When evidence fails in real life: Rethinking sports injury rehabilitation and prevention

Highlights

• •Sports injuries arise from dynamic, context-dependent interactions among factors.
• •Treat injury as a context-dependent process, not a sum of isolated risk factors.
• •Assess the athlete as a whole: integrate biological, psychological, and social domains.
• •Use evidence as a compass: combine research, clinical expertise, and athlete values.
• •Match methods to complexity: simulation, monitoring, and participatory research.

Abstract

Background

Traditional models of sports injury prevention and rehabilitation have largely relied on linear, reductionist frameworks that emphasize isolated risk factors and standardized interventions. Although these approaches have advanced the field of sports, they have shown limited capacity to explain real-world injury patterns or to guide effective, context-sensitive clinical practice. Emerging perspectives grounded in complexity science, contemporary interpretations of evidence-based practice, and dynamic biopsychosocial thinking offer more comprehensive ways to understand and manage sports injuries.

Objective

To critically examine the historical and conceptual foundations of sports injury prevention and rehabilitation, clarify misconceptions surrounding evidence-based practice, and discuss the limitations of reductionist applications of the biopsychosocial model. The goal is to propose a more integrated, complexity-informed framework to support patient-centered and contextually relevant physical therapy practice.

Discussion

The current sports injury literature shows that these injuries arise from nonlinear, dynamic interactions among biological, psychological, and social determinants, challenging the adequacy of linear causality and one-size-fits-all interventions. Evidence-based practice is often misinterpreted as a hierarchy of study designs rather than as an integrative process that combines research evidence, clinical expertise, and patient values within real-world constraints. Similarly, the biopsychosocial model is frequently applied in a fragmented manner, treating its components as separate rather than interdependent. Integrating complexity thinking with a fully realized EBP framework and a dynamic biopsychosocial perspective enables clinicians to navigate uncertainty, tailor interventions, and deliver care that aligns with athletes' lived realities. This approach strengthens clinical reasoning and supports more effective, patient-centered rehabilitation.

Introduction

Sports injury prevention and rehabilitation have long presented both research and clinical challenges. The sports injury etiology model proposed by Meeuwisse in 1994¹ conceptualized injury occurrence as the result of an interaction among intrinsic and extrinsic factors, an inciting event, and an injury mechanism.¹ In 2007, the model was updated to incorporate a cyclical nature, recognizing that exposure may lead not only to injury but also to no injury or to potential adaptations, and to consider how risk factors change across preceding cycles of participation.² Despite its innovation, the model largely reflected a linear view of how intrinsic and extrinsic factors interact, leading to numerous studies that examined isolated risk factors for injury. For example, dynamic knee valgus has been identified as a risk factor for anterior cruciate ligament (ACL) injury.^3,4 Studies investigating linear relationships were valuable in identifying important contributors to injury risk at the time. However, new ideas and concepts continued to shape the understanding of sports injuries.

Around the same time, van Mechelen introduced the “Sequence of Prevention” model, which aimed to systematize research on sports injury prevention.⁵ This model proposed four steps: 1) identifying the extent of the problem—typically through epidemiological studies; 2) establishing etiology and risk factors; 3) developing and introducing preventive interventions; and 4) evaluating the efficacy of these interventions by assessing changes in injury incidence. This model led to epidemiological injury profiles, risk-factor studies, injury-mechanism studies, and randomized controlled trials to test the efficacy of preventive interventions. Numerous trials showed reductions in injuries such as ACL tears, hamstring strains, groin injuries, and recurrent ankle sprains.^6,7 Despite strong evidence of efficacy from randomized controlled trials (RCTs), real-world implementation proved challenging. Injury reductions observed in RCTs were often much smaller when interventions were implemented in practical settings.⁸

