Skip to main content

A Framework for Clinicians to Improve the Decision-Making Process in Return to Sport


Return-to-sport (RTS) decisions are critical to clinical sports medicine and are often characterised by uncertainties, such as re-injury risk, time pressure induced by competition schedule and social stress from coaches, families and supporters. RTS decisions have implications not only for the health and performance of an athlete, but also the sports organisation. RTS decision-making is a complex process, which relies on evaluating multiple biopsychosocial factors, and is influenced by contextual factors. In this narrative review, we outline how RTS decision-making of clinicians could be evaluated from a decision analysis perspective. To begin with, the RTS decision could be explained as a sequence of steps, with a decision basis as the core component. We first elucidate the methodological considerations in gathering information from RTS tests. Second, we identify how decision-making frameworks have evolved and adapt decision-making theories to the RTS context. Third, we discuss the preferences and perspectives of the athlete, performance coach and manager. We conclude by proposing a framework for clinicians to improve the quality of RTS decisions and make recommendations for daily practice and research.

Key Points

  • RTS decisions are complex, nonlinear and multifactorial and thus require external tools to assist practitioners

  • To improve the quality of decisions in sports settings, decision-makers could evaluate the following three domains: (1) assess the methodological soundness of the tests chosen, (2) identify potential deviations from normative decision models and (3) implement shared decision-making.


Decision-making is a process of weighing the risk(s) and benefit(s) among options to make a choice [1]. In clinical practice, return-to-sport (RTS) decisions can be challenging as they are directly linked to the athlete’s well-being and performance. RTS refers to the recovery and rehabilitation continuum: return to participation, return to sport and return to performance [2]. This review focuses on how the quality of RTS decisions could improve.

Premature RTS may risk re-injury [3,4,5] and subsequently harm the athlete’s playing performance [6], financial income [7] and mental health [8, 9]. Yet, if RTS is delayed for a lesser chance of reinjury, it will inevitably reduce a team’s player availability. Lower player availability is undesirable as players’ match availability is associated with team performance across various sports [10,11,12,13,14,15,16]. Consequently, substantial pressure rests on the shoulders of decision-makers to reach a decision that balances the best interest of the athlete’s health and performance.

When the context is predictable and routine, for example when managing a tibia fracture on the field, decision-making could be straightforward and relegated to an automated level (i.e., remove from play immediately). However, when there is a high level of uncertainty and complexity in the context (e.g., to decide whether an athlete at 95% of recovery should play in the grand final), the ability to make high-quality decisions is less clear, yet potentially even more crucial.

The challenge of complexity and the multifactorial nature of RTS decision-making has been acknowledged for over two decades [17]. A 1998 review by Putukian [17] discussed the concerns and struggles that clinicians have when making RTS decisions, which could be attributed to the multifactorial nature and clinical uncertainty presented in medicine [18, 19]. The majority of the research focus since then has been mostly on developing decision-making frameworks and clinical criteria for RTS. One of the most recognised decision-making frameworks is the Strategic Assessment of Risk and Risk Tolerance (StAART) [20]. The framework, together with the RTS criteria, helps to guide a clinicians’ practice. For example, in the management of anterior cruciate ligament (ACL) injury, clinicians may refer to the established RTS criteria [21, 22] and consensus statements [23, 24].

In contrast to the vast literature on RTS criteria, there is less on how clinicians make RTS decisions and how to improve the quality of the decision. This may be because this topic spans at least two distinct fields: sports medicine and decision-making science. We aim to help clinicians conceptualise the decision-making process, increase the thoughtfulness of a decision, identify potential deviations from normative decision models and eventually establish a framework to improve the quality of decision-making.

Disentangling Decisions and Outcomes

The term decision refers to the action taken to reach a decision, and this is different from the outcome of the decision [25, 26]. A high-quality decision refers to a decision that is logical and made based on the uncertainties, values and preferences of the decision-maker [27]. A good outcome is an outcome that the decision-maker would wish to have happened and is of high value to them [27].

A high-quality decision does not necessarily warrant a good outcome due to uncertainties. There are multiple sources of uncertainties, and the two major categories prominent in the medical field are aleatoric uncertainty and epistemic uncertainty [28]. Aleatoric uncertainty is intrinsic to the problem, for example, random variations that arise from observers or instruments. Epistemic uncertainty is extrinsic and comes from limitations in knowledge, such as individual bias [28].

Distinguishing between decision and outcome allows clinicians to separate action from the consequence, so they can focus on improving the quality of the action. Occasionally, clinicians may be disappointed by a bad outcome of a good RTS decision, such as an athlete suffering from a re-injury despite careful medical evaluation. Yet, in the pursuit of a good outcome, there may not be a better way than striving for a high-quality decision. Therefore, in this paper, we focus on evaluating the decision, and not on the outcome.

Evaluating a Decision

There are various ways to evaluate a decision. The first approach is related to the outcome of the decision, such as clinical health outcomes (e.g., pain, quality of life), or how regretful or satisfied the patient is with the decision [29,30,31]. However, there is no consensus on the optimal measurement tool(s) for this purpose. The second approach relates to the expected value of the outcome (i.e., expected utility), where probabilistic information about the risk and benefits of personal preferences and values is considered [32]. The third approach is to consider the decision quality, which is measured by knowledge of the options and outcomes, realistic perceptions of outcome probabilities, and agreement between patients’ values and choices [29].

It may be challenging to measure the quality of a decision with the first two approaches (i.e., outcome and expected utility) due to the complexity of a RTS question. Nevertheless, it may be possible to evaluate the decision with the third approach—decision quality.

Decision analysis is a formal procedure for analysing decision problems by balancing the factors that could influence a decision [27]. To evaluate the decision quality, the decision process could be made transparent by first breaking it down into a sequence of clear steps. We have adapted a decision analysis model from Howard [33] for RTS to systematically evaluate a decision (Fig. 1).

Fig. 1
figure 1

Steps for evaluating a RTS decision

The essence of decision analysis is eliciting the four bases for the decision [33]:

  1. 1.

    The alternatives relates to the options that a decision-maker has. In the context of RTS after an injury, it could be whether the athlete could return to full training/competition, modified training or basic rehabilitation training.

  2. 2.

    The information refers to knowledge that may be important to formulate the outcome. For example, what information do RTS tests provide to the decision-makers?

  3. 3.

    The decision models include models that describe how the decision could be made. That is, on what basis can the decision be made?

  4. 4.

    The preferences of a decision-maker could be of multiple dimensions. These include the value (e.g., how much does RTS mean to the athlete or the team?), time preference (e.g., how important is it to play in the upcoming game?) and risk preference (e.g., how much re-injury risk can the team tolerate?).

Among the four key bases for a decision (alternatives, information, decision models and preferences), the alternatives are highly specific to the context and would be difficult to discuss from a broader perspective. Therefore, we have structured this review around the other three bases for a RTS decision: (1) information, (2) decision models and (3) preferences. We first zoom in to the methodological issues of obtaining information in the medical room. Second, we zoom out to identify the decision models relevant to RTS. Third, we discuss how preferences can be addressed with shared decision-making. Finally, we propose a framework to improve RTS decision-making in practice.

To increase the practicability of the framework and to help readers navigate the three bases for the RTS decision, a case scenario describing an ACL injury is used. We use ACL injury because it is a serious injury in sports that may threaten the career of athletes [6, 34]. Multiple clinical and performance tests have been developed to evaluate the readiness of the RTS [35], yet the re-injury risk of ACL remains high [36, 37] and some athletes do not return to sports following the injury [38].

Part 1: Methodological Concerns in Information Gathering

A football player, in her early career, has undergone an ACL reconstruction surgery six months ago and is eager to return to play. She wants to play as soon as possible to gain a contract extension but is also worried about getting reinjured. In the medical room, you sit with the player and decide on what kind of test to perform on-field and off-field.

At the operational level, there are methodological considerations when gathering information for the decision. Below we discuss some of the underlying assumptions and methods concerns.

Number of Criteria Used in RTS

In general, criteria-based RTS (e.g., muscle strength, functional and dynamic stability, and range of motion) have been suggested over a time-frame approach, which is to decide solely based on the athlete’s time spent in rehabilitation [39,40,41,42,43]. The ideal number of tests to use for this purpose may vary between cases. There are concerns that an insufficient number of tests may jeopardise the clinician’s ability to see the complete profile of an injured athlete. However, too many tests may increase the inherent error (e.g., athlete exhibiting reduced performance due to fatigue or reduced motivation) and exhaust more resources (e.g., staff, time, equipment). Currently, there is no recommendation for the ideal combination and number of tests to provide the most insight into the athlete’s readiness for RTS.

