The Complex Interrelationships of the Risk Factors Leading to Hamstring Injury and Implications for Injury Prevention: A Group Model Building Approach

Abstract

There is a gap in the literature regarding the complex interrelationships among hamstring injury (HI) risk factors. System dynamics (SD) modeling is considered an appropriate approach for understanding the complex etiology of HI for effective injury prevention. This study adopted the SD method and developed a causal loop model (CLD) to elucidate the intricate relationships among HI risk factors. This is performed by combining literature evidence and insights from expert stakeholders through a group model building (GMB) approach. The GMB methodology facilitated the identification of sixty-five critical factors influencing the HI risk, revealing the dynamic interplay between factors. Stakeholder engagement underscored the importance of previous injury characteristics (level of influence of previous injury, severity of previous injury, quality and size of scar tissue) and the quality of rehabilitation. HI-CLD revealed that many factors had indirect effects on HI risk. The HI-causal loop model establishes a foundation for a future stock and flow quantitative SD model aiming to advance HI prevention strategies through an interdisciplinary collaborative effort. These findings underscore the complexity of HI prevention, necessitating a holistic approach that integrates the views of diverse professional expertise. Appropriate inter-professional collaboration and continuous athlete screening are important for effective injury prevention strategies.

1. Introduction

Hamstring injuries (HIs) present a significant challenge in field-based team sports [1], with notable consequences for team performance [2] and financial implications [3]. Recent longitudinal analyses, including a comprehensive twenty-year study, have underscored an alarming increase in HI incidence, accounting for up to 24% of total injuries in male professional football athletes [4].

Extensive efforts have been made to decipher the risk factors of HIs. Green et al.’s [5] pivotal systematic review evaluated 179 potential risk factors and conclusively identified only older age and previous injuries as having a significant association with subsequent HIs. Additional factors, such as athletic performance, high-speed running exposure, and biomechanics, have been identified as significant [6,7,8,9,10]. However, the association of commonly examined variables, such as isokinetic strength parameters and flexibility, with HI remains inconsistent [5]. Despite the significant role of numerous factors, these factors appears to indirectly influence HI risk [5,11]. Systematic reviews and meta-analyses [5,12] have proposed a move towards examining nonlinear interactions among risk factors, suggesting a need for a shift in investigative approaches.

Traditional research methodologies often fall short, primarily employing simplistic linear methods that fail to capture the intricate nature of HΙs [5,13,14,15]. Even when multivariate approaches are utilized, reliance on linear regression models has precluded a comprehensive understanding of the complex, nonlinear interdependencies among factors, including biomechanical profiles, workload, and communication quality between medical staff and coaches [5,8,12,16,17,18,19]. This identifies a clear gap in existing research methodologies, underscoring the necessity of holistic system analysis to effectively comprehend and address HIs [20]. Initial forays into applying system thinking, particularly through machine learning algorithms, have begun to address this complexity [21,22,23].

In response, causal loop diagrams (CLDs) and system dynamics (SD) methodologies have emerged as promising approaches in sports injury research [24,25]. Complex systems are composed of interrelated factors that produce recursive loops of interdependence. In CLDs, factors are connected by polarity arrows that specify the interactions among them [26]. CLDs are qualitative approaches that facilitate the conceptualization of system constructs and dynamic interrelations among factors [26], thereby enhancing the communication of system complexities [27]. By engaging experts and stakeholders through a group modelling building approach, researchers have successfully utilized CLDs to resolve complex issues [27,28]. This methodological innovation not only aids policy makers in crafting effective solutions, but also extends its application to a range of health concerns, including obesity and concussion management, as well as broader sports science and injury research [25,26,27,28,29]. The development of a CLD for HIs is a critical step towards the creation of a quantitative SD model, which promises to significantly enhance the prediction and prevention of HIs [26].

Despite the recent push for system thinking in sports injury research [25,30,31], its application to HI etiology remains limited [5,20]. This study aims to develop a conceptual CLD incorporating HI risk factor interactions. The methodical exploration of CLD integrates the literature evidence and stakeholder insights. This endeavor seeks to lay the groundwork for a simulation SD model aimed at enhancing the understanding and management of HIs in field-based team sports.

2. Materials and Methods

2.1. Study Design and Participants

This study initiated a qualitative analysis to construct a Hamstring Injury Causal Loop Diagram (HI-CLD) using a structured group model building (GMB) procedure. The preliminary stage entailed the literature review, anchored by recent systematic reviews and meta-analyses pertinent to hamstring injuries within the field of team sports [5,12,32,33,34,35,36]. However, as the last systematic review by Green et al. [5] searched the literature until the end of 2018, we conducted another search to identify and integrate the findings from recent original studies that were not previously encompassed. The literature search was executed within the PubMed and Scopus databases from 2019 to March 2023 using a combination of keywords (hamstrings AND injury AND risk factors). Studies that prospectively or retrospectively assessed the factors linked to hamstring injuries were examined for the possibility of adding additional risk factors that were not previously documented. This literature review was not systematic, but its purpose was to collect the main HI risk factors documented in the literature in order to be discussed in GMB workshops. As a result, from this literature review, a list of risk factors was created that was further discussed by the members involved in the GMB process.

Then, this study harnessed the GMB methodology [37,38] using five structured workshops that facilitated a collaborative and in-depth exploration of the multifaceted nature of HI causes. A detailed explanation of the GMB workshops is presented in the following subsections and

Table 1. GMB process for the formulation of HI-CLD.