Recognizing the gap between RCT results and real-world implementation, Finch proposed the TRIPP model (“Translating Research into Injury Prevention Practice”), which added two critical steps to account for contextual factors and real-world implementation.⁹ For instance, the “Knee Control” program from the Swedish ACL prevention trial demonstrated a 64% reduction in injuries in controlled conditions but only a 15% reduction when implemented in community sport.¹⁰ This discrepancy highlighted the importance of factors such as awareness, proper use, sustained adherence, and contextual fit. When interventions are exercise-based, their effectiveness depends, among other factors, on athletes integrating these exercises into regular practice. Therefore, behavior is a key component in achieving trial outcomes, as adherence and compliance are essential determinants of success.^11,12

Challenges in sports injury prevention prompted the field to reconsider whether isolated, reductionist approaches were sufficient to achieve meaningful reductions in injuries in real-world contexts.^13,14 The inherently complex nature of sports injuries began to be recognized as the result of ongoing interactions among multiple determinants.^15,16 This shift marked a move toward athlete-centered, context-sensitive approaches and away from the idea that “one-size-fits-all” solutions derived from RCTs could universally apply.^17,18 New and more relevant questions have since emerged: Why do some athletes or practitioners fail to adopt interventions? How do individuals adapt or modify programs? Which interventions work for whom, and under what circumstances? The once-dominant question— “Does it work?”— has evolved into a broader inquiry that considers complexity, context, and individual variation.19, 20, 21 As new questions emerge, new methods must be employed and explored. Today, sports injury prevention is understood as a complex, dynamic phenomenon shaped by biopsychosocial interactions and multi-level system influences—from individual athlete factors to interpersonal, organizational, and societal contexts.^15,22,23

Complexity in injury prevention and rehabilitation

One of the most misunderstood concepts in rehabilitation is complexity.²⁴ Frequently, complexity is equated with complicated situations or contexts in which many elements are at play. Although the involvement of many elements is sometimes a necessary condition for complexity, it is by no means sufficient. Complexity depends on the nature of interactions among elements.²⁵ If these interactions are simple and linear (i.e., directly related), the situation, regardless of the number of elements involved, can be considered as simple or, at most, complicated. For instance, the Meeuwisse model² indicated linear interactions among intrinsic and extrinsic factors and the injury mechanism.² Conversely, if the relationships among the elements are complex, bidirectional, dynamic, and nonlinear, we will face a complex situation.²⁶ For example, if all sports injuries were determined solely by simple interactions among biomechanical and physiological factors, regardless of an individual's context, history, or behavior, we could fully predict them and establish effective preventive and rehabilitative programs for all individuals involved in sports. Unfortunately, this is not the case. The main reason for this failure is that the relationship between sports injuries and their causes is complex and changes over time.²⁷ As Bolling et al. stated, sports injuries are context-specific and influenced by biological, psychological, and social factors within individuals and their environments.²² In fact, intrapersonal, interpersonal, organizational, community, and societal factors constantly interact to influence performance and injury occurrence.14, 15, 16

To understand how an injury arises from complex interactions among multiple factors, we must unpack the concept of complex interactions.²⁷ Complex interactions are those in which the relationships among elements are nonlinear, dynamic, interdependent, bidirectional, multiscale, and emergent.²⁶ Nonlinear and dynamic interactions mean that the effect of one factor on the outcome is neither proportional nor predictable, and that this effect changes over time. For instance, strength measured at a given point in the season will not be the same later in the season or when fatigue sets in. Interdependence and bidirectionality imply that one causal factor influences another, which in turn modifies the initial causal factor, meaning that the behavior or factor affects and is affected by the whole. For example, consider the kinetic chain: joints such as the knee, hip, and ankle affect and are affected by one another, altering movement patterns. Multiscale and emergent interactions mean that behaviors or patterns related to injury occurrence are not present in individual components but arise from their collective dynamics. These collective dynamics span multiple scales, from the molecular to the organismal and from the personal to the societal.²⁸ For example, when considering behavior and a simple preventive intervention, such as helmet use among cyclists, societal and cultural factors will influence its adoption. On an individual level, helmet use has a clear effect. However, behavior (adoption) modifies this effect and is influenced by factors beyond the individual. Finding a common or global cause of sports injuries that can be applied successfully to all individuals is possible only when dealing with simple or complicated phenomena. The complexity of sports injuries renders this approach impossible, making the “one size fits all” intervention a failure. Therefore, complex interactions are not just about many parts interacting, but about how those interactions continuously shape and reshape the system in nonlinear, multiscale, context-dependent, and often unpredictable ways. However, complexity does not mean that nonlinear interactions invalidate linear analysis approaches.¹⁵ Linear approaches can approximate existing interactions and efficiently investigate phenomena when proximal events or factors have a greater effect than distal ones. In these situations, a linear relationship may emerge.