Baseline Setting in RTS

Returning to pre-injury levels of health and fitness is often seen as the goal of RTS [2]. Therefore, setting an appropriate baseline provides an ideal foundation for clinicians to monitor progress by comparing the current functional and physical capacity of the athlete with previous preinjury data. However, it is challenging to set a baseline that is objective, replicable and suitable for the setting. For example, currently, there is no guideline on the timing and frequency for performing baseline tests. Adding more complexity to the problem, physiological and performance profiles often fluctuate daily due to periodisation in training and competition schedule (e.g., heart rate variability [44], musculoskeletal screening scores [45], hip strength and flexibility [46] and power (as in countermovement jump) [44].)

Here we used the limb symmetry index (LSI) as an example to illustrate the concerns with baseline setting. LSI is often included in the RTS protocol for ACL injury [22, 47,48,49,50]. LSI compares the performance of the involved limb with the uninvolved limb [51]. Often, a 90% side-to-side difference threshold is used as a passing score for RTS [47,48,49,50]. However, there is little scientific evidence on the optimal threshold. Even when limb symmetry is achieved, it does not necessarily indicate the athlete has reached a level sufficient for safe sports participation and performance [50, 52]. It is also questionable whether the uninvolved side could be used as the benchmark when pre-injury data are not available. After ACL reconstruction surgery, patients have reduced single-leg hop performance of both the involved and uninvolved sides [52, 53] and for up to 2 years after surgery [54]. This could be attributed to a combination of factors, such as deconditioning, fear or lack of motivation [54]. Consequently, defining the baseline measure for comparison remains a challenge and a suite of RTS tests have been recommended [2].

Validity of RTS Tests

Content Validity

Content validity refers to how well a test protocol reflects what it intends to measure [55, 56]. Selecting measurement tools is important as unnecessary noise may dampen the accuracy of the decision model. If the tests selected are prone to false positives, clinicians may be unnecessarily delaying the rehabilitation process of the athlete [47].

Traditionally, in RTS decisions, clinicians would consider internal athlete data (e.g., physical fitness, strength, well-being, periodic health-screening, body-mass, anthropometric, internal load responses) and external factors such as training loads (e.g., running performance, training and match exposure), the timing in the season, and the importance of the game or training. However, there seems to be a bias towards assessing variables that are easily measured, and missing measures that may be important, but more difficult to measure [57]. For example, in the rehabilitation of an ACL injury, a clinician may assess the hip, knee and ankle joint alignment in jump and land testing to identify the extent of valgus or varus movement. The assessment may provide valuable information regarding movement strategies and physical capabilities of the athlete; however, it may not provide sufficient information regarding the performance in competition. In competition, an athlete may encounter different chaotic and unpredictable scenarios, such as unplanned movement tasks and under high opponent pressure and cognitive load. Despite the best intentions to design testing to be sports specific, the overall physical, psychological and emotional demands of a competitive match could be hard to replicate. Consequently, decision-makers may need to identify the content validity of the test and decide to interpret the test result.

Predictive Validity

Predictive validity is how well a test predicts performance on a criterion that is administered at a later date, such as RTS outcome [56, 58]. Predictive validity is only available for some of the tests such as hop tests [47, 59], single-leg bridge test [60] and psychological readiness test [61]. For most RTS tests, clinicians may not know whether passing the test means the athlete could achieve a satisfactory RTS outcome or not. In a recent study, there was no association between the predetermined functional performance test cut-offs and the risk of a new ACL injury [62]. Similar, the Landing Error Scoring System may not predict the ACL injury risk in a cohort of high school and college athletes [63].

Responsiveness of RTS Test

Responsiveness, or sensitivity, refers to how well a test can detect meaningful changes in skill and functional assessment [55]. While it is important to track progress, recent evidence suggested that some common clinical tests cannot accurately track meaningful gains in biological and functional recovery after injury [64,65,66]. The time to normalise also differs. For example, in lower-limb injury assessment, 6-m timed hop test returned to normal earlier than the other three single-leg hop tests (single hop for distance, triple hop for distance and crossover hop for distance) [47]. Similarly, in hamstrings strain rehabilitation, straight leg raise returned to full at an early stage as compared to maximum hip flexion with active knee extension [64]. Limited literature is available to inform what tests are most suitable for informing treatment progression and rehabilitation progression [64].

Meaningful Change in RTS Test Result

One of the purposes of conducting RTS tests is to assess the progression made in rehabilitation and to inform the RTS decision [2]. Statistical tests could identify whether the observed change in a particular RTS is due to true difference or the result of chance. The statistical tests, however, in isolation cannot indicate whether the change was clinically meaningful or could be reliably distinguished from random error in the measurement [67]. As such, there is a concept of “clinical significance” to describe whether the change is both noticeable and meaningful to the injured athlete. The clinically important difference refers to the difference in an outcome measure that is clinically meaningful [68]. For example, the smallest change required to detect a meaningful change beyond typical error for 6-m timed hop test is 12.96% [69]. For RTS tests where the data for meaningful change are unavailable, longitudinal tracking may help to identify a trajectory for an informed decision [47].

Unknown Interaction Between Variables

In decision-making, there may be some pieces of information missing, whether known or unknown. For example, little is known about the linearity of soft tissue healing [70] or how compensation movement makes up quantitative symmetry (e.g., reaction and response time). There are also variables that a clinician may have not measured (e.g., knee movement in the worst chaotic scenario) or could not be measured (e.g., knee movement in an unplanned body contact or under extreme fatigue). The lack of measurement of cognitive load and sports-specific stimulus in rehabilitation may also expose a potential flaw in RTS decision-making [57].

Part 2: Zoom Out to Identify the Decision-Making Framework and Theories

You have gathered the information required and are deciding your stance on whether the athlete is suitable to return to play.

After gathering the information, here we zoom out to a broader perspective on decision-making models relevant to RTS. We first discuss a conventional RTS decision-making framework, then introduce the normative and descriptive decision models (Fig. 2). This allows clinicians an opportunity to see how a fully rational person may decide (normative models) and to explain when the decision could deviate from the norm (i.e., descriptive models).

Fig. 2
figure 2

Overview of decision frameworks and theories

RTS Decision-Making Frameworks

In 2010, Clover and Wall [71] introduced a guideline for RTS decision-making. They proposed considerations for clinical factors and functional athletic ability. Intangible factors for RTS are also included, such as motivation of the athlete, social support, psychological readiness, fear of reinjury, insurance coverage and availability of rehabilitation team [71].

The first formal RTS decision-making guiding framework, a 3-step decision-based model, was proposed by Creighton et al. in 2010 [72]. The framework was designed to guide decisions on when to clear an athlete for full participation in sport without restriction. In 2015, minor revisions were made to the 3-step framework and it was renamed the Strategic Assessment of Risk and Risk Tolerance (StARRT) [20].

The StARRT has been used to clarify the components within and the sequence of decision-making and could help to explain the hidden assumptions that clinicians make in different clinical vignettes.

The process has three steps [72]:

  • Step 1 Evaluating health status. The health status of the athlete is evaluated through medical factors, such as symptoms, medical history, clinical objective tests and severity of the injury.

  • Step 2 Evaluating participation risk. The risk of participation is evaluated through the sport risk modifiers, such as the type of injury or illness, age, types of sports, level of play, the significance of upcoming competition, social factors and financial cost.

  • Step 3 Risk tolerance modifiers. The final step to RTS decision is a risk–benefit assessment by assessing the risk tolerance modifiers. These modifiers can exist at multiple levels (e.g., individual, interpersonal, organisational, community and policy levels) and may shift the decision-makers’ priorities and preferences. As a result, RTS decision-making could be more complicated than just a medical case.

The framework has helped make the decision-making process transparent by guiding the key variables that the clinician could consider [73]. However, the StARRT does not intend to define or guide a high-quality decision-making process. In the next section, various decision-making theories are introduced in an attempt to explain the decision-making process. Examples are provided to illustrate some of the methods by which a RTS decision could be reached.