Steps	Task’s Purpose	Time	Script from Scriptapedia	Actions	Participants
A.	Review the main risk factors for acute hamstring injury (HI) from systematic reviews and relevant literature	-	-	Create a list of HI risk factors for further discussion in the group model building workshops.	Two members of the modeling Team
B	Group Model Building Workshops
B1	Discuss list of risk factors for acute HI	120’	Variable elicitation	Participants discuss the variables and remove or add variables.	Modeling team and two members of the stakeholders’ team
B1.1	Create a CLD for acute HI (HI-CLD) based on literature and the discussion during the previous GMB session	-	-	Formulation of an initial perspective of HI risk factors interaction.	One member of the modeling team
B2	Discuss and review the HI-CLD with the modeling team	120’	1. Causal Mapping with Seed Structure, 2. Model Review 3. Next Steps and Closing	Review the HI-CLD and propose corrections.	Modeling team
B3	Review HI-CLD with the modeling team	60’	1. Model Review, 2. Next Steps and Closing	Review the HI-CLD after the corrections and prepare the following steps.	Modeling team
B4	Presentation of CLD to stakeholders and incorporation of their point of view	120’	1 Modeling Project Community Presentation, 2. Model Review	Presentation of HI-CLD to stakeholders. Discussion about the HI-CLD and the included variables.	Modeling team and all members of the Stakeholders’ team
B5	Review the HI-CLD and incorporation of stakeholders’ points of view	90’	Initiating and elaborating a “Causal Loop Diagram” or “Stock and Flow” Model, 2. Model Review	Summarizing the inputs from stakeholders.	Modeling team

Abbreviations: HI—hamstring injury, CLD—causal loop diagram, HI-CLD—hamstring injury causal loop diagram, and GMB—group model building.

. The GMB workshops were guided by specific scripts (structured group exercises) outlined in “Scriptapedia” and “ScriptMap” [39,40], ensuring a rigorous and systematic approach in each GMB workshop. In the preparatory stage of the GMB process planning, the core modeling team reviewed the scripts proposed in Scriptapedia and decided to use the more representative scripts for the purpose of this study after team consensus. Specifically, this study used the scripts of “Variable Elicitation”, “Causal Mapping with Seed Structure”, “Model Review”, “Next Steps and Closing”, “Modeling Project Community Presentation”, and “Initiating and Elaborating a Causal Loop Diagram”. Where gaps in understanding emerged, targeted literature reviews were conducted and laced into the fabric of the GMB process, thus enriching the resultant HI-CLD.

The GMB was pivotal, inviting the participation of a spectrum of experts and stakeholders, whose diverse perspectives were instrumental in the nuanced formulation of the CLD. Specifically, eight participants (two females) were involved in the GMB process, divided into the core modeling team (n = 4) and the stakeholder team (n = 4). The core modeling team primarily conducted GMB workshops, gathered perspectives from stakeholders, and developed the final HI-CLD. The members of the core modeling team participated in all GMB workshops where they shared the specific roles of facilitator, modeler, and recorder, to certify the rigorous execution of the GMB workshops. These roles are described in detail in the following subsections. The core modeling team engaged the stakeholders, whose main role was to discuss the HI-CLD and provide their point of view about the HI problem. The core modeling team was responsible for collecting stakeholders’ perspectives and incorporating them into the HI-CLD.

The core modeling team included an academic physical therapist with expertise in sports injury prevention and rehabilitation, as evidenced by relevant published literature; two clinical physical therapists pursuing their Ph.D. studies in sports injury prevention; and one academic expert in SD modeling. Regarding the stakeholders’ team, the four members who participated in this study resulted from eight possible stakeholders who were initially approached via personal contact, phones, and email to participate in the GMB workshops. The group of stakeholders initially approached was selected for its diverse expertise related to athlete health, including sports physiotherapists, conditioning trainers, orthopedic surgeons, and sports psychologists. The main criterion for including stakeholders in the GMB workshops was to be professionals who work with different aspects of athlete health. Owing to scheduling constraints, four stakeholders (one sports psychologist, two conditioning trainers, and one sports physical therapist) were unable to participate in this study. Consequently, four stakeholders participated in the GMB workshops. The absence of sports psychologists and conditioning trainers from the GMB workshops limits the HI-CLD because their point of view was not adopted in the model. However, small groups of participants in GMB workshops increase communication among the members of the modeling process, making possible a dynamic and in-depth discussion [38]. The stakeholders who participated included two academic physical therapists with extensive expertise in sports injury prevention and rehabilitation, as documented by significant published literature; one orthopedic surgeon who was also a lecturer in orthopedics at a medical school and served as the head of the medical staff for a Greek first-division football club; and one sports physical therapist who was part of the medical team of a Greek first-division football club.

This endeavor is not isolated, but part of a grander schema aimed at the construction of a quantitative SD model dedicated to the prevention of HIs. The University of Patras Ethics Committee approved the ethical integrity of this study. In addition, this study adhered to the registration requirements of ClinicalTrials.gov (NCT05425303). Informed consent was essential, with all participants fully briefed on the study aims and consenting to their involvement. The analytical prowess of Vensim software (PLE version 10.1.3, Ventana Systems) was leveraged in the development of the model, with this study’s reporting aligning with the COREQ guidelines for qualitative research [41].

2.2. Details about the GMB Process

Five GMB workshops were conducted to develop the HI-CLD, each lasting approximately two hours. The HI-CLD is a continuation of an initial CLD diagram that considers all acute non-contact lower limb injuries [25]. In these co-creation workshops, the core modeling team and stakeholders engaged in a detailed discussion of the diagram, incorporating their perceptions of the problem into the HI-CLD. Eight (n = 8) participants/team members, in total, constituted the HI-CLD modeling team, four (n = 4) of them constituted the core modeling team (SAX, GNP, NIL, and CT) who participated in all workshops, and four (ET, KF, SR, FS) participated in the following workshops and they will be called stakeholders. During these co-creation workshops, each member of the core modeling team assumed one or more of the following roles based on Scriptapedia guidelines [39,40]: (1) the facilitators (SAX and GNP), who guided the team through the discussion; (2) the modeler (NIL), focused on the team’s created model; and (3) the recorder (CT), primarily tasked with recording the main elements of the modeling team progress. Stakeholders provided their opinions and discussed the model (ET, KF, SR, FS) [37,38].

Table 1 presents the entire process of formulating the HI-CLD. Ιn the first GMB workshop (B1), the risk factors affecting HI from the literature review were discussed. Specifically, the core modeling team and two academic members of the stakeholders’ team (ET and KF) were involved in the first workshop. Based on the “Variable Elicitation” script [40], the members reviewed the risk factors list and proposed additional factors or deleted factors. After the members reached a consensus, a final list of risk factors was formulated, and the members proposed an additional literature review for other possible risk factors affecting HI. Based on the outcome of the aforementioned workshop, the principal investigator created the first perspective of risk factor interaction to be discussed in the following GMB workshops.