How is it possible to apply the concept of complex interactions in understanding sports injuries? Imagine a player who, on assessment, has adequate biomechanical attributes and good technique. However, due to specific personal (e.g., poor sleep) and contextual factors (e.g., pressure to perform well) arising from contract negotiations, he becomes fatigued, and his movement patterns change. This altered movement can increase stress on joints or muscles, leading to injury. In this situation, the interaction between neuromuscular factors and fatigue is nonlinear, as fatigue not only reduces performance but also unpredictably reshapes the body's movement. The emerging movement can reflect either a more repetitive or more disorganized pattern, depending on other existing factors.^29,30 This reshaping, driven by contextual factors at different scales (physiological/behavioral), may impose distinct stresses on the musculoskeletal system and is not captured in the athlete’s initial assessment, even though it relates to injury. For example, consider an athlete under pressure who makes riskier decisions during a match. He pushes through pain or adopts a more physical style of play than he would in other contexts. Combined with a slippery field or an aggressive opponent, the risk of injury spikes. Finally, in a specific context (e.g., a decisive stage of competition), consider an athlete facing high training volume, poor sleep, and suboptimal nutrition. All these aspects can impact recovery, regardless of the player's initial physical attributes. The interaction among these factors creates a fragile physiological state in which even minor perturbations (such as a sudden sprint) can trigger injury. What makes these cases complex, rather than merely multifactorial, is that the same set of factors produces different outcomes depending on context, and the athlete's state emerges from their interaction rather than from any single factor. Effective interventions must therefore account for these interactions and remain adaptable to changing conditions.

From singular solutions to pluralistic thinking

Sports injury prevention and rehabilitation involve more than using the “best” consensus approach. The complex nature of sports injuries does not support the pursuit of “the best” intervention or the establishment of a universal protocol. The extensive search for factors associated with injury occurrence (mainly athlete-related) led to overlooking other levels- such as rules changes, context, and even education.⁷ This risk-factor approach has proven limited and, at best, directed toward the general population rather than the specific athlete in care.¹⁵ No single intervention or biomechanical/physiological factor can effectively prevent injuries or treat all athletes. Looking for the Holy Grail of injury rehabilitation assumes a single solution to a complex situation. Since there is no one-size-fits-all solution, clinicians must be prepared to apply multiple valid care pathways tailored to individuals and their specific settings. Clinicians are thus invited to shift from a reductionist to a complexity-embracing mindset.²⁶

The challenge of embracing complexity stems from the lack of complexity-based guidelines or consensus on the best approach to injury prevention and rehabilitation. However, navigating complexity doesn't mean abandoning structured thinking.³¹ Instead, it requires practical frameworks that allow for flexibility, tailoring, adaptation, and clinical reasoning. Rigid protocols might provide comfort,³² but they seldom account for the dynamic nature of sports injuries.³¹ Recognizing the complex nature of sports injuries requires implementing context-dependent approaches. One key feature of complexity is equifinality, meaning that many different factors and situations can lead to the same outcome.¹⁵ Therefore, there is no direct relationship between a specific treatment and a particular pathological condition, as multiple contextual factors contribute to the production of the same injury. Clinicians must consider potential biomechanical and psychological factors relevant to injury, as well as personal and social factors, when tailoring approaches to the athlete rather than focusing solely on a particular health condition. Protocols and guidelines provide practical frameworks for interventions, but they should remain flexible enough to address the athlete's unique needs.