Decision-Making Theories

In decision-making, normative models and descriptive models form the two fundamental branches of decision theory [74]. Normative models are the system of rules and standards for decision-making (i.e., how one should make decisions). They have theoretical value and concerns about how to make the best possible decision when a person is fully rational and informed [74].

In contrast, descriptive models are psychological theory that explains how people actually make judgements and decisions [75]. Due to human behaviour, conflict occurs between how we would like to reason (normative) and our temptation (descriptive) of taking a faster or easier route in cognitive thinking. Descriptive models attempt to understand and explain the deviations from normative models. Here we use an example to illustrate the difference between normative and descriptive approaches: an athlete with an injury may know that alcohol could dampen recovery (a normative model explains what the athlete should do). Despite this, the athlete may still choose to drink at a party due to various reasons (a descriptive model explains why the athlete behaviour deviated from the normative model).

By comparing descriptive models to normative models, decision-makers may identify the potential deviations from normative models and correct the deviations if necessary. The section starts with normative models and is followed by descriptive models.

Normative Models

Common normative models include rule-based theory and explicit utility theory.

Rule-Based Theory

The rule-based approach is where a clinician decides based on a set of defined criteria [21, 22]. The assessment could be done on a binary scale (i.e., pass or fail). Table 1 illustrates a hypothetical example using established criteria for ACL injury [22]. Here we assume the relative importance and value assigned for all attributes are the same. The set of criteria includes seven tests, incorporating both function and subjective outcomes to reflect the overall knee performance. The passing criterion for RTS is to score > 90% on the seven tests [22].

Table 1 Hypothetical example of RTS criteria assessment, with criteria based on Grindem et al. [22] A tick suggests that the athlete has scored > 90% on that test, while a cross represents < 90%


In scenario 1, the athlete scored above 90% on all tests below and is cleared to RTS. In scenario 2, not all tests are passed and the athlete is not cleared to RTS (see Table 1).

Expected Utility Theory

Expected utility theory is a decision model that illustrates how one decides in uncertain conditions, based on the outcomes of different options and the probability of each outcome [76, 77]. It assumes the decision made is rational as it is based on an assessment of the cost and benefit surrounding choices [78, 79]. Under this theory, a clinician makes a decision based on the utility (a subjective value assigned by the decision-makers) of the outcomes of different options and the probability (estimated likelihood) of each outcome [76, 77]. As with other normative models, expected utility theory assumes that decision-makers are fully rational in decision-making and have access to complete information about probabilities and consequences, in terms of time, resources and knowledge [20]. Table 2 shows a hypothetical calculation of weight utility value according to the same ACL RTS guideline as above [22].

Table 2 Hypothetical calculation using arbitrary units and utility value in ACL RTS, with criteria based on Grindem et al. [22]. Limb symmetry index (LSI)


In Table 2, importance reflects how much the clinician values a specific test, and this is represented by a numerical weight. Utility value is based on the performance of the test, with 10 the highest score possible and 0 the lowest. In this case, achieving the goal of 90% LSI would correspond to a score of 10. The weight utility value is calculated by multiplying importance (numerical weight) by utility value (AU). For example, an importance of 3 and a utility value of 10 AU will give a weighted utility value of 30 AU (3 × 10AU = 30 AU). The highest possible weighted utility value in this example is 270AU and the decision is made based on the sum of the weighted utility value [80].

In scenario 1, the athlete achieved 90% on all the tests (indicated as “achieved 0.90”) and the sum of the weighted utility value is 270AU. The decision is RTS. In scenario 2, some of the tests have not passed the 90% threshold and the sum of the weighted utility value is 213AU. The weighted utility value has not reached the requirement set by the clinician, and the athlete was not cleared to RTS in scenario 2.

Descriptive Models

Because humans are unlikely to be perfectly rational at all times, decisions made could deviate from a normative model. Systematic deviations from normative models are known as biases [75]. By applying normative models to the decisions made, decision-makers could look for possible biases and understand the nature of those biases with descriptive models. Examples of descriptive models include prospect theory, heuristics and bounded rationality [74]. With a better understanding of the biases, decision-makers could develop approaches to correct them (de-bias) and improve the quality of the decisions. The following section describes the common descriptive theories and how a decision may stray from the previous normative models.

Prospect Theory

Prospect theory suggested that people consider expected utility relative to a reference point rather than the absolute outcome. It also suggested that future gains and losses are asymmetrical, with losses having a greater emotional impact than gains (i.e., humans dislike losses more than potential gains).


In Table 2, the prospect theory would suggest that the decision-maker does not necessarily make decisions based on the absolute weight utility (i.e., 270AU). Instead, they would look at how far the expected utility is relative to a reference point (which is unknown here). If we adopt prospect theory in the context of RTS, a re-injury (loss) may bring a more negative emotional impact than winning (gain). While this may not be true in all cases, it may be worth noting the potential deviations in decision-making due to emotional distress.

Bounded Rationality

Bounded rationality describes how humans take reasoning shortcuts and make decisions within the bounds imposed by the environment, ability, information and goal [81]. The decision is rational; however, it is within the limits of information available to the decision-maker. That is, due to the limitation in accessing information, people tend to make sufficient judgements, rather than optimal ones [82, 83]. (For more details, see Gigerenzer and Goldstein [81] and Robertson and Joyce [83].) In RTS, not all meaningful data are collected due to various reasons, such as high cost, a lack of feasibility and time. Therefore, the best outcome for a decision made with unknown factors is not the same as decisions made in the context of transparency [84].


In the rehabilitation of an ACL injury, some information will always be unknown due to factors such as limitations in resources. This includes how we can accurately assess the degree of healing of the ACL graft after a reconstruction surgery or measure the loading capacity of the ACL. Consequently, the decision made by the clinician in the vignette is only based on the information available in Tables 1 and 2 and is limited by the cognitive capacity, and the knowledge and choices of the decision-maker.


Also known as a cognitive short cut, a heuristic is a decision-making strategy to act more quickly or frugally by ignoring parts of the information [85]. Heuristics allow people to make a rapid, efficient judgement without consuming a substantial amount of time, processing capacity, or when information is incomplete.

Logically, a clinician’s decision for RTS would be grounded in a more rational choice as described in normative models due to availability of time and opportunity to gather additional information from test or other staff members (e.g., doctors, coaches, fitness coach). However, RTS decision-making can also be based on heuristic decision-making, as seen when athletes make decisions regarding RTS [86].

There are many types of heuristics that are used in daily life [87]. Tversky and Kahneman [88] proposed three classes of heuristics which people may rely on to assess the probabilities of an uncertain event: availability heuristic, representativeness heuristic and anchoring and adjustment heuristic. In Table 3, we have suggested examples of heuristics that may be of relevance in RTS decisions. Heuristics sometimes may be useful in reducing the complexity of a task in assessing probabilities; however, it may also lead to systematic errors [88].

Table 3 Definitions and examples of heuristics in RTS

Part 3: Preferences of the Decision-Makers

You have consolidated the information and have weighed the risk and benefits of the medical clearance. Understanding that you are bounded by the information and knowledge available, you have used the rule-based theory described in Table 1 as the basis for decision-making. Based on scenario 1, where the player has passed all of the tests, you have decided that the player is clinically fit to return to full training. Using the StARRT framework as a reference, you would like to discuss your rationale and other contextual factors with the athlete, coach and manager, to reach a shared decision.

The StARRT framework helps clinicians make RTS decisions based on whether the risk assessment outcome exceeds the decision-maker’s risk tolerance[20]. That is, if the risk assessment is lower than the risk tolerance after all factors are considered, the athlete may be cleared to RTS. However, a low risk decision may not be synonymous with a high-quality decision.

In general medicine, it is recommended that the decision made by the clinician reflects the preferences of a well-informed patient, with consideration of factual and probabilistic health information [32, 90, 91]. There are multiple dimensions to address, including characteristics of the decision, knowledge and expectations of the situation and treatment options and outcomes, personal values and preferences, support and resources needed, personal characteristics and clinical characteristics [29, 91,92,93].

Practically, there is no optimal measurement tool that can measure the quality of the RTS decision based on the performance outcome or the expected utility of the decision-makers. However, a clinician can improve the decision quality by ensuring the decisions are well-informed and grounded in a shared decision-making approach.

Improving Decision Quality by Shared Decision-Making

Shared decision-making has been a best practice for decision-making in the field of medicine [2, 94, 95]. It respects multiple perspectives and also aims to minimise disagreement due to conflicting interests.