This was followed by two GMB workshops (B2 and B3), where the core modeling team (Table 1) discussed the HI-CLD using the “Causal Mapping with Seed Structure,” “Model Review”, and “Next Steps and Closing” scripts from Scriptapedia [39,40]. In these workshops, further development of the HI-CLD occurred and a decision was made to integrate more stakeholders into the formulation process.

Then, all team members (n = 8) were invited to participate in the fourth GMB workshop. To better organize this workshop, the core modeling team prepared and emailed to participants a video presentation on the HI-CLD, which introduced the scope of the meeting. The fourth GMB workshop included a two-hour web meeting involving all team members for a detailed discussion of the different components of the HI-CLD. The workshop was structured based on the “Modeling community presentation” and “Model Review” scripts. After the fourth workshop, stakeholders rated each risk factor included in the diagram according to their perception of its importance in the etiology of HIs. The core modeling team created a Microsoft Word file with all the factors discussed and inserted into the HI-CLD during the GMB workshops. This file was emailed to all participants of the modeling process and asked them to rate each risk factor’s importance in the etiology of HI according to their point of view. This rating was performed on a scale of 1–10, where 1 represents “this factor has no association with HI” and 10 represents “this factor has an extremely high association with HI”

After integrating the fourth GMB workshop’s feedback from stakeholders, the core modeling team held a final fifth meeting to review and make the last modifications to the HI-CLD based on previous GMB discussions, stakeholders’ factor ratings, and literature evidence.

3. Results

The formulated HI-CLD illustrates the interrelationships among the risk factors, denoted by either a positive (+) or negative (−) polarity link (Figure 1). A positive polarity indicates that the dependent factor changes in the same direction as the independent factor. For example, if the independent factor increases, so does the dependent factor. Conversely, a negative polarity signifies that the dependent factor will change in the opposite direction to the independent factor; thus, if the independent factor increases, the dependent factor will decrease and vice versa [25,26].

Sixty-five factors were included in the HI-CLD and scored (1–10), as detailed in

Table 2. Definition and stakeholders’ and modeling team mean scoring (1 = no association with HI, 10 = high association with HI) of factors included in the conceptual Hamstring Injury Causal Loop Diagram (HI-CLD).