A traditional approach to reducing the clinician's decision-making burden is to search for specific injury-related patterns.¹⁵ However, recognizing patterns should not be seen as rigid templates but as adaptable guides. Injury presentations often share biomechanical or behavioral causes, yet each athlete has a unique physiology, history, and psychosocial background.²⁷ Respecting this individuality means avoiding overgeneralization or labeling the athlete with a diagnostic term. Identification of the athlete’s needs requires listening, observing, and adapting, even when the pattern appears familiar. While a simple ankle sprain can be “explained” by an inversion mechanism, treatment and prevention should consider the athlete’s needs beyond the injury pattern. Recognizing injury mechanisms or recovery patterns can guide decision-making, but clinicians should interpret them through the lens of each athlete’s unique physiology, psychology, and social context. Clinicians must work collaboratively with the athlete to develop solutions and support rehabilitation, balancing consistency and creativity while being informed by evidence. Injury rehabilitation is not a straight path of undisputed procedures, but a dynamic landscape shaped by context, complexity, and variability.

To work effectively within this pluralistic landscape, clinicians must develop a critical relationship with evidence. This criticism does not mean rejecting research findings. Instead, it involves questioning their relevance, limitations, and applicability to the athlete in front of you. Evidence-based practice serves as a compass, not a map, guiding treatment directions but not dictating every step.³² Defining the best intervention should be viewed as a process grounded in the dynamic interaction among empirical knowledge, clinical expertise, and patient values.³³ Therefore, clinicians need to be critical of the evidence without rejecting it, ensuring that all evidence is contextualized. Scientific findings provide valuable insights, but they often come from controlled environments that may not reflect the athlete's real-world experiences.³⁴ Pluralistic complex thinking empowers clinicians to incorporate diverse perspectives and treatment approaches, along with available evidence, as complementary tools in a personalized care strategy.³⁵

The misconceptions of evidence-based practice

It is undeniable that the field of sports injury prevention and physical therapy has evolved considerably over recent decades. However, research and practice have also historically been shaped by a reductionist view that emphasizes linear causality and narrowly defined outcomes. As mentioned before, researchers often focused on isolated physical risk factors and treated the human body as a predictable, mechanical system. Quantitative methods, particularly randomized controlled trials (RCTs), dominated this landscape.³⁶ This traditional approach followed the hierarchical “evidence pyramid,” placing systematic reviews and RCTs at the top.³⁷ Although the pyramid was designed to provide a logical structure for assessing scientific credibility, it assumes that higher-tier studies automatically produce more valuable or applicable knowledge. In practice, these methods often lead to what Cartwright and Munro called “it-works-somewhere claims”—showing that an intervention worked under specific conditions, but not necessarily in new or different contexts.³⁸ Consequently, this paradigm struggles to answer the “will-it-work-for-us” questions that clinicians face daily. For this reason, although RCTs are effective in establishing the direct effect of an intervention on the selected outcome under a controlled environment, they are not designed to capture the complex, interconnected causal mechanisms at play in real-world systems.³⁶