Two phases characterise shared decision-making: 1) deliberation (pre-decisional, the process leading to a decision) and 2) determination (the act of decision) [96]. Deliberation is where knowledge is searched for, gained and appraised. To improve the quality of the shared decision, both the deliberation and determination could be evaluated [96]. An accurate judgment requires stakeholders to first collaborate to decide on the definition of success [2, 97]. Then decide on which pieces of information to pay attention to, nominate weighting and integrate the information [98]. This information may include the alternatives available, the advantages and disadvantages of the alternatives, the nature of the decision, the associated outcome and its likelihood [94, 96].

The second phase, determination, is to choose one of the options [96]. The actual decision may occur in a ‘black box’, where one combines the available information in their own way without transparency or accountability [99]. The lens decides how one interprets the “real” probabilities, which could be obscured by one’s cognitive and emotional influence. For example, how an athlete weighs the importance of his or her sports career may affect how the information is processed (Fig. 3).

Fig. 3
figure 3

Shared decision model in sports. Adapted to RTS context from Elwyn et al.[94]

Understanding the decision-making theories may allow decision-makers to realise the normative approach and thus engage in a high-quality and rational discussion during deliberation.

The Perspectives of Decision-Makers

The keys to high-quality decision-making include accounting for individual preferences, social and contextual factors (e.g., the type of injury or illness, age, types of sports, level of play, the significance of upcoming competition and social factors and financial cost) [2, 32, 100, 101]. Social and contextual factors also impose constraints at multiple levels and influence the RTS decision, including at individual, interpersonal, organisational, community and policy levels [72, 73, 102]. The factors may shift the athlete’s and decision-makers’ priorities and preferences, which make decision-making more complicated [20, 72].

Traditionally, clinicians are the gatekeeper of the RTS decision [71, 103,104,105,106,107]. The clinician has skills in assessing the injury-related criteria in RTS, including assessing the state of healing, risk of re-injury and risk of short- or long-term problems [96, 104, 108, 109]. Clinicians also have an overriding duty of care to patients and a legal and ethical obligation to act in a manner that is necessary and appropriate to protect the health of an athlete.

However, with the addition of trainers, rehabilitation coaches and performance coaches, clinicians are no longer the only staff contributing to rehabilitation and RTS decisions. It is questionable whether clinicians should still be the main advisor for RTS decisions, given the numerous non-medical factors to consider [97, 100, 103, 108, 110,111,112,113]. In a sports setting, a clinician may even have dual allegiances, as the clinician does not work exclusively for the patient, but also on behalf of the club or organisation. They may experience pressure from their employer (i.e., the sports organisation) to minimise lay-off time and to clear an athlete as soon as possible. As such, an inherent conflict of interest may present in a professional sports team setting [114, 115].

The following section discusses the general concerns and considerations of the athlete and coaches to improve communication transparency and to minimise conflicts.


There are internal and external factors influencing how an athlete may view the quality of the decision and listening to their opinions may be beneficial to inform the final decision [101]. Internal factors include perception of body, self-resentment [116, 117] and their emotional tie to their sport [117]. External factors include sociocultural influences, such as financial concerns, expectations from family and friends and their given sport’s culture of risk [118, 119]. Some athletes may face social pressure to perform [118]. Social pressure could be the pressure to meet the expectations of peers, fans and coaches [116, 117, 120,121,122]. Shame and alienation from the team due to injury may lead to low self-esteem and depression [120, 122, 123].

There is limited evidence on how athletes approach decisions about RTS, especially in a complex and risky scenario. ‘Playing hurt’ is a common phenomenon across different sports, age groups and performance levels [117,118,119, 124, 125]. However, it is unclear how and when an athlete would choose to play hurt.

In a recent study that investigated how athletes decide on RTS [86], athletes would consider the relevance of the competition (e.g., the importance of the competition), potential sporting consequences (e.g., loss of the starting position) and whether the risk of playing hurt could be offset by some means (e.g., availability of protective gear or possibility of being removed from play if pain increases). If the medically safe alternative (e.g., withdrawal from competition) does not have severe sporting consequences (e.g., loss of starting position), the athlete may opt for it. In contrast, if playing hurt may produce a sporting consequence that the athlete cannot afford but the risk of playing could be subjectively reduced, they may choose to play hurt. Clinicians and coaches can be influential in the athlete’s decision-making as clinicians and coaches are likely to know about the sporting consequences and the possibility of risk reduction.

As opposed to the risk analysis suggested in the normative StARRT framework [20], not all athletes attempt to obtain information actively and comprehensively [86]. Therefore, it may be helpful for clinicians and coaches to guide athletes through the information seeking process and provide a full picture of the situation and the sporting consequence.

Performance Coach and Manager

In some settings, coaches and managers could be the decision-makers for RTS, and thus, it is important to have their perspective as well. Coaches and managers are competent in assessing the non-injury-related RTS criteria, such as the athlete’s desire to compete, psychological impact, financial consideration and loss of competitive standing [108].

Based on existing literature, some coaches believe they have a responsibility to push the athlete to their limits, mentally and physically to achieve excellence in performance [126]. While some coaches act according to the training restriction implemented to reduce injury risk [122], some perceive prolonged or delayed RTS as harmful to the overall and long-term performance of the athlete [122]. Some coaches also believe clinicians are overly cautious and delay RTS of athletes unnecessarily [122]. However, research is scarce and based on small sample size, thus limiting generalisability.

To facilitate rehabilitation, coaches and managers may help to remove the barriers arising from the social and environmental context [127]. For example, they can ensure that athletes have sufficient resources to access adequate supervised rehabilitation. Coaches and managers can also ensure all relevant personnel are provided with information regarding the injury and the rehabilitation progression. These actions may increase transparency in communication and facilitate the decision to include or exclude from the main training group [127].

There are times when clinicians might miss something important without realising it. Shared decision-making may help to minimise the blind spots by filling the missing gaps and broadening the perspectives.

Practical Implication

Based on a decision analysis model, we have outlined a framework to help clinicians make systematic and objective RTS decisions. The first step is to choose appropriate RTS tests and to synthesise the information in a meaningful way. The second step is to understand the decision-making theories and identify possible deviations from normative models. The third step is using shared decision-making to improve decision quality by eliminating the contextual ‘blind spots’, such as an individual’s expectation, preference and value. We propose a framework that clinicians could refer to when they decide on RTS in a sports organisation (Fig. 4).

Fig. 4
figure 4

Three steps to making a high-quality RTS decision

Future Research

Currently, there is limited evidence or expert knowledge on how clinical decisions in sports are made, especially for upper-limb injuries. While in principle, the decision-making process of other sports injuries would be similar, future research could also investigate upper-limb injuries, for example, a shoulder dislocation injury. Similarly, there is little attention paid to how heuristics may be present in sports medicine practice. Research is needed to identify the heuristics used in clinical practice as limited work has been done in the field. Strategies for better judgment and decisions, such as reducing bias, are also required.

Another concern is the increasing number of data types with the growth of sports technology. There is a certain point where additional information no longer improves a human’s ability to make better decisions [128]. The human mind has an upper limit for information processing capacity and is sufficiency sensitive to large inconsistencies, but not small ones [129, 130]. Providing more information than the upper limit would only exhaust one’s cognitive information capacity in decision-making, potentially leading to overload, poor decision-making, and dysfunctional performance [131]. Consequently, there is an urge to identify tools that aid human brains in making decisions.

Examples of these decision-making tools could be statistics, mathematical modelling and artificial intelligence (AI) algorithms. In particular, machine learning techniques, a subfield of AI, attracted attention for their strength to transform a large amount of data into useful knowledge and identify nonlinear patterns [132,133,134]. In many cases, these external aids may complement or be superior to human performance [135,136,137]. Currently, the application of the above tools mostly remains on the theoretical level. Future research may explore how these tools may be applied on a practical level.


The purpose of this review was to provide an overview of RTS decision frameworks and what constitutes high-quality decision-making. There is a lack of empirical knowledge in RTS decision-making and the potential adaptations within its process; most research focuses on biological and medical factors. One of the strengths of the review is to lay out the decision basis and hence the transparency of a decision. Understanding decision-making theories in the context of RTS and potential deviations from normative decisions may improve the work process and quality of decision-making. More research is required to understand how decisions are made and how to use computation tools to support and improve decision quality.

