total injuries in recent epidemiological investigations [6]. However, the occurrence of injuries without contact with other players represents the second most common mechanism [6] and the one that could be limited or avoided with prevention actions. Knees, shoulders, and ankles are the joints where athletes most often experience injuries, and this is why most of the research about injury prevention focuses on lower extremity injuries or shoulder injuries. Injury prediction (IP), which consists of creating mathematical models to recognize subjects at an elevated risk of injury and to identify possible predictors of future injuries during the season, is essential for injury prevention.
Laudner [89] measured the level of shoulder instability in a cohort of 45 NCAA American football athletes compared to a control of 70 age-matched active people employing a force place to compute the radial area deviation of the center of pressure of an arm during a one-arm plank exercise. The authors showed a decreased sensorimotor control of the shoulder for the football cohort probably due to repetitive stress on the shoulder joint experienced by players due to tackling. 
A threshold of 21 touches in the closed-chain upper extremity stability test during the preseason achieved a sensitivity of 0.79 (95% confidence interval: 0.57–0.91) and a specificity of 0.83 (95% confidence interval: 0.44–0.97) in predicting shoulder injuries during the season [90].
Concerning core–lower extremities injuries, which represent the highest percentage of injuries occurring in American football [5][6], Wilkerson et al. [91] proposed a logistic regression 4-factor model fitted on data from 84 players in 2012, then refined it to a 3-factor model in 2015 [92] due to the greater number of players involved (n = 145 over 3 seasons). The subjects completed questionnaires during the preseason, including the Oswestry Disability Index, International Knee Documentation Committee, and components of the Foot and Ankle Ability Measure. Moreover, the scholars administered tests to assess core endurance and aerobic capacity, monitoring recovery with the help of a HR sensor. Three factors were found to be the most discriminant for high injury risk established in preseason: the number of starting games higher than one, an Oswestry Disability Index score higher than four, and a wall-sit hold for less than 88 s. The presence of at least two of these three factors during the preseason corresponded to a sensitivity of 56% and a specificity of 80% [92]. The prediction of lower extremity injuries was also tackled with the lower quarter Y-balance test [93][94], consisting of the player standing on one leg and reaching (with the contralateral leg) the anterior and posterior directions, forming a Y shape.

2.6. Heat Monitoring of Physiological Parameters

Exertional heat illness can occur with various symptoms and severities, and it is possible to distinguish [95] between heat syncope (which happens to unfit or unacclimatized people in hot environments when standing for long periods of time or when rapidly changing posture), heat exhaustion (which represents an early cessation of exercise in hot environments due to multiple factors including cardiovascular strain, low blood pressure, and fatigue), exertional heat injury, and exertional heat stroke. Exertional heat illness is common in American football, with an average incidence rate of 1.31 per 1000 athlete exposures, ranging from 0.06 to 4.19 per 1000 athlete exposures across 7 studies analyzed in a recent systematic review [96]. Moreover, the same review reported American football as the field sport with the highest exertional heat illness incidence among other similar team sports. Heat monitoring (HM) aims to understand the contribution of the American football uniform to the exertional heat illness problem.
The research regarding heat monitoring started in 2006 [97] with the first studies on the heat response of athletes during preseason practices in the heat. From 2007 to 2010, three papers [98][99][100] simulated practice conditions and monitored the temperature of players with higher BMIs (linemen) because they were found to experience higher increments of temperature [97]. 

2.7. Monitoring of the Training Load

Monitoring training loads (TLs) aims to improve athlete performance and reduce the risk of injuries. The use of wearable sensors provides a simple and effective way to track the workload experienced by athletes. Coaches can exploit the data extracted from wearables to tailor strength and conditioning programs to meet the specific needs of each player. Additionally, coaches can analyze the recorded data from practice and games to group athletes with similar needs. Flatt et al. [101] reported on the daily fluctuations of HRV experienced by a concussed player. Monitoring internal load is crucial to understand whether players have the appropriate recovery time. It has been shown that linemen experience a decrease in vagal tone throughout the season [101][102][103], and they do not fully recover baseline vagal activity on consecutive training days [104]. This parasympathetic impairment observed in linemen is due to multiple contributing factors, such as a progressive increase in physiological stress as the season progresses, a high frequency of soft tissue traumas, and a high frequency of sub-concussive impacts [103]. Monitoring HRV could provide coaches with the necessary information to better plan practices throughout the week, ensuring adequate recovery time between them.

3. Summary

Collecting information through wearable sensors, which is already widespread in concussion monitoring, is lacking in the field of footwear applications and the biomechanics of sports motion. One possible drawback of the analyzed literature is that all the results regarding the mechanical interactions between the footwear and playing field were reported in simulated environments with a machine replicating the force and weight of an athlete. Wearables could solve this issue, and foot-mounted or shoe-mounted wearable devices are already available to provide valuable information regarding running gait mechanics [105][106]. Therefore, these sensors could also be used for sprinting, changing directions, blocking actions, and other sport-specific motion analyses. Moreover, the use of retroreflective markers in conjunction with motion cameras has been the major technique used to evaluate the biomechanics of sport-specific movements. Thus, contactless methods based on video analysis or radar analysis[177,178] could be promising to extract important information without the need for wearing sensors. Future industrial research in the field of footwear should focus on improving the innovation of artificial turf design, with the aim of reproducing the mechanical qualities of natural grass [79], and the design of shoes, with the aim of finding a trade-off between performance, comfort, and prevention [73][74][75]. Despite many studies evaluating the design of helmets, no studies dealt with the evaluation of new designs of shoulder pads; only one study proposed an integrated solution of helmet and shoulder pads [24]. Shoulder pads protect against shoulder, neck, and chest injuries; therefore, it could be interesting to expand research to the biomechanics of other equipment.

This entry is adapted from the peer-reviewed paper 10.3390/s23073538

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