The field of sports rehabilitation faces what Di Fabio called the “myth of evidence-based practice”,³⁹ suggesting that no treatment plan is ever definitively the best or most suitable for every patient. The gap between research and clinical practice is evident: studies describe daily interventions over several weeks, whereas real-world settings may limit sessions due to insurance coverage. Similarly, studies may focus on elite athletes, whereas clinicians often work with recreational participants. Therefore, understanding the scientific literature must always be complemented by clinical reasoning grounded in experience. Evidence-based practice (EBP), as initially defined, combines three pillars: the best available research evidence, clinical expertise, and patient values.^40,41 It also requires consideration of environmental factors (e.g., health policies) and organizational factors (e.g., available resources). For example, clinicians need time and knowledge to critically evaluate evidence and access up-to-date literature, which in turn influences EPB practice. Implementing EBP demands specific skills and resources, including the ability to appraise research for validity, relevance, and applicability, as well as access to databases and full-text articles.⁴² Numerous studies have consistently highlighted barriers to EBP implementation in physical therapy, including limited time, language barriers, limited access to evidence, insufficient statistical or appraisal skills, and low organizational support or interest.⁴² Ultimately, EBP implementation faces a crisis rooted in research design and quality, values, and clinicians' ongoing challenge of determining the best course of action for each patient in each specific moment and situation.³²

Despite these barriers, EBP has often been applied with an excessive focus on published evidence, while the roles of clinical expertise and patient context have been neglected. Even within the evidence hierarchy, questions remain: is a poorly designed RCT necessarily superior to a well-conducted cohort study? The pyramid is a useful guide, but not an absolute rule.⁴³ Quality, study design, population characteristics, feasibility, and publication timing must all be considered. Misapplication of the model becomes evident when treatments supported by strong evidence do not fit a patient’s context, preferences, or resources.

Clinical expertise—once viewed skeptically as subjective—has regained recognition as central to decision-making. Expertise involves not only technical skills, but also diagnostic reasoning, communication, prognostic judgment, and the ability to interpret evidence in context.⁴⁴ Although many interventions have been tested and shown to be effective in controlled studies, their real-world implementation remains challenging. Evidence-based interventions need to be complemented by evidence-informed, cost-effective, and context-specific implementation strategies for clinicians and policymakers.⁴⁵ A further challenge is integrating patient values and preferences. This integration requires empowering patients to participate in decision-making, have adequate health literacy, and understand their role in treatment.⁴⁶ For example, the athlete needs to understand what is going on, ask questions, and openly discuss their ideas. Most athletes are not used to having an active role in their care, so empowerment and active listening are key to incorporating patient values and implementing EBP. Only then can clinicians meaningfully engage in shared decision-making. If evidence alone continues to drive clinical action, EBP will remain incomplete. True evidence-based practice depends on the balanced integration of research evidence, clinical expertise, and patient values to create genuine patient-centered care. Without embracing the full complexity of sports injuries, EBP will be only partially useful.

The biopsychosocial model: from ingredients to the whole pancake

The biopsychosocial model was introduced to move beyond the limitations of a purely biomedical perspective, recognizing that health and recovery are influenced not only by biological but also by psychological and social factors. In theory, this model provides a holistic framework for understanding the patient as a whole person.⁴⁷ In practice, however, it often remains fragmented, failing to fully acknowledge the complexity of health-related factors. Physiotherapists and researchers continue to compartmentalize the biological, psychological, and social dimensions rather than integrating them. We also need to recognize the fluid nature of associations among domains within an individual patient over time, as captured by the concept of complex interactions.⁴⁸

As health professionals, we are deeply rooted in the biomedical model, and so are our training and systems. We are comfortable identifying structural changes, measuring strength, and quantifying movement. Our understanding of biological mechanisms is strong, supported by standardized tests and clear outcomes. We recognize that psychological, behavioral, and social aspects influence recovery, but we often lack the confidence, tools, and frameworks to assess or address them. As a result, we may acknowledge these dimensions in theory but struggle to integrate them into daily practice and recognize how such factors can directly or indirectly influence biological clinical outcomes. The pancake-making analogy makes the problem clear. To make pancakes, we need eggs, flour, and milk—all essential ingredients. But the pancake itself is not the sum of its ingredients; it is the final, integrated outcome. Similarly, we cannot reduce a patient to separate ingredients (biological, psychological, or social). Yet, in the way we often apply the biopsychosocial model, we examine each ingredient separately and forget to make the pancake.

This fragmented approach also shapes the way research is conducted. Randomized controlled trials (RCTs), still considered the gold standard in clinical research, are largely designed around biological variables. Participant inclusion criteria commonly emphasize biological features—such as age, sex, diagnosis, and symptom duration—while psychological or social characteristics are rarely defined or reported. This procedure creates a paradox: we base our interventions on data drawn from biologically homogenous but psychologically and socially diverse samples. However, fundamental differences in how patients respond to treatment often lie in the domains our research overlooks.

Recent research efforts have begun to address this gap. For example, in areas such as low back pain or tendinopathy, studies now aim to profile patients by identifying which individuals respond best to specific interventions, considering psychosocial factors.^49,50 This approach acknowledges that patients experience pain, recover from disease, and engage in treatment differently. Hertel’s models of chronic ankle instability illustrate this progression clearly. His early model focused on structural and functional deficits to understand recurrent ankle sprains.⁵¹ Later, the model expanded to include psychological and social factors, recognizing that coping strategies, fear of reinjury, and social support systems also influence clinical outcomes.⁵² Similarly, a scoping review highlighted numerous psychological and social determinants of return to sport, including motivation, self-efficacy, fear, and environmental support.^53,54 Yet, even with this growing awareness, these factors remain largely absent from RCTs and clinical decision-making. The top of the evidence pyramid remains occupied by studies that rarely capture the complexity of real-life rehabilitation.⁴³

The consequence of applying reductionist approaches is that patients are not fully seen. Returning to the pancake analogy, we focus on the ingredients, not the pancake. Their recovery is interpreted through a narrow lens that privileges biological explanations, while the psychological, behavioral, and social “ingredients” remain underexplored. To truly embrace complexity and apply the biopsychosocial model, clinicians must move from identifying separate factors to understanding how they interact within each person’s unique context. This new thinking depends on the understanding of complexity and its dynamic interactions and requires a shift in both research and clinical practice. For researchers, this means designing studies and analyses that account for psychosocial profiling and contextual variables. For clinicians, it means developing skills to explore patients’ beliefs, motivations, and social environments alongside their physical assessments. Only then can we create “the full pancake”—an integrated, person-centered approach that reflects the true complexity of health and recovery.

Embracing complexity with clarity

Methodological pluralism is essential in sports injury research, but adherence to complex thinking is equally necessary to avoid reductionist pitfalls. Over the years, several methodological approaches have been proposed to address injuries as multifactorial and emergent phenomena, each grounded in distinct assumptions about how risk factors interact. These analytical methods, when used properly, can help us to investigate the injury process with a complexity lens. They can be grouped into four broad categories: 1) statistical and predictive classification approaches (such as CART, Random Forests, Latent Profile Analysis, and neural networks), which focus on uncovering patterns and predictors; 2) causal and structured modeling approaches (including Causal Bayesian Networks and Group Model Building), which emphasize mapping causal pathways and interventions; 3) dynamic simulation approaches (System Dynamics and Agent‑Based Modeling), which explicitly capture feedback loops, adaptation, and emergent behavior; and 4) complex systems monitoring approaches (higher‑order resilience indicators and early warning signals), which track system fragility and critical transitions without requiring all causal links to be known. ach category illuminates different dimensions of injury risk, and combining their insights helps researchers respect the full complexity of sports injuries.^15,55, 56, 57

Statistical and predictive classification approaches such as CART, Random Forests, Latent Profile Analysis, and neural networks are powerful for uncovering nonlinear predictors, hidden subgroups, and complex statistical patterns in sports injury data.⁵⁷ They can highlight thresholds (e.g., training load limits) or risk clusters (e.g., the web of determinants)¹⁵ that linear models might miss, and neural networks can integrate high‑dimensional, multimodal inputs.58, 59, 60 These methods allow the inclusion of social, psychological, and behavioral factors to establish a more comprehensive interactive nexus among variables. However, they remain fundamentally reductionist, treating injuries as outcomes of the observed variables without modeling feedback loops, adaptation, or emergence. Their strength lies in prediction and exploratory discovery, but they risk oversimplifying inherently dynamic systems. These approaches are compatible with complicated systems but align only partially with complex systems thinking. Even when considering psychosocial and contextual variables, these methods depend on selecting the factors involved a priori. However, they are an advancement over traditional EBP investigative methods.