Availability of Data and Materials

Not applicable.



Artificial intelligence


Anterior cruciate ligament


Limb symmetry index




The Strategic Assessment of Risk and Risk Tolerance


  1. Burton L, Westen D, Kowalski R. Thought and language. Psychology. 2nd ed. Stafford: Wiley; 2009. p. 285–330.

    Google Scholar 

  2. Ardern CL, Glasgow P, Schneiders A, Witvrouw E, Clarsen B, Cools A, et al. 2016 Consensus statement on return to sport from the First World Congress in Sports Physical Therapy. Bern Br J Sports Med. 2016;50(14):853–64.

    Article  PubMed  Google Scholar 

  3. Stares J, Dawson B, Peeling P, Drew M, Heasman J, Rogalski B, et al. How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury. J Sci Med Sport. 2018;21(10):1019–24.

    Article  PubMed  Google Scholar 

  4. Stares JJ, Dawson B, Peeling P, Heasman J, Rogalski B, Fahey-Gilmour J, et al. Subsequent injury risk is elevated above baseline after return to play: a 5-year prospective study in elite Australian Football. Am J Sports Med. 2019;47(9):2225–31.

    Article  PubMed  Google Scholar 

  5. Hägglund M, Waldén M, Ekstrand J. Injury recurrence is lower at the highest professional football level than at national and amateur levels: does sports medicine and sports physiotherapy deliver? Br J Sports Med. 2016;50(12):751–8.

    Article  PubMed  Google Scholar 

  6. Walden M, Hagglund M, Magnusson H, Ekstrand J. ACL injuries in men’s professional football: a 15-year prospective study on time trends and return-to-play rates reveals only 65% of players still play at the top level 3 years after ACL rupture. Br J Sports Med. 2016;50(12):744–50.

    Article  PubMed  Google Scholar 

  7. Secrist ES, Bhat SB, Dodson CC. The financial and professional impact of anterior cruciate ligament injuries in National Football League Athletes. Orthop J Sports Med. 2016;4(8):2325967116663921.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gouttebarge V, Aoki H, Ekstrand J, Verhagen EALM, Kerkhoffs GMMJ. Are severe musculoskeletal injuries associated with symptoms of common mental disorders among male European professional footballers? Knee Surg Sports Traumatol Arthrosc. 2016;24(12):3934–42.

    Article  PubMed  Google Scholar 

  9. Ruddock-Hudson M, O’Halloran P, Murphy G. Exploring psychological reactions to injury in the Australian Football League (AFL). J Appl Sport Psychol. 2012;24(4):375–90.

    Google Scholar 

  10. Hägglund M, Waldén M, Magnusson H, Kristenson K, Bengtsson H, Ekstrand J. Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study. Br J Sports Med. 2013;47(12):738–42.

    Article  PubMed  Google Scholar 

  11. Drew MK, Raysmith BP, Charlton PC. Injuries impair the chance of successful performance by sportspeople: a systematic review. Br J Sports Med. 2017;51(16):1209–14.

    Article  PubMed  Google Scholar 

  12. Hoffman DT, Dwyer DB, Bowe SJ, et al. Is injury associated with team performance in elite Australian football? 20 years of player injury and team performance data that include measures of individual player value. Br J Sports Med. 2020;54:475–9.

    Article  PubMed  Google Scholar 

  13. Arnason A, Sigurdsson SB, Gudmundsson A, Holme I, Engebretsen L, Bahr R. Physical fitness, injuries, and team performance in soccer. Med Sci Sports Exerc. 2004;36(2):278–85.

    Article  PubMed  Google Scholar 

  14. Emery CA, Kang J, Schneider KJ, Meeuwisse WH. Risk of injury and concussion associated with team performance and penalty minutes in competitive youth ice hockey. Br J Sports Med. 2011;45(16):1289–93.

    Article  PubMed  Google Scholar 

  15. Waldén M, Hägglund M, Ekstrand J. Football injuries during European Championships 2004–2005. Knee Surg Sports Traumatol Arthrosc. 2007;15(9):1155–62.

    Article  PubMed  Google Scholar 

  16. Podlog L, Buhler CF, Pollack H, Hopkins PN, Burgess PR. Time trends for injuries and illness, and their relation to performance in the National Basketball Association. J Sci Med Sport. 2015;18(3):278–82.

    Article  PubMed  Google Scholar 

  17. Putukian M. Return to play: making the tough decisions. Phys Sportsmed. 1998;26(9):25–7.

    Article  CAS  PubMed  Google Scholar 

  18. Malcolm D. Medical uncertainty and clinician-athlete relations: the management of concussion injuries in rugby union. Sociol Sport J. 2009;26(2):191.

    Article  Google Scholar 

  19. Shrier I, Charland L, Mohtadi NGH, Meeuwisse WH, Matheson GO. The sociology of return-to-play decision making: a clinical perspective. Clin J Sport Med. 2010;20(5):333–5.

    Article  PubMed  Google Scholar 

  20. Shrier I. Strategic Assessment of Risk and Risk Tolerance (StARRT) framework for return-to-play decision-making. Br J Sports Med. 2015;49(20):1311–5.

    Article  PubMed  Google Scholar 

  21. Kyritsis P, Bahr R, Landreau P, Miladi R, Witvrouw E. Likelihood of ACL graft rupture: not meeting six clinical discharge criteria before return to sport is associated with a four times greater risk of rupture. Br J Sports Med. 2016;50(15):946–51.

    Article  PubMed  Google Scholar 

  22. Grindem H, Snyder-Mackler L, Moksnes H, Engebretsen L, Risberg MA. Simple decision rules can reduce reinjury risk by 84% after ACL reconstruction: the Delaware-Oslo ACL cohort study. Br J Sports Med. 2016;50(13):804–8.

    Article  PubMed  Google Scholar 

  23. Meredith SJ, Rauer T, Chmielewski TL, Fink C, Diermeier T, Rothrauff BB, et al. Return to sport after anterior cruciate ligament injury: panther symposium ACL injury return to sport consensus group. Orthop J Sports Med. 2020;8(6):2325967120930829.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rothrauff BB, Karlsson J, Musahl V, Irrgang JJ, Fu FH. ACL consensus on treatment, outcome, and return to sport. Knee Surg Sports Traumatol Arthrosc. 2020;28(8):2387–9.

    Article  PubMed  Google Scholar 

  25. Gass SI. Decision-aiding models: validation, assessment, and related issues for policy analysis. Oper Res. 1983;31(4):603–31.

    Article  Google Scholar 

  26. Vlek C. What constitutes “a good decision”?. A panel discussion among Ward Edwards, István Kiss, Giandomenico Majone and Masanao Toda. Acta Psychol. 1984;56:5–27.

    Article  Google Scholar 

  27. Howard RA. The foundations of decision analysis revisited. In: von Winterfeldt D, Miles RF, Edwards W, editors. Advances in decision analysis: from foundations to applications. Cambridge: Cambridge University Press; 2007. p. 32–56.

    Chapter  Google Scholar 

  28. Indrayan A. Medical uncertainties, statistical errors and data biases: a dangerous triad for empirical. Research. 2020;37:3–4.

    Google Scholar 

  29. Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Thomson R, Trevena L. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4(4):CD001431.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Holmes-Rovner M, Nelson WL, Pignone M, Elwyn G, Rovner DR, O’Connor AM, et al. Are patient decision aids the best way to improve clinical decision making? Report of the IPDAS Symposium. Med Decis Mak. 2007;27(5):599–608.

    Article  Google Scholar 

  31. Sepucha KR, Borkhoff CM, Lally J, Levin CA, Matlock DD, Ng CJ, et al. Establishing the effectiveness of patient decision aids: key constructs and measurement instruments. BMC Med Inform Decis Mak. 2013;13(2):S12.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hamilton JG, Lillie SE, Alden DL, Scherer L, Oser M, Rini C, et al. What is a good medical decision? A research agenda guided by perspectives from multiple stakeholders. J Behav Med. 2017;40(1):52–68.

    Article  PubMed  Google Scholar 

  33. Howard RA. Decision analysis: practice and promise. Manag Sci. 1988;34(6):679–95.

    Article  Google Scholar 

  34. Ekstrand J, Krutsch W, Spreco A, Van Zoest W, Roberts C, Meyer T, Bengtsson H. Time before return to play for the most common injuries in professional football: a 16-year follow-up of the UEFA Elite Club Injury Study. Br J Sports Med. 2019;54:421–6.