	Model’s Factors	Stakeholders’ Viewpoint and Supporting Literature Evidence for Each Factor Included	Scoring (1–10)
1	Level of influence of previous injury	Higher Order Factor that is a compound of simpler measures of previous injury characteristics [5,12,36]. Stakeholders agreed that is probably the most significant risk factor that affects soft tissue quality and the ability of muscle to generate appropriate functionality.	9.20
2	Quality of rehabilitation	Quick return to play without appropriate soft tissue reconditioning and insufficient ability of HS to tolerate the sport’s load demand increase the risk for re-injury. Evidence-based and sport-specific rehabilitation reduces the re-injury rate [5,18,33,36].	8.86
3	Severity of previous injury	Severity of the first injury increases the risk for recurrence. The new injury tends to result in more days lost [5,12,33,36].	8.71
4	Coache’s compliance with medical instruction	The coach is a critical person in injury prevention. Teams that their coaches have a more democratic style, better compliance, and communication with medical staff report fewer HIs [18,42]. Stakeholders agreed a coach with concentrate leadership style increases the (re)injuries likelihood.	8.71
5	Number of previous injuries	The number of previous HIs affects the soft tissue quality leading to future injuries [5,43].	8.57
6	Scar Tissue Size and Quality	The size and the quality (elasticity, collagen quality) of scar tissue depend on the severity of previous HI and the quality of early mobility intervention. Scar tissue quality may has an impact on the athletes neuromuscular coordination affecting the re-injury rate [44,45].	8.57
7	External or internal pressure on athlete to return to play after an injury	Coaches with a less democratic leadership style may puss athletes, with not properly healing injuries, to return to play or not consider the minor discomforts/injuries mentioned by the athletes [18,42].	8.57
8	Accumulative fatigue	Fatigue during football games affects the HS muscle’s ability to decelerate the lower leg in the late swing phase of high-speed running. After fatigue, there is a decline in knee extension peak angles and total angles during running, affecting all running kinematics [18,46].	8.50
9	Age	Older age athletes (>25) are at increased risk. This associated with a grater injury history, grater workload, and decline in soft tissue quality [5,12].	8.29
10	Hamstring eccentric strength	Lower HS eccentric strength has been associated with HI and re-injury [43,47,48].	8.29
11	Week-to-week changes in running exposure	Sudden changes in running exposure affect injury likelihood. More than the two-yearly average amount of high-speed running (>24 km/h) in the 4 weeks before HS injury and more significantly in the last week (OR = 6.44) impact the occurrence of HI [49]. Weekly distance covered above 24 km/h (RR = 3.4), and absolute week-to-week change in distance covered above 24 km/hour (RR = 3.3) had a significant impact on the risk of HI in the following week [17].	8.29
12	Anatomical location	Previous HI increases the risk for new injury. History of HI (Risk Ratio (RR) = 2.7), previous ACL injury (RR = 1.7), and previous calf strain injury (RR = 1.5) significantly increase the risk for HI [5].	8.14
13	Acute chronic workload ratio (ACWR)	Sudden changes in workload increase the fatigue and the risk for injury. ACWR within the range of 0.8–1.3 could be considered an appropriate level. ACWR ≥ 1.5 represents the ‘danger zone,’ and ACWR > 2.0 has been associated with a greater risk for injury relative to moderate or low ACWR (RR = 3.7–3.9) [16,50,51,52].	8.14
14	Communication between medical staff and coaching staff	Lack of communication between the Medical and the coaching staff has been linked with a higher rate of HIs. It affects factors such as prevention strategies, rehabilitation, and load management in at-risk payers [18,42].	8.00
15	Chronicity	Previous HI in the same season significantly increases the risk for re-injury (RR = 4.8) [5].	7.86
16	Accumulative workload	Higher chronic training loads with a balanced ACWR protect athletes by producing positive neuromuscular adaptations. It is measured: Internal loads: RPE, External loads: GPS, accelerometer [50,51,53].	7.86
17	Quality of high-speed running biomechanics	Higher order factor that is a compound of various measure kinematic and kinetic variables during high-speed running, mainly assessed by 3D kinematic evaluation	7.81
18	Quality of high acceleration movements	The primary mechanism leading to HI [54].	7.80
19	Neuromuscular coordination	Higher Order Factor that is a compound of simpler measures about the neuromuscular condition.	7.75
20	Strength and flexibility asymmetries	High strength and flexibility asymmetries are proposed as potential risk factors. High interlimb asymmetries are associated with asymmetrical imposed loads on athletes [5].	7.71
21	Structural and HS architectural quality	A collective variable that describes the quality of structural and architectural characteristics	7.71
22	Hamstring eccentric load during sprinting	Large extension moments in hip and knee power absorption during the swing phase of running would place competing demands on the HIs [8,10].	7.71
23	Injury prevention strategies	Evidence-based overall prevention strategies applied by the teams are essential for HI prevention [55,56].	7.71
24	Strength quality	A collective factor that describes the quality of the whole strength characteristics. It is measured by isokinetic dynamometer (ID), hand-held dynamometer (HHD), Nordic hamstring device [5].	7.57
25	Hamstring strength endurance	A decline in eccentric hamstring endurance may be associated with changes in the intermuscular recruitment pattern, increasing the risk of injury [57].	7.57
26	Hamstring flexibility	Conflicted evidence for the linear association of hamstring flexibility and injury. Reducing hamstring flexibility after return to play is associated with re-injury. It is measured by passive single leg raise (SLR), active knee extension (AKE), and passive knee extension (PKE) [5,43].	7.57
27	In-season recovery from competition	Medical experts of high-level football teams have highlighted the strategies as a significant risk factor. lack of in-season recovery may increase the workload and accumulative fatigue [18].	7.57
28	Hamstring EMG activation pattern/intermuscular interplay/intermuscular hierarchy	Athletes whose HIs activated after the lumbar erector spinae were eight times more likely to sustain a hamstring injury [58]. The overactivation of the biceps femoris and the decline of the semitendinosus activation during eccentric exercise have been connected to HI [59].	7.43
29	High-speed running exposure during training	Lack of regular exposure to high-speed football during training sessions may make athletes unprepared to cope with demanding situations during sports participation. Exposures to maximal velocity running during training reduce the HI risk [18,53].	7.43
30	Permanent medical staff in the team	The existence of adequate and permanent medical staff within the team is important for appropriate injury prevention [60,61].	7.43
31	Psychological mood	A collective higher-order factor that composes the quality of multiple measure factors of athlete psychological characteristics (daily hassles, anxiety, negative life events, common mental disorders (CMD)). It is assessed by the use of various questionnaires. Included studies assess all sports injuries, non-specific studies for HI risk [55,62,63].	7.33
32	Hamstring: quadriceps strength ratio	Conflicted evidence about the linear relationship of hamstring to quadriceps ratio and HI. It is proposed that an increased ratio increases the risk for HI [5,34]. This factor affects the overall strength quality and performance.	7.29
33	Hamstrings fascial stiffness	Increased passive stiffness of fascial tissues is observed in the leg with a previous history of HI [64]. Hamstring stiffness is defined as the resistance to a particular external force that changes the muscle’s shape and is determined from the mechanical oscillation of the leg evaluated by an accelerometer. It is measured by MRI, and shear wave elastography [65].	7.29
34	Dynamic core stability	Lateral trunk flexion and low electromyography (EMG) activity of trunk muscle during running have been associated with HI. Core stability is considered an important factor for injury prevention and high speed running performance [8,9,10]. Dynamic assessment: 3D kinematic evaluation. Static Assessment: Core endurance tests (maximum time).	7.29
35	Biceps femoris long head (BFlh) fascicle length	Biceps femoris long head length fascicles shorter than 10.56 cm (RR = 4.1) increase the risk for HI [48,66].	7.14
36	Lateral trunk flexion	Ipsilateral to late swing phase trunk flexion during high-speed running has been associated with HI [8,10].	7.14
37	Motivation	Sports devaluation and overall burnout symptoms have been linked to HIs. Athlete’s doubts about the benefits gained from the sports may lead to loss of concentration, increasing the risk of injury [21,22].	7.14
38	Negative life event	Negative life events stress (previous injury, death of a family member, etc.) has been associated with future injuries in football [67].	7.14
39	Core and gluteal EMG activity during swing phase	Graeter gluteal and trunk muscle electromyographic activation during the swing phase of sprinting were related with a decreased risk of HIs [9].	7.00
40	Exposure rate to training and competition	Higher exposure to training and matches increases the total workload and the exposure of athletes to high-speed running [50,51].	7.00
41	Level of competition	Higher levels of competition cause athletes to have increased workloads and are imposed more on external risk factors [68,69].	7.00
42	Match congestion	Muscle injuries were lower when there were at least 6 days between the matches’ exposure [6,70].	7.00
43	Angle of peak torque	The angle of peak torque production has been proposed as a potential risk factor for HI and re-injury. Hamstring torque production in the outer range position (near knee extension with hip flexion) is an important measure [71].	6.86
44	Pennation angle	Pennation angle was significantly greater in injured BFlh than uninjured control at rest and during isometric contraction as measured by ultrasound [72].	6.86
45	Sleep quality	Lower sleep quality has been associated with HIs [21].	6.86
46	BMI	Conflicted evidence. Did not significantly increase HI [5,12]. Injured players had significantly higher BMI [19].	6.71
47	Time loss from sports participation due to injury	The higher time loss is associated with larger soft tissue damage, increasing the possibility of re-injury [33].	6.67
48	Gender	HS incidence is 0.3 to 0.5 per 1000 h of exposure in women and 0.3 to 1.9 per 1000 h of exposure in men [68].	6.57
49	Environmental condition	There is no clear evidence. The different environmental conditions in the match’s place may indirectly affect the occurrence of injury [5,73].	6.50
50	Cross-sectional area (CSA)	Larger CSA is associated with a higher ability of muscle to produce power. It is measured by MRI [48,66].	6.43
51	Leg length discrepancy	Functional leg length asymmetries (OR = 3.80) have been associated with HI [13].	6.43
52	Interchange of opponent player	A player who enters to play after a rest on the interchange bench may have short-term protection against HI due to the absence of fatigue. However, his unfatigued condition may contribute to increased average high-speed running for his direct opponent, increasing the fatigue and the risk of injury for the opposition team’s players [5,74].	6.43
53	Genetic factors	Genetic variants seem to be involved in the multifactorial etiology of HIs, as measured by genotyping [75].	6.00
54	Player position	The player position that requires more high-speed running actions increases the injury risk [5,36,76].	6.00
55	Athlete’s aerobic capacity	Higher athlete’s aerobic capacity protects against high workload and ACWR [50,51,53].	5.86
56	Surface type	HI risk is 1.5-fold higher on grass compared with artificial turf surfaces [1].	5.86
57	Team financial level	Team’s sports performance is significantly affected by injuries and financial performance. Investment in injury prevention can reduce the injury incidence [60].	5.86
58	Team financial consistency	Team financial consistency affects the work of the team’s staff and athletes [60].	5.71
59	Season phase	Season phase impacts the workload characteristics applied to athletes [5,76].	5.14
The following variables included after reviewing stakeholders’ discussion and conducting further literature review
60	Lumbo-pelvic control	Anterior pelvic tilt increases tensile forces on HS during running. Lack of appropriate lumbo-pelvic stability may increase the anterior pelvic tilt during high-speed movements [77].
61	Sociocultural stress	The expectation from athletes to be “tough” and participate in sports through pain and injury. Factors such as social pressure mechanisms, organizational stress, and stress in pursuit of high achievement in sports affect the response to injury [78].
62	Compliance of athlete	Lack of player adherence to medical and coach instructions may limit the effects of contemporary injury prevention programs [79].
63	Changes in coaches and medical staff	Replacement of head staff members (coaches, trainers, medical staff) in male elite-level football teams seems associated with increased HI burden during the season [61,80].
64	Local and national injury prevention policies	Investment in injury prevention, national and league characteristics [20,70,80,81].
65	Muscle fiber typology	Athletes with faster typology fibers are 5.3 more likely to sustain a HI. Potential correlation with fatigue [82].