Causal and structured modeling approaches, including Causal Bayesian Networks and Group Model Building, attempt to move beyond correlation by explicitly mapping causal pathways and intervention effects. Bayesian networks clarify causal inference but are limited by their acyclic structure (no feedback loops), which cannot represent reinforcement or emergent dynamics.⁶¹ Group Model Building, by contrast, incorporates stakeholder knowledge and feedback loops, making it better suited to complex systems thinking.⁶² Together, these approaches are powerful tools for investigating injury phenomena from a biopsychosocial perspective. However, both approaches rely on presumed causal links, which may overlook hidden or unknown variables. This means that the models' efficacy depends on the researcher’s selection of meaningful variables. They are moderately compatible with complexity as they help structure understanding and intervention design, but cannot fully capture the adaptive, emergent nature of sports injuries. Although incomplete, these approaches are important for investigating presumed interactions and can be used within a biopsychosocial framework.

Dynamic simulation approaches, namely System Dynamics and Agent‑Based Modeling, are the most direct tools for embracing complexity in sports injury research. System Dynamics captures population‑level feedback loops, tipping points, and time‑dependent processes,⁶³ while Agent‑Based Modeling represents individual heterogeneity, adaptive behavior, and emergent injury clusters.⁶⁴ Together, they allow researchers to simulate how injuries arise from dynamic interactions rather than isolated risk factors. Their limitations lie in the computational demands of data processing and the need for carefully defined rules or equations, which may still miss hidden influences. Nonetheless, these methods are highly compatible with non‑reductionist thinking and represent true complex systems approaches, as they explicitly model feedback, adaptation, and emergence. Again, the researcher’s mindset will guide the discovery of the influencing factors. It is important to recognize that even powerful tools like these may yield an incomplete understanding of injury if key contextual factors are not considered.

Complex systems monitoring approaches, such as higher‑order resilience indicators and early warning signals, treat sports injuries as emergent phenomena in complex physical systems with unknown variables.²⁷ Instead of modeling every causal link, they focus on detecting fragility and critical transitions that signal an athlete is approaching a tipping point.65, 66, 67 This perspective is deeply non‑reductionist, as it acknowledges uncertainty and emphasizes system‑level resilience over isolated predictors.²⁷ Its limitation is explanatory: it can warn when the system is unstable, but not always explain why. This approach allows identification of fragile states without identifying the factors that cause them. It can detect athletes susceptible to injury without identifying the factors that directly cause this fragility. The investigator or clinician must conduct a thorough assessment, considering multiple factors, to determine the rehabilitative or preventive measure. Still, this is a truly complex systems-thinking approach, rooted in dynamical systems theory and resilience science, and consistent with calls to shift from isolated risk factors to pattern recognition in complex injury systems.^26,27 Complex system monitoring should be understood as a first step in identifying at-risk individuals, who should then be assessed multidimensionally within a biopsychosocial framework.

A complexity-compatible biopsychosocial approach can be achieved through classification, structured modeling, or dynamic simulation approaches using athlete-centered information from multidimensional assessments following monitoring. Embracing complexity in sports injury research does not mean abandoning methodological rigor; instead, it provides a richer toolbox for researchers to develop evidence to inform clinicians and physical therapists as they navigate the unpredictable realities of practice. The approaches outlined here — from predictive classification and causal mapping to dynamic simulation and resilience monitoring — each provides a distinct lens that, when critically appraised and applied with contextual sensitivity, can guide prevention and prediction strategies that respect the multifactorial nature of injuries. However, they are not the only pathways to success in sports injury rehabilitation and prevention.

Beyond quantitative methods, we also need to recognize the value of qualitative, mixed-method, and participatory research. If we acknowledge that context matters, we need to develop research that explores contextual factors.^22,68 While clinicians may have a strong understanding of structure and function, athletes are experts in their own context, so progress is impossible without including patients’ voices.^19,69 While qualitative studies have long been used to inform practice in public health, they have been used only to a limited extent in sports physical therapy research. As Greenhalgh et al. argue, different study designs provide complementary perspectives that help explain why an intervention has or has not had an effect in a specific population.⁷⁰ Beyond including athletes’ and stakeholders’ voices in qualitative studies, the next step is to involve them as partners in the research process.⁷¹ Participatory research is a growing field across health-related disciplines, but it remains underutilized in sports physical therapy. Examples of injury prevention include athletes and coaches collaborating to design interventions, allowing them to speak up and provide feedback to tailor programs to their needs and realities.^72,73 The main value of this approach is that, by including athletes and stakeholders, the proposed solutions align with real-world needs, as users know their needs and potential challenges in applying or receiving interventions.

The methodologies discussed help incorporate context more effectively: predictive models highlight how risk factors vary across populations; causal and participatory approaches embed stakeholder perspectives; dynamic simulations capture how individual and team behaviors evolve over time; resilience monitoring reflects the shifting fragility of athletes in real‑world environments; and qualitative mixed methods allow the better understanding of contextual factors and the inclusion of athletes and coaches in planning treatment and preventive strategies. Clinicians, therefore, need critical appraisal skills and contextual awareness to select and adapt methods that fit their practice. The future lies in integrating these perspectives, combining data‑driven discovery with system‑level insight, so that practitioners can anticipate transitions, tailor interventions, and foster resilience rather than simply react to isolated risk factors. By fully embracing complexity and context, the field can move toward more adaptive, personalized, and effective injury prevention strategies—a direction that empowers clinicians to act with clarity amid uncertainty.

Clinical implications

Injuries arise from nonlinear interactions among biological, psychological, and social determinants that vary over time and across contexts, making linear, reductionist explanations insufficient. By adopting a complexity-informed perspective, clinicians can better interpret variability, anticipate changes, and adapt interventions to the evolving realities of sport. Clinicians must critically appraise and interpret research evidence alongside clinical expertise and patient values, acknowledging that findings from controlled environments rarely translate directly to real-world settings. Effective rehabilitation requires clinicians to move beyond isolated variables and engage with the athlete’s lived experience, motivations, constraints, and support systems as expected in a proper biopsychosocial model. Integrating these dimensions into assessment and intervention enhances therapeutic success, improves adherence, and increases contextual relevance, supporting a successful athlete-centered approach (Fig. 1).

Fig. 1.

Evidence-based practice and the biopsychosocial models must recognize the complex, interactive, non-linear dynamics of sports injury rehabilitation and incorporate them into a broader complexity perspective to effectively provide patient-centered, context-sensitive, and individualized care.

Conclusion

Recognizing sports injuries as complex, dynamic phenomena is essential for advancing prevention and rehabilitation. This shift strengthens clinical reasoning and positions complexity as a foundational lens for contemporary physical therapy practice. Evidence-based practice must be applied as an integrative, contextually grounded framework rather than as a rigid hierarchy of study designs. When these components are integrated, EBP becomes a practical and flexible guide for decision-making in complex clinical environments. Finally, the biopsychosocial model further reinforces the need for holistic, patient-centered care by emphasizing that biological, psychological, and social factors are inseparable in shaping injury, recovery, and performance. Together, complexity thinking, evidence-based practice, and the biopsychosocial model converge toward a single imperative: clinicians must deliver care that is individualized, context-sensitive, and responsive to the dynamic nature of human health and sport.

Funding

This work was partially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq – grant number 401976/2025-4.

Declaration of competing interest

The author(s) declared no potential conflicts of interest.