    Article  PubMed  Google Scholar 

  35. Webster KE, Hewett TE. Return to sport after anterior cruciate ligament reconstruction: criteria-based rehabilitation and return to sport testing. In: Nakamura N, Marx RG, Musahl V, Getgood A, Sherman SL, Verdonk P, editors. Advances in knee ligament and knee preservation surgery. Cham: Springer; 2022. p. 83–93.

    Chapter  Google Scholar 

  36. Paterno MV, Rauh MJ, Schmitt LC, Ford KR, Hewett TE. Incidence of second ACL injuries 2 years after primary ACL reconstruction and return to sport. Am J Sports Med. 2014;42(7):1567–73.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Della Villa F, Hägglund M, Della Villa S, Ekstrand J, Waldén M. High rate of second ACL injury following ACL reconstruction in male professional footballers: an updated longitudinal analysis from 118 players in the UEFA Elite Club Injury Study. Br J Sports Med. 2021;55:1379–80.

    Article  PubMed  Google Scholar 

  38. Lai CCH, Ardern CL, Feller JA, Webster KE. Eighty-three per cent of elite athletes return to preinjury sport after anterior cruciate ligament reconstruction: a systematic review with meta-analysis of return to sport rates, graft rupture rates and performance outcomes. Br J Sports Med. 2018;52(2):128–38.

    Article  PubMed  Google Scholar 

  39. Serner A, Weir A, Tol JL, Thorborg K, Lanzinger S, Otten R, et al. Return to sport after criteria-based rehabilitation of acute adductor injuries in male athletes: a prospective cohort study. Orthop J Sports Med. 2020;8(1):2325967119897247.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hickey JT, Timmins RG, Maniar N, Williams MD, Opar DA. Criteria for progressing rehabilitation and determining return-to-play clearance following hamstring strain injury: a systematic review. Sports Med. 2017;47(7):1375–87.

    Article  PubMed  Google Scholar 

  41. van der Horst N, van de Hoef S, Reurink G, Huisstede B, Backx F. Return to play after hamstring injuries: a qualitative systematic review of definitions and criteria. Sports Med. 2016;46(6):899–912.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zambaldi M, Beasley I, Rushton A. Return to play criteria after hamstring muscle injury in professional football: a Delphi consensus study. Br J Sports Med. 2017;51(16):1221–6.

    Article  PubMed  Google Scholar 

  43. Tassignon B, Verschueren J, Delahunt E, Smith M, Vicenzino B, Verhagen E, et al. Criteria-based return to sport decision-making following lateral ankle sprain injury: a systematic review and narrative synthesis. Sports Med. 2019;49(4):601–19.

    Article  PubMed  Google Scholar 

  44. Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W. Monitoring fatigue during the in-season competitive phase in Elite Soccer Players. Int J Sports Physiol Perform. 2015;10(8):958–64.

    Article  PubMed  Google Scholar 

  45. Esmaeili A, Stewart AM, Hopkins WG, Elias GP, Lazarus BH, Rowell AE, Aughey RJ. Normal variability of weekly musculoskeletal screening scores and the influence of training load across an Australian Football League Season. Front Physiol. 2018;9:144.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Paul DJ, Nassis GP, Whiteley R, Marques JB, Kenneally D, Chalabi H. Acute responses of soccer match play on hip strength and flexibility measures: potential measure of injury risk. J Sports Sci. 2014;32(13):1318–23.

    Article  PubMed  Google Scholar 

  47. Davies WT, Myer GD, Read PJ. Is it time we better understood the tests we are using for return to sport decision making following acl reconstruction? A critical review of the hop tests. Sports Med. 2019;50:485–95.

    Article  PubMed Central  Google Scholar 

  48. Fitzgerald GK, Axe MJ, Snyder-Mackler L. A decision-making scheme for returning patients to high-level activity with nonoperative treatment after anterior cruciate ligament rupture. Knee Surg Sports Traumatol Arthrosc. 2000;8(2):76–82.

    Article  CAS  PubMed  Google Scholar 

  49. Munro AG, Herrington LC. Between-session reliability of four hop tests and the agility T-test. J Strength Cond Res. 2011;25(5):1470–7.

    Article  PubMed  Google Scholar 

  50. Wellsandt E, Failla MJ, Snyder-Mackler L. Limb symmetry indexes can overestimate knee function after anterior cruciate ligament injury. J Orthop Sports Phys Ther. 2017;47(5):334–8.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Petschnig R, Baron R, Albrecht M. The relationship between isokinetic quadriceps strength test and hop tests for distance and one-legged vertical jump test following anterior cruciate ligament reconstruction. J Orthop Sports Phys Ther. 1998;28(1):23–31.

    Article  CAS  PubMed  Google Scholar 

  52. Wren TAL, Mueske NM, Brophy CH, Pace JL, Katzel MJ, Edison BR, et al. Hop distance symmetry does not indicate normal landing biomechanics in adolescent athletes with recent anterior cruciate ligament reconstruction. J Orthop Sports Phys Ther. 2018;48(8):622–9.

    Article  PubMed  Google Scholar 

  53. Gokeler A, Welling W, Benjaminse A, Lemmink K, Seil R, Zaffagnini S. A critical analysis of limb symmetry indices of hop tests in athletes after anterior cruciate ligament reconstruction: a case control study. Orthop Traumatol Surg Res. 2017;103(6):947–51.

    Article  CAS  PubMed  Google Scholar 

  54. Chung KS, Ha JK, Yeom CH, Ra HJ, Lim JW, Kwon MS, et al. Are muscle strength and function of the uninjured lower limb weakened after anterior cruciate ligament injury?:Two-year follow-up after reconstruction. Am J Sports Med. 2015;43(12):3013–21.

    Article  PubMed  Google Scholar 

  55. Robertson SJ, Burnett AF, Cochrane J. Tests examining skill outcomes in sport: a systematic review of measurement properties and feasibility. Sports Med. 2014;44(4):501–18.

    Article  PubMed  Google Scholar 

  56. Robertson S, Kremer P, Aisbett B, Tran J, Cerin E. Consensus on measurement properties and feasibility of performance tests for the exercise and sport sciences: a Delphi study. Sports Med Open. 2017;3(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Paul DJ. Reconstructing cognitive function following ACL injury. Aspetar Sports Med J. 2020;9:78–83.

    Google Scholar 

  58. Ardern CL, Taylor NF, Feller JA, Whitehead TS, Webster KE. Psychological responses matter in returning to preinjury level of sport after anterior cruciate ligament reconstruction surgery. Am J Sports Med. 2013;41(7):1549–58.

    Article  PubMed  Google Scholar 

  59. Paterno MV, Huang B, Thomas S, Hewett TE, Schmitt LC. Clinical factors that predict a second ACL injury after ACL reconstruction and return to sport: preliminary development of a clinical decision algorithm. Orthop J Sports Med. 2017;5(12):2325967117745279.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Freckleton G, Cook J, Pizzari T. The predictive validity of a single leg bridge test for hamstring injuries in Australian Rules Football Players. Br J Sports Med. 2014;48(8):713–7.

    Article  PubMed  Google Scholar 

  61. Webster KE, Feller JA. Development and validation of a short version of the anterior cruciate ligament return to sport after injury (ACL-RSI) Scale. Orthop J Sports Med. 2018;6(4):2325967118763763.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Fältström A, Hägglund M, Hedevik H, Kvist J. Poor validity of functional performance tests to predict knee injury in female soccer players with or without anterior cruciate ligament reconstruction. Am J Sports Med. 03635465211002541.

  63. Smith HC, Johnson RJ, Shultz SJ, Tourville T, Holterman LA, Slauterbeck J, et al. A prospective evaluation of the landing error scoring system (LESS) as a screening tool for anterior cruciate ligament injury risk. Am J Sports Med. 2011;40(3):521–6.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Whiteley R, van Dyk N, Wangensteen A, Hansen C. Clinical implications from daily physiotherapy examination of 131 acute hamstring injuries and their association with running speed and rehabilitation progression. Br J Sports Med. 2018;52(5):303–10.