Abbreviations: ACL—anterior cruciate ligament, ACWR—acute chronic workload ratio, BMI—body mass index, GPS—global position systems, HS—hamstring, HI—hamstring injury, MRI—magnetic resonance imaging, RR—risk ratio, and RPE—rate of perceived exertion.

. Factors such as previous injury characteristics (level of influence of previous injury, severity of previous injury, scar tissue size and quality), quality of rehabilitation, coach’s compliance with medical instructions, pressure on an athlete to return to play after an injury, accumulative fatigue, age, week-to-week changes in high-speed running, and eccentric HS strength were highlighted as particularly significant risk factors according to stakeholder opinions (Table 2). The HI-CLD, presented in Figure 1, is a conceptual model that outlines the entire system affecting the essentials of an HI, capturing the nonlinear dynamic interrelationships among risk factors. The HI-CLD shows that most of the factors have an indirect effect on HI injury. The effect of each risk factor fluctuates based on the factors’ dynamic interrelationships. It incorporates key higher-order risk factors represented by the aggregation of multiple simpler measured variables [30]. These include the level of influence of previous injuries, neuromuscular coordination, psychological mood, quality of high-speed running biomechanics, high acceleration movement quality, and accumulative fatigue. Additionally, the internal athlete profile interacts with external factors such as workload characteristics, quality of rehabilitation, surface type, environmental conditions, and the application of injury prevention strategies, along with team factors such as the quality of communication between coaching and medical staff. The risk factor associations in the HI-CLD create feedback loops which an output in the system is routed back as input, creating a circuit chain of causes and effects. Circuit chains of interrelationships created by positive polarity links among factors produce reinforcing (R) feedback loop. On the other hand, circuit chains of interrelationships created by positive and negative polarity links produce balancing feedback loops (B) [25,26].

Table 3. Definition of the main feedback loops presented in the HI-CLD.

Loop	Definition
B1	HS re-injury loop
B2	Effect of psychological factors loop
B3	Effect of neuromuscular coordination on fatigue and the total injury risk
B4	Negative workload effect on neuromuscular coordination due to fatigue loop
R1	Positive workload effect on neuromuscular coordination loop

outlines the main feedback loops depicted in the HI-CLD.

The subsequent sections provide an in-depth description of HI-CLD. To better describe HI-CLD, risk factor interrelationships were grouped into four main categories.

3.1. Impact of Neuromuscular Coordination on the Quality of High-Speed Running Biomechanics and High-Accelerated Movements Quality That Affect Risk for HI

A network of contributing determinants shapes a specific profile that affects the quality of high-speed and highly accelerated movements. The primary mechanism of HI, which is critical to HI-CLD, is the quality of the high-acceleration movement [54,83]. Such rapid sports actions subject the hamstrings to highly eccentric demands, thereby increasing the risk of HI [54,83]. Highly accelerated sporting activities are influenced by multiple internal characteristics of the athlete such as neuromuscular coordination [77], the quality of high-speed running biomechanics [77,84], psychological mood [62], external environmental factors [73], and surface type [1]. Furthermore, factors including an athlete’s history of previous injuries [5], workload characteristics [50], fatigue [18,50], age [5], and team factors [5,18] significantly underlie the rapid acceleration movements observed during sporting activities.

A considerable proportion of HIs occurs during the late swing phase of high-speed running (HSR) [54,83]. Various aspects of core stability during HSR are linked with a future HI [8,9,10,77]. Lateral trunk flexion [8], lumbo-pelvic control, and core muscle activation patterns [9] during running are among the factors that influence the risk of HI [77]. Additionally, the hamstring’s adequate eccentric control and recruitment pattern are essential [58,59,84]. As depicted in the HI-CLD, HSR biomechanics are significantly influenced by the athlete’s internal neuromuscular coordination, fatigue resulting from inadequate workload management, and the presence of previous injuries [85].

