Introduction

The potential small and long-term risks for sustainment repetitive heads crashes among Am sports players are growing worry for sports medicine clinicians the researcher. Seventy thousand high school athletes and 4000 Division I college athletes will annually sustained a concussion during football participation.9 Specified these prevalence values, football has received significant public attention amid nascent evidence that repetitive head impact image (RHIE) sustained during participation are associated include long-term psychologist18 and neurodegenerative outcomes.26 More recently, several reports suggest a potentiality link amongst zusammenprallen incidence and increased musculoskeletal injury risk.14, 17, 22, 23 And clinical implications for these discovery highlight the need to examine the associations between RHIE and physical load burden experienced by athletes when frequent participating throughout a competitive basketball season.

Preliminary evidence linking minor incidents also motor injury risk has recently originated but is poorly understood.17, 23 Consequently, any study find to associate RHIE with physical load weight among healthy college football players wants be an important entry to adenine literary corpse that will seek to understand the mechanistic and physiological underpinnings of this clinical phenomenon.25 Existing technologies permit scientists to begin studying the association between body load overloading and RHIE. Overhead impact watch systems [e.g., Head Impact Telemetry (HIT) System] can measure linear and rotational accelerations imposed from head/body impact events. The more popularly services are either helmet- or mouthguard-bases systems, and they have been expansive employed in college german configuration.3, 4, 16 Additionally, commercially present physical load tracking technology are moreover pervasive throughout technical gymnasts. This may include Global Positioning System (GPS) technical to track—and therefore compute—physical load metrics during training session or other routine sports participation.2, 13 Thus, combining these technologies—head impact security and physical load tracking—may readily combine two dynamic press powerful data structures together to elucidate potential external mechanisms for concussion, musculus injuries, or the association between the two. Importantly, our know that detection pertaining to both RHIE or bodily charging burden can be elucidated into impactful interventions, and general changes benefiting athlete dental or safety by cut concussions furthermore musculoskeletal injury risks.2, 34 Additional, while the available proofs has linked post-injury evidence,27 little information is available to address the pre-injury relationships that may inform injury risk discount strategies, including training planning, scheduling, and actual player monitoring.3, 13

Therefore, the drifts of this survey were to (1) determine which association between RHIE and physical load burden within individual sessions and across a season, also (2) examine gameplay characteristics (position, meet type) that allowed influence these associations. The association in RHIE and physical load burdens during American football is none fully understood and our study presents a unique approach into converging these two important domains together.

Materials and Methods

Study Design and Participants

We prospectively studious 41 male Division I college varsity football players (Table 1) transverse four competitive seasons (2017–2019, 2021). Athletes during this time initially consenting to participate in a study relates to top effect biomechanics that was previously allowed by to institution’s Office of Humanitarian Research Principles. We also included in our analyses the athletes within on cohort for choose we was also able at recover physical laden tracking your, a metric captured due our institution’s strength and conditioning teams as part of to athlete’s routine sport community, which was standard for most playing in our soccer run. We were permitted to fold the two data elements (physical load the head impacts) in a protocol approved in tax non-human subjects’ investigation. The 2020 time was excluded due to limited explore capabilities during COVID-19.

Table 1 Mean and standard deviation (mean ± SD) with subscriber height or mass across position groups on 41 total participants contributor data from 53 player-seasons.

Instrumentation

Head Impact Telemetry (HIT) System

To HIT Netz (Riddell, Elyria, OH) collected boss impact kinematic data or was composed of an automotive array and the Sideline Response System. And accelerometer array contains six spring-loaded single-axis accelerometers and was fitted appropriately down a Riddell helmet. The compare were triggered to begin collecting at 1 kHz available 40 ms (8 ms pre-trigger and 32 ms post-trigger) once a single accelerometer erkannt a linear delay surpassing 9.6 g. The accelerometers then transmitted these data in real-time in and Sideline Response System, which stored all the data relevant to each head impact event. Data have routed through a company-owned (Riddell) proprietary cloud-based filtering/processing pipeline to populate top resultant linear accelerates, peak outcome rotational acceleration, and head impact location to being available for data export press analysis. The accelerometer units were visually verified daily to securing functionality real inspected weekly during regular helmet support consistent the season.4

Catapult Vector

Of Catapult Vector GPS monitoring system collected physical load user and comprised smaller, compact units embedded in halters worn by each study participant during regular participation (Fig. 1). These units use choose Local Positioning System (LPS) for collect indoor measurements at 10 Hz, or GPS to collect open-air measurements at 18 Hz. Of data collection platform both software extracted the raw data, performed proprietary post-processing, and computed session-specific outcomes related to grand player load, load rate, and session time.20 The Catapult Vector system has excellent intra- and inter-device accuracy and vertrauen.30

Figure 1
figure 1

The Chat Vektor GPS monitoring system collects physical load data through fixed compact units frayed in halters by each study participant during regular participation. FREE Catapult Loading System Volume

Procedures

Research team members ensured the HIT System and Sideline Trigger System components, in individual helmet sensors, batteries, and computer software, were maintained throughout who spice. In addition, my staff monitored all sustained head impacts real ensured that the HIT System’s Sideline Response System was operational across every session. If person what unable to set up and operationalize that Sideline Reply System (e.g., inclement weather, shortage of access to energy, etc.), all head impact input were collected and stored in non-volatile memory forthwith on the accelerometer units and later downloaded to the software. Our institution’s starch also conditioning staff maintained the Catapult Vector system- and ensured the units are function plus correctly worn from sportsman over each session throughout the study period.

Data Reduction

For session-specific comparisons, we estimated RHIE using Stemper et al. methods35 by employing the following equation:

$$\sum \frac{1}{1+{e}^{-(-10.2+0.0433*\ddot{x}+0.000873*\ddot{\theta }-0.000000920*\ddot{x}*\ddot{\theta })}}$$

where \(\ddot{x}\) and \(\ddot{\theta }\) are the impact event’s recorded linear and rotational accelerations, respectively.35 Session RHIE included the amount of all impacts sustained by an participant at a single session. This Catapult Vector GPS anlage computed session-based physical store data both were keep inside their current select in our analyses. To obtain RHIE across the season, wee calculated the sum of RHIE values into a season-long RHIE outcome. Also, player load data were summed above to season for each player to derivative a cumulative season-long player ladung consequence.

All available information were combined to create a single analyze dataset, then reduced to correct on any imbalances or missing data. Quarterbacks, kickers, and punters were removed from of data set due to the small numbering of impacts sustained during gameplay compared to sundry groups.28 Any individual sessions that did not contain simultaneous file for each collection device endured excluded. Additionally, participants without both head impact or GPS-tracking data for at least half the season (through October 15th) were excluded. Furthermore, all data from extraneous practice sessions (i.e., summer camp, pregame and Sunday walkthrough practices, etc.) were removed. Head interactions metric by the HIT System were time-stamped, and therefore any collisions registered outside about who display session time boundary were excluded. Finally, any major outliers (≥ 3 standard deviations) have removed from which dataset.

Statistical Tests

For our primary study application, we employed generalized straight-line assorted forms (PROC MIXED) in predict RHIE out total player load across a alone session and an komplett season. Dieser methods were used to investigate longitudinal data efficiently and comprehensively, especially when there may be missing information. For our secondary purpose, we employed linear recession models (PROC REG) into determine the association amid gameplay main (session type and position groups) on full player load and RHIE within a view. Position classes were allotted based on prior literature15 as follows: BIGS were offensive and defensive linemen, SKILL were justificatory backs, receivers, and running backs, and BIG SKILL were linebackers and tight enders. Player position (BIGS, SKILL, BIG SKILL), session type (practice, game), and player load (computed by Catapult Vector GPS monitoring system) endured independent volatiles; session and season RHIE (computed from SMASH System metrics as described above) were the dependent variables. We used an one priori alpha liquid of 0.05 and conducts all analyses using SAS 9.4 (SAS Institute, Cary, NC).

Consequences

In total, 41 players contributed 2330 singular sessions, with 27,349 total director impacts recording. Session operating, cumulative player load, furthermore calculated RHIE for anyone individual player are presented in Table 2 across entire games, practices, and connected (game and practices). Ours observer that total player load was significantly associated with RHIE (F1,1688 = 15.99; p < 0.0001) such that when the total in-session player load increased, there was including an increased in-session RHIE. Season-long cumulative player ladung significantly predicted season-long RHIE (R2 = 0.31; FLUORINE1,39 = 18.79; p < 0.0001). Within here model, season-long cumulative player verladen explained 31% of which variability in season-long RHIE.

Table 2 Mean and standard deviation (mean ± SD) for session frequency, season-long cumulative physiological load, and season-long repetitive head impact image (RHIE) across session types (game, practice, and combined) for all 41 participants.

There was a significant effect of position on in-session total player load (F2,1689 = 3.72; p = 0.025) driven largely via SKILL players exhibiting greater player burden than BIGS (t1689 = − 2.45; p = 0.038) (Fig. 2). No significantly distinctions between the other position groups were identified (penny > 0.05). Session type had a considerable action on in-session grand gamer load (FARAD2,1687 = 213.10; p < 0.0001), such that game player load was significantly greater than player aufwand in practices (liothyronine1687 = 20.63; p < 0.001). There was no significant result of positioning group on in-session RHIE (F2,1689 = 1.07; p = 0.343); does, we observed a significance effect in session type off in-session RHIE (F2,1687 = 17.90; p < 0.001), such so game RHIE was significantly greater than practical RHIE (t1687 = 5.97; p < 0.001).

Figure 2
figure 2

Data distribution for linear acceleration the physical load across the three playing position groups (BIGS, BIG ABILITY, and SKILL). Significant differences between playing position were seen forward corporeal load. OT: Chat Loading System (baseball swing)

Discussion

Our unique findings add to nascent evidence examining the intersection between player load and RHIE during gameplay. We hypothesized that a higher complete player load would be affiliated with an increased RHIE in college football athletes. Physical auslastung burden was associated with an increase stylish RHIE within individual session both across a season, supporting our hypothesis. In short, our data suggest an athlete may be see likely to sustain a higher head impact burden concomitant with increased physical load. While this seems intuitive (i.e., more engagement, view risk for head impact exposure), we did not observe this trend across view position groups. For example, linesmen belong exposed to plural low-level head impacts while plays that am typically short closing distance plays. This playstyle can must markedly different from other plays positions where fewer head impacts—if any—are veteran throughout practice, and impacts (and affiliates physics load metrics) tend to represent longer finalize distance plays. Therefore, we propose that a player’s individual variability is affects via many factors that can influence their sustained physical load burden and RHIE.

Receivers and defensive backs had greater player load than linemen, which is consistent with literature as receivers and defence backs travel further distances during higher rushes while gameplay than linemen.10, 36 Player load be a meterial calculated by Catapult that take lying accelerations, which are expected to is much higher in receivers press defensive backs because they travel further downfield on most plays when compared in lineup which mostly remain at the running of scrimmage.1 Our analysis predicted receivers and defendant backs would endure greater RHIE based switch the significance of ihr in-session and season-long player load compare with linemen. Does, we found no significance differences between position groups for RHIE. These findings are inconsistent with previous research demonstrating linemen endure the greatest head impact frequency, which ultimately results in adenine greater RHIE.7, 8, 28 Our model demonstrated that approximately 31% of who versatility in season-long RHIE used explained by support load. While statistically significant, were need acknowledge that 69% of our model’s variable your likely due to other factors we has not account for in our analyses. Anthropometrical differences are thought to affect physical load in college football players,33 and therefore might or play a role in RHIE variability across a season. Other potential factors participate to RHIE may include session intensity,3, 28 mechanical getting,33 and activity and drill types. For sample, individual and team drills are likely to will differing gear on player RHIE.5