    Article  PubMed  Google Scholar 

  65. Hegedus EJ, McDonough S, Bleakley C, Cook CE, Baxter GD. Clinician-friendly lower extremity physical performance measures in athletes: a systematic review of measurement properties and correlation with injury, part 1. The tests for knee function including the hop tests. Br J Sports Med. 2015;49(10):642–8.

    Article  PubMed  Google Scholar 

  66. Hegedus EJ, McDonough SM, Bleakley C, Baxter D, Cook CE. Clinician-friendly lower extremity physical performance tests in athletes: a systematic review of measurement properties and correlation with injury. Part 2—the tests for the hip, thigh, foot and ankle including the star excursion balance test. Br J Sports Med. 2015;49(10):649–56.

    Article  PubMed  Google Scholar 

  67. Mann BJ, Gosens T, Lyman S. Quantifying clinically significant change: a brief review of methods and presentation of a hybrid approach. Am J Sports Med. 2012;40(10):2385–93.

    Article  PubMed  Google Scholar 

  68. Katz NP, Paillard FC, Ekman E. Determining the clinical importance of treatment benefits for interventions for painful orthopedic conditions. J Orthop Surg Res. 2015;10(1):24.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Noyes FR, Barber SD, Mangine RE. Abnormal lower limb symmetry determined by function hop tests after anterior cruciate ligament rupture. Am J Sports Med. 1991;19(5):513–8.

    Article  CAS  PubMed  Google Scholar 

  70. Järvinen TA, Järvinen M, Kalimo H. Regeneration of injured skeletal muscle after the injury. Muscles Ligaments Tendons J. 2014;3(4):337–45.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Clover J, Wall J. Return-to-play criteria following sports injury. Clin Sports Med. 2010;29(1):169–75.

    Article  PubMed  Google Scholar 

  72. Creighton DW, Shrier I, Shultz R, Meeuwisse WH, Matheson GO. Return-to-play in sport: a decision-based model. Clin J Sport Med. 2010;20(5):379–85.

    Article  PubMed  Google Scholar 

  73. Shrier I, Matheson GO, Boudier-Revéret M, Steele RJ. Validating the three-step return-to-play decision model. Scand J Med Sci Sports. 2015;25(2):e231–9.

    Article  CAS  PubMed  Google Scholar 

  74. Bell DE, Raiffa H, Tversky A. Descriptive, normative, and prescriptive interactions in decision making. In: Tversky A, Bell DE, Raiffa H, editors. Decision making: descriptive, normative, and prescriptive interactions. Cambridge: Cambridge University Press; 1988. p. 9–30.

    Chapter  Google Scholar 

  75. Baron J. The point of normative models in judgment and decision making. Front Psychol. 2012;3:577.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Edwards W. How to use multiattribute utility measurement for social decisionmaking. IEEE Trans Syst Man Cybern. 1977;7(5):326–40.

    Article  Google Scholar 

  77. Connolly T, Arkes HR, Hammond KR. Multiattribute choice. Judgement and decision making. 2nd ed. Cambridge: Cambridge University Press; 1999.

    Google Scholar 

  78. Reyna VF, Rivers SE. Current theories of risk and rational decision making. Dev Rev. 2008;28(1):1–11.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ashby D, Smith AF. Evidence-based medicine as Bayesian decision-making. Stat Med. 2000;19(23):3291–305.

    Article  CAS  PubMed  Google Scholar 

  80. Barber-Westin SD, Noyes FR. Factors used to determine return to unrestricted sports activities after anterior cruciate ligament reconstruction. Arthroscopy. 2011;27(12):1697–705.

    Article  PubMed  Google Scholar 

  81. Gigerenzer G, Goldstein DG. Reasoning the fast and frugal way: models of bounded rationality. Psychol Rev. 1996;103(4):650–69.

    Article  CAS  PubMed  Google Scholar 

  82. Simon HA. A behavioral model of rational choice. Q J Econ. 1955;69(1):99–118.

    Article  Google Scholar 

  83. Robertson S, Joyce D. Bounded rationality revisited: making sense of complexity in applied sport science. SportRxiv. 2019.

  84. Gigerenzer G. Simple heuristics that make us smart. New York: Oxford University Press; 1999.

    Google Scholar 

  85. Gigerenzer G, Hertwig R, Pachur T, Oxford University P, Oxford University P. Heuristics [electronic resource]: the foundations of adaptive behavior. Oxford: Oxford University Press; 2011.

  86. Mayer J, Burgess S, Thiel A. Return-to-play decision making in team sports athletes. A quasi-naturalistic scenario study. Front Psychol. 2020;11:1020.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Gigerenzer G, Gaissmaier W. Heuristic decision making. Annu Rev Psychol. 2011;62(1):451–82.

    Article  PubMed  Google Scholar 

  88. Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and biases. Science. 1974;185(4157):1124–31.

    Article  CAS  PubMed  Google Scholar 

  89. Nilstad A, Petushek E, Mok KM, Bahr R, Krosshaug T. Kiss goodbye to the 'kissing knees': no association between frontal plane inward knee motion and risk of future non-contact ACL injury in elite female athletes. Sports Biomech. 2021.

  90. Sepucha K, Ozanne E, Silvia K, Partridge A, Mulley AG Jr. An approach to measuring the quality of breast cancer decisions. Patient Educ Couns. 2007;65(2):261–9.

    Article  PubMed  Google Scholar 

  91. Marteau TM, Dormandy E, Michie S. A measure of informed choice. Health Expect. 2001;4(2):99–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Jaffray JY, Wakker P. Decision making with belief functions: compatibility and incompatibility with the sure-thing principle. J Risk Uncertain. 1993;7(3):255–71.

    Article  Google Scholar 

  93. O’Connor AM, Tugwell P, Wells GA, Elmslie T, Jolly E, Hollingworth G, et al. A decision aid for women considering hormone therapy after menopause: decision support framework and evaluation. Patient Educ Couns. 1998;33(3):267–79.

    Article  CAS  PubMed  Google Scholar 

  94. Elwyn G, Frosch D, Thomson R, Joseph-Williams N, Lloyd A, Kinnersley P, et al. Shared decision making: a model for clinical practice. J Gen Intern Med. 2012;27(10):1361–7.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Barry MJ, Edgman-Levitan S. Shared decision making—the pinnacle of patient-centered care. N Engl J Med. 2012;366(9):780–1.

    Article  CAS  PubMed  Google Scholar 

  96. Elwyn G, Miron-Shatz T. Deliberation before determination: the definition and evaluation of good decision making. Health Expect. 2010;13(2):139–47.

    Article  PubMed  Google Scholar 

  97. Dijkstra HP, Pollock N, Chakraverty R, Ardern CL. Return to play in elite sport: a shared decision-making process. Br J Sports Med. 2017;51(5):419–20.

    Article  PubMed  Google Scholar 

  98. Montazemi AR, Wang F, Khalid Nainar SM, Bart CK. On the effectiveness of decisional guidance. Decis Support Syst. 1996;18(2):181–98.

    Article  Google Scholar 

  99. Hunink MGM, Drummond MF, Weinstein MC, et al., editors. Decision making in health and medicine: integrating evidence and values. 2 ed. Cambridge: Cambridge University Press; 2014. p. 392–413.

  100. McCall A, Lewin C, O’Driscoll G, Witvrouw E, Ardern C. Return to play: the challenge of balancing research and practice. Br J Sports Med. 2017;51(9):702–3.

    Article  PubMed  Google Scholar 

  101. Bolling C, van Mechelen W, Pasman HR, Verhagen E. Context matters: revisiting the first step of the ‘sequence of prevention’ of sports injuries. Sports Med. 2018;48(10):2227–34.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Gruskin S, Ferguson L, Alfven T, Rugg D, Peersman G. Identifying structural barriers to an effective HIV response: using the National Composite Policy Index data to evaluate the human rights, legal and policy environment. J Int AIDS Soc. 2013;16(1):18000.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Matheson GOMDP, Shultz RP, Bido J, Mitten MJJD, Meeuwisse WHMDP, Shrier IMDP. Return-to-play decisions: are they the team physician’s responsibility? [Miscellaneous Article]. Clin J Sport Med. 2011;21(1):25–30.

    Article  PubMed  Google Scholar 

  104. Herring SA, Kibler WB, Putukian M. The team physician and the return-to-play decision: a consensus statement-2012 update. Med Sci Sports Exerc. 2012;44(12):2446–8.