Neuromuscular coordination is described as a higher-order variable affected by the interaction of multiple neuromuscular measurement factors, such as strength characteristics [5,47,71], flexibility, core stability, and asymmetries in strength or flexibility [5,18]. Furthermore, strength encompasses various qualities, including eccentric hamstring strength, strength endurance, and hamstrings-to-quadriceps ratio [5,57]. In addition, fast fibers typology and genetic factors may be associated with decreased hamstring muscle endurance and tolerance to accumulative fatigue [75,82]. The internal neuromuscular condition lays the foundation for the proper execution of dynamic sports actions like HSR [5,85]. For instance, deficits in core stability and lumbopelvic control that increase the anterior pelvic tilt may lead to a rise in forces applied to HS during HSR. If this situation is combined with decreased HS flexibility, eccentric strength, or endurance the risk for injury during HSR increases [5,8,57,77]. Similarly, an athlete with low HS strength endurance may be at an increased injury risk, specifically when HSR exposure is elevated, as this athlete is more prone to hamstring fatigue [57]. As shown in the B3 feedback loop, the quality of the neuromuscular coordination has a considerable influence on accumulative fatigue and the quality of HSR biomechanics affecting the risk for HI. Finally, neuromuscular coordination affects the quality of HSR biomechanics through dynamic core stability and HS EMG activation patterns (Figure 1 and Figure 2).

3.2. Interaction of Previous Injury Characteristics, Age, and Psychological Mood with the Athlete’s Neuromuscular Coordination That Affect Risk for HI

The likelihood of HI escalates with a history of earlier hamstring strain, particularly if the injury occurs during the same season [5]. Factors such as chronicity [33,36], injury type [5], number of previous injuries [43], and injury severity [5,36] are pivotal factors that influence the risk of HI.

Previous injuries alter the athlete’s internal neuromuscular coordination [5] and psychological mood [21,22,62] having a considerable impact on the whole HI-CLD [5,12]. For example, the size of scar tissue and the duration of time lost due to an earlier HI critically degrade soft tissue quality [44,45,48,86]. Stakeholders emphasize the significance of soft tissue quality post-injury, which affects the muscle’s capacity to generate adequate strength and functionality under sport-specific demands [44,45,48,86]. Shortened biceps femoris long head length [48,66,86,87] and increased HS fascial stiffness post-injury [64,65,66,72,88] modify the structural and flexibility attributes. These interconnected factors culminate in the B1 feedback loop within the HI-CLD, delineating the etiology of HI recurrence (Figure 1 and Figure 3). Furthermore, extended absence from sports due to ACL injury can detrimentally affect overall neuromuscular coordination [5], whereas given the anatomical and functional connectivity between the calf muscles and hamstrings, an injured calf muscle diminishes force absorption during dynamic actions, thereby imposing increased loads on the hamstrings [5].

The B2 feedback loop of the HI-CLD revealed that an athlete’s psychological mood is influenced by injury history, fatigue, and factors such as team financial stability (Figure 1 and Figure 3). Various psychological aspects (e.g., daily hassles, anxiety, negative life events, and common mental disorders (CMD)) associated with football injuries or observed post-recovery [62,63,67,89] can degrade athlete concentration and reaction times [21]. These factors elevate the injury risk, and psychological interventions have been shown to mitigate this risk [62]. Despite stakeholder discussions and consensus on incorporating these psychological mood aspects into the HI-CLD, supporting literature specifically regarding HS injuries remains limited.

Age is also a decisive risk factor for HIs [5,12]. Older athletes (>25 years old) with extensive injury histories significantly affect the entire etiology system [5,90]. Aging is associated with a decline in neuromuscular coordination and soft tissue integrity [5,12]; however, older athletes often face greater exposure to sports and higher workloads [5]. This interplay of factors, including increased age, critically influences the comprehensive etiology of hamstring injuries.

3.3. Effect of Workload and Fatigue on Neuromuscular Coordination and High Accelerated Movements That Affect Risk for HI

Stakeholders have underlined the critical role of appropriate training intensity, pointing out the detrimental effects of both detraining and overtraining on the etiology of HIs [50,91]. The R1 feedback loop in HI-CLD suggests that an adequately high workload without fluctuations between acute and chronic loads can enhance neuromuscular coordination and the athlete’s aerobic capacity, thereby increasing resistance to fatigue [50,51]. Conversely, excessive workloads, especially without sufficient recovery or with a high acute-chronic workload ratio (ACWR), lead to overload and fatigue, adversely affecting neuromuscular coordination as depicted in the B4 feedback loop [16,46,50,51,52]. It has been documented that a neuromuscular system challenged by a substantially higher acute workload than chronic workload is subjected to unprepared forces, causing increased fatigue [50]. An ACWR exceeding 1.4 is associated with a significantly higher risk of injury [50,51], although this threshold varies based on individual athlete profiles, where enhanced aerobic capacity and experience may offer protection against sudden workload increases [53]. Figure 1 and Figure 4 illustrates RI and B4 feedback loops as described above.

Furthermore, the rate of exposure of athletes to HSR is pivotal in preventing HIs [17,49,92,93,94]. Adequate HSR training exposure is recognized as an effective strategy for HI prevention, preparing the neuromuscular system to withstand the high eccentric demands of high-speed movements encountered during competition [18,93,95]. Balanced exposure to these movements during training is essential, with week-to-week changes in exposure recommended not exceeding 10% [50,85]. Sudden changes in HSR exposure can overload the athlete’s neuromuscular system, rendering it unprepared for exerted forces [17,49].

Finally, seasonal phases, player positions, and match congestion also influence the cumulative workload and ACWR. Specific player positions [36,76] and opponent-player interchanges [74] can increase the demand for HSR during the game, leading to fatigue. Moreover, inadequate recovery periods during phases of the season with high match congestion exacerbate cumulative workload and fatigue [6,70].

3.4. Effect of Local and National Policies and Team Staff’s Cooperation in the Total Strategies for Injury Management and Prevention That Affect Risk for HI

Stakeholders have emphasized the importance of proper injury management in mitigating the adverse effects of previous injuries as a risk factor for new HI. In addition, they noted that high-quality rehabilitation is crucial for restoring optimal neuromuscular coordination after injury. Furthermore, the coach plays a crucial role in the injury prevention and management of players with injuries or those of older age with a history of injuries [18,85]. Thus, as the literature suggests and presented in Figure 4, high-quality communication between medical and coaching staff is essential for effectively managing athletes with previous injuries and implementing appropriate preventive strategies [18]. A coach’s democratic style enhances team staff’s communication quality, aiding in the prevention of HIs [18]. However, stakeholders in the GMB process have noted that team communication suffers from changes in coaching or medical staff during the season (Figure 1 and Figure 4) [61,80].

Moreover, the implementation of appropriate injury prevention strategies is critical for reducing the risk of HI [55,56]. Global policies for HI prevention and injury-specific prevention programs have been widely advocated in the literature [55,56]. Crucial strategies include workload management and the targeting enhancement of athletes’ neuromuscular coordination. Furthermore, evaluating athletes’ psychological states and implementing psychological interventions are essential within a comprehensive injury prevention strategy [62]. Regarding that, sociocultural stress (e.g., social and organizational pressure for achieving high performance) may considerably impact athlete’s psychological mood [78]. Finally, the influence of local and national injury prevention policies on the entire system cannot be underestimated [19,20,81]. Local and national prevention policies beyond the interpersonal level and investment in injury prevention are vital components for creating a safe environment for sports participation [60,81,96].

4. Discussion

The HI-CLD formulated in this study comprehensively captures the complexity of HI etiology and offers insights to enhance injury prevention strategies and policies. To the best of our knowledge, this is the first study to integrate HI risk factors into a nonlinear model, providing a holistic analysis of the HI etiology system. While previous research has proposed more generic models for understanding and preventing sports injuries, HI-CLD represents a significant advancement by delineating the specific interactions of risk factors through both positive and negative links [31,50,97,98].

The dynamic complexity of the HI injury risk analyzed in the HI-CLD may explain why previous studies failed to find isolated predictors of HI injury [5,11,30]. The main weaknesses of these procedures are twofold. First, the majority of the relevant literature examines a complex phenomenon using simple linear regression methods that conceal the nonlinear association between the risk factors [11]. Second, examining athletes only at the beginning of the season does not consider the dynamic changes in factors during the season [30]. Previous studies [5] emphasized the significance of examining risk factor interactions. In line with these assumptions, the HI-CLD proves that HI is a highly complex phenomenon, with 65 interacting factors affecting the risk of injury. An important conclusion of the GMB process illustrated in the HI-CLD is that many factors have an indirect effect on the risk of HI. This may explain why frequently examining factors such as strength measures fail to be directly associated with future HIs [5].

The HI-CLD can guide injury prevention strategies by highlighting the importance of inter-professional collaboration. Injury prevention is a multifaceted endeavor that requires collaboration among various specialists who work closely with athletes in professional team settings. These specialists included coaches, fitness coaches, medical teams (orthopedics and physiotherapists), and sports psychologists. This study includes in the GMB workshops stakeholders working in the team’s medical staff. Despite the efforts to include all the professionals above-mentioned in GMB workshops, this did not become feasible. Each profession contributes expertise to different aspects of HI-CLD, exerting influence over the entire system. For instance, the HI-CLD includes aspects of the athlete’s psychological condition, which is the main responsibility of the sports psychologist; previous injuries and the impact on neuromuscular coordination, which is the main responsibility of the medical staff; and workload characteristics, which are the main responsibilities of coaches. Consequently, effective cooperation among team staff is essential to accurately predict and prevent injuries [18,42]. Previous research has emphasized the importance of collaboration between coaching and medical staff to reduce HI rates [18,42]. Furthermore, while injury prevention programs have demonstrated significant success in lowering HI rates, their efficacy is contingent on athlete compliance and the adoption of evidence-based methods by team staff [56,99,100].

Further, as extensively discussed in previous research and opinion papers [30,31,101,102], the athlete is a complex adaptive system, and its injury profile may change significantly during the season [30]. According to this, components described in the HI-CLD, such as workload, neuromuscular coordination, possible micro-injuries, fatigue, and psychological mood, interact and modify their properties, thereby affecting the athlete’s overall condition. Owing to this ongoing dynamic modification of an athlete’s characteristics, understanding when athletes reach an at-risk-for-injury stage during the season is crucial for HI prevention [30]. Therefore, frequent data collection describing the athlete’s condition at different times during the season is essential [30].

Team’s medical and coaching staff can use feasible screening protocols to capture the state of the athletes, thereby enhancing injury prevention. Regarding that, wearable technologies (global position systems (GPS), portable dynamometers, inertial sensors, etc.) and field-based tests enable frequent data collection on a daily, weekly, or monthly timescale [30]. The implementation of practical frequent data collection protocols can inform practitioners of “early warning signals.” Capturing the time when an athlete reaches an unstable stage (e.g., due to increased fatigue or microinjuries) is an essential first step for effective injury prevention [30]. Therefore, the team’s staff is important in implementing iterative feedback cycles of (re)evaluation, identification, and intervention as a crucial injury-prevention strategy [81,97]. This is essential because of the dynamic and complex nature of the HI, as proposed by the HI-CLD.

Table 4. Proposed field-based evaluation protocol for frequent athlete screening and interventions to prevent HIs.

1	(Re) Evaluation
	Factors	Measurements
	External Workload	Global position system (GPS) data (daily data collection)
	Internal Workload (Sence of fatigue)	Borg’s rating of perceived exertion (RPE) (daily data collection)
	High-speed running biomechanics	Two-dimensional video analysis or Inertial sensors (weekly or monthly evaluation)
	Ballistic function	Triple hop for distance test (weekly or monthly evaluation)
	Core muscle strength endurance	Prone Bridging Test, Side Bridging Test, Biering–Sorensen test (weekly or monthly evaluation)
	Hamstring strength endurance	Single-leg hamstring bridge (SLHB) (weekly or monthly evaluation)
	Hamstring eccentric strength	Hand-held dynamometer (HHD). Brake test at 30° of knee flexion from a prone position (weekly or monthly evaluation)
	Hamstring, hip flexors, calf muscle flexibility	Straight leg raise (SLR), modify Thomas, weight-bearing lunge test (WBLT) (weekly or monthly evaluation)
	Psychological condition	Athlete Burnout Questionnaire (ABQ) (weekly or monthly evaluation)
2	Identification of specific impairments of athletes
3	HI preventive strategies and interventions to tackle specific athlete impairments
	Impairments	Interventions
	Low eccentric strength, isometric to eccentric ratio or increased interlimb asymmetries	HS eccentric strength exercises (e.g., Nordic hamstrings exercise, dead lift) to increase strength and reduce interlimb asymmetries
	Low repetitions in SLHB test < 25 repetitions	HS Strength endurance exercises
	Decrease flexibility below cut-points and asymmetries in SLR test, modify Thomas test, WBLT.	Hamstring, hip flexors, and soleus flexibility exercises
	Exposure to high-speed running during training	Constant exposure to high-speed sports training without high week-to-week changes and maintaining the weekly exposure < 24 kg/h
	Core stability and lower limb impairments in running kinematics	Proper running kinematics, preventing later trunk flexion and forward trunk lean
	Impairments in core stability observed in running assessment and in specific core muscle strength endurance tests	Core stability exercises
	Impairments in core stability and increased anterior pelvic tilt	Lumbopelvic control exercises, preventing anterior pelvic tilt
	High values in the specific psychological questionnaire or during interviews with sports psychologists	Psychological interventions to reduce possible psychological symptoms
	Increase psychological and physiological fatigue or pain in hamstrings during the evaluation protocol.	Adequate recovery from the competition and specific interventions
	Athlete compliance with medical and coaching instructions	Strategies to increase athletes’ adherence to preventive interventions
	Communication quality between medical and coaching staff	Proper communication between medical and coaching staff. Frequent medical and coaching staff discussions and meetings about the management of injured athletes and the application of injury prevention strategies

describes a proposed evaluation protocol and preventive intervention strategies for the aforementioned purposes and the following text provides practical implications.

As proposed, the sports team’s staff should organize and apply feasible field-based assessment protocols to evaluate athlete conditions. More sophisticated assessments that require costly equipment, such as isokinetic dynamometers, may make implementing continuous screening more difficult. Primary evaluations should focus on higher-order factors that better represent the system’s function [30]. As HI-CLD proposes, these factors include workload characteristics, neuromuscular coordination, and psychological conditions. Regarding workload, repeated screening of GPS data can prevent spikes in the applied workload by maintaining an ACWR range of 0.8 to 1.3 and week-to-week changes below 10%. Particularly, week-to-week changes in HSR and distances exceeding 24 kg/h should be prevented. GPS technologies may make it feasible for fitness coaches to record and moderate the applied workload to the desired level. Additionally, using the Borg Rating of Perceived Exertion (RPE) scale, team staff can gauge athlete fatigue. On the other hand, core stability during HSR and overall sprinting biomechanics are essential components. Utilizing two-dimensional assessment or inertial sensors can enhance the evaluation of HSR biomechanics. Furthermore, the team’s staff would benefit from adding to the assessment protocol examinations of ballistic function, core endurance tests [103,104], hamstring endurance [57], hamstring eccentric strength [47], and hamstring flexibility [5]. Moreover, validated questionnaires such as the Athlete Burnout Questionnaire (ABQ) can provide information about the athlete’s psychological condition (Table 4) [21,22,55].

Similarly, it is vital to apply specific and individualized exercise prevention interventions to deal with specific impairments (neuromuscular or psychological) captured in structural evaluation. Likewise, effective prevention plans should incorporate adequate exposure to HSR during training [18,93], strategies to increase athletes’ adherence to medical instructions, and strategies for adequate recovery from competition [55,79]. Finally, it is essential to mention that HI assessment protocols should be incorporated into a more comprehensive protocol that considers the risk factors for the main sports injuries. Although HI is the most prevalent injury [4], other types of injuries represent a considerable percentage of the total injury rate and should not be underestimated [85]. Importantly, these interventions should be adopted in the specific context of the particular team, sports, and societal characteristics (Table 4) [20,81].

The HI-CLD incorporates a socioecological view of HI etiology, as Bolling et al. [20] proposed in the revision of the first step of the sequence of prevention. This model includes factors at multiple levels, such as the individual (e.g., athlete internal risk profile), sociocultural (e.g., interaction among stakeholders), and policy levels (e.g., local and national policies) [20,96]. The HI-CLD shows that the aforementioned levels are not isolated, but are highly interdependent. A change at a higher level such as the policy (“local and national policies for injury prevention” in the HI-CLD), affects the athlete’s condition which is at the lower level. For instance, policies that produce an increase in match congestion have an impact on athletes’ fatigue and the risk of injury. In addition, frequent changes in medical or coaching staff produce an imbalance that affects team communication quality and the feasibility of injury prevention strategies affecting injury occurrence. Therefore, policy makers should consider possible proposed changes from the perspective of injury prevention.

However, the provided HI-CLD possesses limitations worth noting. First, the qualitative nature of the model necessitates further efforts to quantify factor interactions, underscoring the need for transitioning to a more quantifiable analysis. Structural equation modeling techniques may provide quantitative results by simultaneously assessing the interrelationships of multiple factors. Future studies will benefit by incorporating such statistical techniques. Second, broadening the stakeholder base to include various specialties such as coaches, players, and psychologists could enrich the model’s development. However, constraints related to these professionals’ availability and their commitments limited their participation in the GMB process. Broadening stakeholder participation can provide more accurate factor interrelationships. Third, the GMB workshops were organized based on some of the scripts proposed. The application of more detailed scripts could promote a more in-depth discussion of the problem.

5. Conclusions

HI-CLD provides insight into the complexity of HI risk factors, enhancing the understanding of the etiology of HI. Based on this knowledge, practitioners, researchers, and policy makers can use HI-CLD to improve the applied total injury prevention strategies. The formulated model and stakeholders’ opinions revealed that appropriate injury prevention is a comprehensive, multilevel approach beyond isolated procedures. Stakeholders emphasized the importance of injury management and the responsibility of all team staff in preventing the likelihood of re-injury. This study and the formulated HI-CLD highlight that the interrelationships of many risk factors lead to the indirect impact of many model’s elements on HI via other higher-order factors.

Finally, computer simulations present a promising avenue for augmenting data analysis concerning risk factors and facilitating predictive insights [26]. The current qualitative HI-CLD paves the way for establishing a computer simulation SD model capable of evaluating plausible HI preventive strategies prior to implementation based on collected data on the risks of injury. An SD model’s capability to simulate various “what if” scenarios offers invaluable insights for practitioners and researchers, enabling strategic decision making before the application of real-world interventions [25,26]. The progression towards formulating a quantitative SD model represents a forthcoming phase in this project’s continuum, aiming to significantly contribute to HI prevention efforts.