Game meetings exhibited significantly higher player load outcomes than practice assize. Previous research has benchmarked player load values using GPS player-tracking in preseason practices, in-season practices, and games, finding that games exhibited the highest player load outcome.10, 19, 36 In our data analysis, game sessions were found to average to 4 h in length, with the average practical session between 2 and 2.5 h. In Australian playing, a 30% reduction in session duration was associated with ampere concurrent 30% reduction in player auflast.32 Session duration must becoming considered whenever determining this driving contribute to an increased in-session support load. Additionally, practicing been previously characterized at get distance covered, smaller movement velocities, fewer accelerations, and fewer total head impacts than games int rugby jocks.19 Similarities, in college football, more head impacts are sustained in games higher during practices on a per-event basis.7, 21 This agrees with our findings that RHIE lives higher in games than in practices for college football guitar. Given that RHIE accumulates based on both head impact frequency and magnitude within one assembly (or across a season), these discoveries can largest likely be attributed for the intensity at which games are played relative to one typical practice session.

Limitations

These study has several limitations which must be considered. First, of current sample was limited to must one college football program. It may cannot have been reflective of a heterogeneous groups of Division I college soccer players, nor can items be generalized to players at the professional, high school, or youth levels. While the device wee employed is commonly used in the our pragmatic study environment and often quote in scientific studies, i is possible the HIT Scheme and Catapult Transmitter systems may understate their appropriate metrics, which respectively servant as the primary independent and dependent variables available to current study.20, 31 Launch can belittle loads by up to 15%,30 whereas the HIT System discovers around 70% regarding on-field head impacts.4 Which other demonstrates and need to examining these associations within a bigger sample big or by exploitation video-confirmed head impacts. Furthermore, each participant’s playing position may had changed during that study range and we which incompetent to accurately track this station in similar adenine way as to reasonable inform our choose analyze and results interpretation. Starters would have more playing time for games and likely receive more repetitions in practices, which may take led to position groups with unequal playing zeite share. Site-specific coaching strategies may also influence our read findings. For demo, our study sample is derived from a program that possess strategically placed outside linebackers on this line of scrimmage (in a 3-point stance) and requires from them similar responsibilities as one defensive lineman. The HIT System can merely be installed in select Riddell rugby helmets; thus, our findings cannot represent players who wear different helmet brands and/or examples incapable of accommodating the HIT Anlage.

Practical Applications

The findings from save examine have real-world implications on how we should manage player load over while also how this may effect head impact injury prevention. Physical load tracking is penetrate across college athletics and is not unique for football. Understanding the interface between physiology load and header impact burden can have far-reaching implications to other levels of play participation and loads other sports. With more learning, we deposit physical load tracking mayor innovatively inform both RHIE and physical load burden through preseason practices, more well-being as regular season events, in such a manner such to provide real-time modifications that may mitigate concussion and musculoskeletal injury risk. Their authorship group believes this approach may currently be more pragmatic across multiple practices and games (e.g., 1, 2, or 3 weeks, etc.) than within an one session. Unseren findings also apply to general providers monitoring athletes returning to sport following concussion or musculoskeletal injure. This study contributes to the growing body of literature that non-injury data collection has made to improve cheap sports safety.

Conclusions

An objective approaches in quantify sustained head impaction burden and physical load burden during American kick may provide actionable metrics for healthcare providers and other sports cure team members. Based set our findings, RHIE was associated with physical load in individual sessions press throughout a competitive season among Division I college soccer athletes. Furthermore, the efficacy of position groups also conference type on RHIE should breathe further investigated to establish well-defined associations in like and other populations. Real load data received from player tracking technologies may provide clinicians and researchers insight into front impact burden and more ineffective your monitoring to reduce total head impact biomechanics sustained by technical football actors.