    Article  PubMed  Google Scholar 

  105. Ekstrand J, Lundqvist D, Davison M, D’Hooghe M, Pensgaard AM. Communication quality between the medical team and the head coach/manager is associated with injury burden and player availability in elite football clubs. Br J Sports Med. 2019;53(5):304–8.

    Article  PubMed  Google Scholar 

  106. Gabbett TJ, Whiteley R. Two training-load paradoxes: can we work harder and smarter, can physical preparation and medical be teammates? Int J Sports Physiol Perform. 2017;12(Suppl 2):S250–4.

    Article  PubMed  Google Scholar 

  107. McCall A, Dupont G, Ekstrand J. Injury prevention strategies, coach compliance and player adherence of 33 of the UEFA Elite Club Injury Study teams: a survey of teams’ head medical officers. Br J Sports Med. 2016;50(12):725–30.

    Article  PubMed  Google Scholar 

  108. Shrier I, Safai P, Charland L. Return to play following injury: whose decision should it be? Br J Sports Med. 2014;48(5):394–401.

    Article  PubMed  Google Scholar 

  109. Shultz R, Bido J, Shrier I, Meeuwisse WH, Garza D, Matheson GO. Team clinician variability in return-to-play decisions. Clin J Sport Med. 2013;23(6):456–61.

    Article  PubMed  Google Scholar 

  110. Matheson GO, Shultz R, Bido J, Mitten MJ, Meeuwisse WH, Shrier I. Return-to-play decisions: are they the team physician’s responsibility? Clin J Sport Med. 2011;21(1):25–30.

    Article  PubMed  Google Scholar 

  111. Creighton DW, Shrier I, Shultz R, Meeuwisse WH, Matheson GO. The team physician and the return-to-play decision: a consensus statement-2012 update. Med Sci Sports Exerc. 2012;44(12):2446–8.

    Article  Google Scholar 

  112. Ardern CL, Bizzini M, Bahr R. It is time for consensus on return to play after injury: five key questions. Br J Sports Med. 2016;50(9):506–8.

    Article  PubMed  Google Scholar 

  113. Dunlop G, Ardern CL, Andersen TE, Lewin C, Dupont G, Ashworth B, et al. Return-to-play practices following hamstring injury: a worldwide survey of 131 premier league Football teams. Sports Med. 2019;50:829–40.

    Article  PubMed Central  Google Scholar 

  114. Testoni D, Hornik CP, Smith PB, Benjamin DK Jr, McKinney RE Jr. Sports medicine and ethics. Am J Bioeth: AJOB. 2013;13(10):4–12.

    Article  PubMed  Google Scholar 

  115. Stovitz SD, Satin DJ. Professionalism and the ethics of the sideline physician. Curr Sports Med Rep. 2006;5(3):120–4.

    Article  PubMed  Google Scholar 

  116. Young K, White P, McTeer W. Body talk: male athletes reflect on sport. Injury Pain. 1994;11(2):175.

    Google Scholar 

  117. Podlog L, Eklund RC. A longitudinal investigation of competitive athletes’ return to sport following serious injury. J Appl Sport Psychol. 2006;18(1):44–68.

    Article  Google Scholar 

  118. Mayer J, Thiel A. Presenteeism in the elite sports workplace: the willingness to compete hurt among German elite handball and track and field athletes. Int Rev Sociol Sport. 2018;53(1):49–68.

    Article  Google Scholar 

  119. Mayer J, Giel KE, Malcolm D, Schneider S, Diehl K, Zipfel S, et al. Compete or rest? Willingness to compete hurt among adolescent elite athletes. Psychol Sport Exerc. 2018;35:143–50.

    Article  Google Scholar 

  120. Wiese-bjornstal DM, Smith AM, Shaffer SM, Morrey MA. An integrated model of response to sport injury: psychological and sociological dynamics. J Appl Sport Psychol. 1998;10(1):46–69.

    Article  Google Scholar 

  121. Podlog L, Hannon JC, Banham SM, Wadey R. Psychological readiness to return to competitive sport following injury: a qualitative study. Sport Psychol. 2015;29(1):1–14.

    Article  Google Scholar 

  122. Podlog L, Eklund RC. Professional coaches’ perspectives on the return to sport following serious injury. J Appl Sport Psychol. 2007;19(2):207–25.

    Article  Google Scholar 

  123. Nixon HL. Accepting the risks of pain and injury in sport: mediated cultural influences on playing hurt. Sociol Sport J. 1993;10(2):183.

    Article  Google Scholar 

  124. Roderick M, Waddington I, Parker G. Playing hurt: managing injuries in English Professional Football. Int Rev Sociol Sport. 2000;35(2):165–80.

    Article  Google Scholar 

  125. Schubring A, Thiel A. Coping with growth in adolescent elite sport. Sociol Sport J. 2014;31(3):304.

    Article  Google Scholar 

  126. Nixon HL. Coaches’ views of risk, pain, and injury in sport, with special reference to gender. Differences. 1994;11(1):79.

    Google Scholar 

  127. Walker A, Hing W, Lorimer A. The influence, barriers to and facilitators of anterior cruciate ligament rehabilitation adherence and participation: a scoping review. Sports Med Open. 2020;6(1):32.

    Article  PubMed  PubMed Central  Google Scholar 

  128. Glöckner A, Heinen T, Johnson JG, Raab M. Network approaches for expert decisions in sports. Hum Mov Sci. 2012;31(2):318–33.

    Article  PubMed  Google Scholar 

  129. Saaty TL, Ozdemir MS. Why the magic number seven plus or minus two. Math Comput Model. 2003;38(3–4):233–44.

    Article  Google Scholar 

  130. Simon HA. Models of man: social and rational; mathematical essays on rational human behaviour in a social setting. New York: Wiley; 1957.

    Google Scholar 

  131. Cowan N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behav Brain Sci. 2001;24(1):87–185.

    Article  CAS  PubMed  Google Scholar 

  132. Witten IH, Frank E, Hall MA. Chapter 9—moving on: applications and beyond. In: Witten IH, Frank E, Hall MA, editors. Data mining: practical machine learning tools and techniques. 3rd ed. Boston: Morgan Kaufmann; 2011. p. 375–99.

    Chapter  Google Scholar 

  133. Bittencourt NFN, Meeuwisse WH, Mendonça LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept. Br J Sports Med. 2016;50(21):1309–14.

    Article  CAS  PubMed  Google Scholar 

  134. Edouard P, Verhagen E, Navarro L. Machine learning analyses can be of interest to estimate the risk of injury in sports injury and rehabilitation. Ann Phys Rehabil Med. 2020.

  135. Bate L, Hutchinson A, Underhill J, Maskrey N. How clinical decisions are made. Br J Clin Pharmacol. 2012;74(4):614–20.

    Article  PubMed  PubMed Central  Google Scholar 

  136. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. Clinical versus mechanical prediction: a meta-analysis. Psychol Assess. 2000;12(1):19–30.

    Article  CAS  PubMed  Google Scholar 

  137. Maymin PZ. The automated general manager: can an algorithmic system for drafts, trades, and free agency outperform human front offices? J Glob Sport Manag. 2017;2(4):234–49.

    Article  Google Scholar 

Download references


KY is supported by the Australian Government Research Training Program Scholarship.


Open Access funding enabled and organized by CAUL and its Member Institutions All authors declare that no funding was received for this review.

Author information

Authors and Affiliations



KY and SR provided conceptualisation. KY did writing—original draft preparation. SR, CLA, FS and KY were involved in writing—review and editing. All authors read and approved the final manuscript.

Authors’ Information

KY is a physiotherapist and PhD student within the Institute for Health and Sport at Victoria University in Melbourne, Australia. CLA is a researcher in the Department of Family Practice at the University of British Columbia in Vancouver, Canada. FS is an associate professor in Sports Physiology within the Institute for Health and Sport at Victoria University. SR is a Professor of Sports Analytics within the Institute for Health and Sport at Victoria University.

Corresponding author

Correspondence to Kate K. Yung.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable-review article.

Consent for Publication

Not applicable.

Competing interests

Kate Yung, Clare L. Ardern, Fabio R. Serpiello and Sam Robertson declare that they have no conflicts of interest relevant to the content of this review.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yung, K.K., Ardern, C.L., Serpiello, F.R. et al. A Framework for Clinicians to Improve the Decision-Making Process in Return to Sport. Sports Med - Open 8, 52 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: