Behaving Differently! Why Don’t we Consider Different Research Questions to Integrate Behavioural Sciences into Sports Injury Prevention Research? A Methodological Rationale

Abstract

The claim that sports-related injuries can be prevented faces almost no objection. However, we continue struggling to reproduce preventive effects observed in efficacy trials while implementing preventive measures in sports settings. The artificial conditions created in trials allow researchers to control individual behaviour and tackle the influence of contextual factors to maximise the effect sizes. Still, the preventive effect is conditioned to change behaviour in naturalistic conditions. Athletes should adopt and sustain behaviours of interest; otherwise, the preventive effect will be diluted. Previous studies have highlighted the importance of considering behaviour in sports injury prevention research, but guidance on better integrating behavioural sciences into the field still needs to be provided. The present article proposes a framework outlining different types of evidence needed to foster the integration of behavioural sciences into sports injury prevention. After a brief introduction, we describe the framework and present examples of research questions covered by their component paths. Finally, we present a hypothetical application of the scheme using the adoption of FIFA 11 + as the preventive behaviour of interest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-026-00985-3.

Key Points

• Preventing sports-related injuries in sports settings demands behaviour change.

• Integrating behavioural sciences into sports injury prevention research can improve outcomes when implementing preventive measures in sports settings.

• Future research should consider a theoretical foundation and be informed by different types of evidence to promote behaviour change.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-026-00985-3.

Background

Sports injury prevention research has made significant progress in the last years. The cumulative scientific evidence demonstrating the efficacy and effectiveness of different interventions allows us to confidently assert that ‘it is possible to prevent sports-related injuries’ [1–7]. Such statement is supported by evidence from randomised controlled trials. Still, adherence problems often compromise preventive effects observed in sports settings [8–13]. For example, despite its proven efficacy in trials, adherence rates to the Nordic hamstring exercise (NHE) programme have been reported as low as 6% among professional football teams [9, 11, 12]. This gap between efficacy and effectiveness stems largely from the failure to consider adherence as a human behaviour [14, 15].

Behavioural sciences is a multidisciplinary field that employs scientific methods to generate and test theories that explain and/or predict human behaviour [16]. Behavioural and social sciences theories and models (BSSTMs) have been effectively used to explain and predict various health-related behaviours, including smoking cessation, self-examination, condom use, exercise, and dietary habits [17–21]. Furthermore, they offer a robust foundation for developing interventions aimed at modifying such behaviours [22, 23]. A systematic review with meta-analysis indicated that theory-based interventions designed to improve physical activity levels achieve larger effect sizes [24]. Given the evidence from broader health contexts, integrating behavioural sciences into sports injury prevention research may enhance adherence rates and outcomes in sports settings.

The usefulness of behavioural sciences for sports injury prevention relies on the availability of evidence built using a theoretical basis and applying high-quality methods. A systematic review, published in 2010, showed that only 11% of the sports injury prevention studies explicitly used BSSTMs [25]. Most studies employed theories to guide intervention design and/or implementation or to measure theoretical constructs, providing almost no information on how theories can be directly applied to promote behaviour change [25, 26]. Methods used to measure theoretical constructs also seems to be inadequate. According to a systematic review only 40% of the studies using BSSTMs gathered data using instruments for which measurement properties had been previously stablished. When planning the implementation of preventive measures, using theories and models in such ways is not optimal. Based on the available evidence, it is essential to determine how to connect theories to behaviour change using high-quality methods [22, 27].

Constructing links between theories and behaviour change requires asking and answering different research questions to generate empirical evidence supporting such connections [27]. Consequently, this article introduces a novel framework, the Behavioural Integration Scheme (BIS), aimed at systematically addressing essential research questions that integrate behavioural sciences into sports injury prevention. We demonstrate its application through the FIFA 11 + programme, a popular and widely investigated warm-up programme in the literature, providing a roadmap for researchers to design studies that translate efficacy into effectiveness [1, 28–30].

Behavioural Integration Scheme

Frameworks are vital research tools for structuring and organising a research field [31]. A systematic framework is essential to guide researchers in generating evidence that links BSSTMs to behaviour change through evidence-based strategies in sports settings. The BIS provides a structured approach to research questions, from efficacy to real-world implementation, that should be addressed to effectively integrate behavioural sciences into sports injury prevention research (Fig. 1). The accumulation of evidence addressing the research questions proposed by BIS will foster the promotion of behaviour change in the sports injury prevention field.

Fig. 1.

Behavioural Integration Scheme (BIS). BCTs Behaviour change techniques

At first glance, the scheme appears overwhelming without further clarification. Therefore, in the following sections, we will explain each path separately and provide examples of research questions that each path aims to answer. We also provide a supplementary table (Table S1) outlining critical steps for practical applications of the BIS.

Path A. Standard Efficacy (Explanatory) Trials

Box 1. Example of research question:

Is the FIFA 11+ efficacious to prevent sport-related injuries among youth female football players?

Efficacy trials are designed to evaluate the intervention’s potential to produce its intended effect under ideal and controlled conditions (Fig. 2) [32]. Most trials in sports injury prevention research are efficacy trials [33]. These trials focus on preventive measures, maximising internal validity by controlling contextual factors and ensuring the consistent delivery of interventions [34]. Researchers typically deliver the intervention or strictly regulate the delivery process to ensure optimal adoption rates [35]. In such circumstances, the research team manages athletes’ behaviour, resulting in minimal variability.

Fig. 2.

Path A – Efficacy trials

Evidence about the efficacy of interventions is crucial for preventing sports injuries [35]. However, this evidence does not provide insights into ‘how’ to achieve impactful outcomes when implementing effective interventions in sports contexts [36]. Rössler et al. demonstrated the efficacy of the ‘11 + Kids’ programme in preventing injuries among young football players through a cluster-randomised controlled trial [1]. Nevertheless, the highly controlled conditions of efficacy trials may not capture the complexities of real-world sports environments, such as gaps in knowledge, varying levels of motivation, and environmental constraints which can undermine adherence [37].

Translating efficacy into effectiveness requires information about implementation strategies [38, 39]. Implementation strategies can be defined as a set of methods or techniques used to enhance the adoption, implementation, and sustainability of interventions [40]. Contextual factors influence individual behaviour, and planning the implementation strategies used to support adherence becomes a critical step for achieving successful outcomes in sports settings [36, 41]. The choice of implementation strategies should be guided by an investigation of the context and empirical data supporting the use of specific strategies to address key contextual factors [27].

While efficacy trials are essential for establishing the potential of preventive measures, they often overlook real-world challenges such as inconsistent adherence [34]. To address this gap, effectiveness trials (Path A+) provide complementary evidence by focusing on external validity and the pragmatic application of interventions in sports settings.

Path A+ Standard Effectiveness (Pragmatic) Trials

Box 2. Example of research question:

What is the injury reduction achieved by the FIFA 11+ as a coach-delivered warm-up routine programme in a youth female football setting with variable adoption and/or adherence?

Effectiveness trials evaluate the impact of an intervention in real-world conditions [32]. Researchers allow flexibility in the delivery process, recognising the influence of and addressing contextual factors during implementation [34, 42]. Unlike efficacy trials, which prioritise internal validity, effectiveness trials emphasise external validity and aim to determine how interventions would perform in routine sports settings [43]. Such trials are often part of hybrid effectiveness-implementation designs, simultaneously assessing injury outcomes and the effects of implementation strategies [34].

In real-world settings, the impact of preventive measures on injury outcomes depends on individual behaviour [35]. Inconsistent adherence can weaken the effectiveness of interventions, even with their established efficacy [44]. The uptake of efficacious interventions, such as the NHE programme, remains low among professional and amateur football teams, and its preventive impact varies based on adherence levels [11, 12]. Therefore, effectiveness trials must consider factors that may hinder adherence, including individual cognitions (e.g., knowledge, attitudes, beliefs) and environmental constraints (e.g., sports and team culture, social influences, available human and financial resources), to understand their effects on adherence and injury outcomes [35, 42].

Effectiveness trials evaluate the intervention and the implementation strategies used to enhance adherence. Notably, in many sports contexts (e.g., team sports), the decision regarding which preventive measures will be adopted by athletes is influenced or determined by others (e.g., coaches, physiotherapists, etc.) [45]. While planning implementation strategies researchers should investigate in which levels behaviour change are required to support adherence [42].

Planning the implementation strategy involves selecting: (1) the method for delivering the intervention (i.e., who delivers it, to whom, how often, for how long, in what format, and in what context); and (2) behaviour change techniques (BCTs) tailored to the target population and context [46]. While most trials specify the delivery process, BCTs, regarded as the active components that facilitate individual behaviour, are seldom reported in sports injury prevention studies [39].

Replication of all intervention components is a fundamental step in promoting the integration of behavioural sciences in sports injury prevention. A call for a more detailed description of behavioural intervention components has already been made, and various guidelines specifying the items that should be reported are available [47–50]. An adequate description and reporting facilitate the accumulation of evidence that enables researchers to identify which strategies effectively target specific preventive behaviours and how to apply them in future research [26].

Path A+ (Fig. 3) builds on the foundation of efficacy trials by incorporating implementation strategies and testing interventions in real-world settings. Insights from Path A+ guide the validation of behavioural determinants (Path B) and the design of BCTs targeting these determinants (Path C), creating a continuous feedback loop that informs the refinement and implementation of interventions.

Fig. 3.

Path A+ – Hybrid effectiveness-implementation studies

Path B. Validation of the Targets

Box 3. Example of research question:

Step 1: Are the methods/instruments used to measure Health Action Process Approach (HAPA) model constructs a good representation of such behavioural determinants?

Step 2: Is the HAPA model useful to explain coaches’ adoption and implementation of FIFA 11+ during the season? Which behavioural determinants from the HAPA model are associated with adoption? How strong are those associations?

Behavioural determinants refer to theoretical constructs of individual cognitions, such as self-efficacy or intention, that may influence the adoption and maintenance of preventive behaviours [22]. BSSTMs use behavioural determinants and their relationships to explain and/or predict behaviour [51]. These constructs are critical for linking theories to behaviour change, and their validation is essential to ensure our interventions target meaningful, measurable, and modifiable constructs influencing the adoption of preventive behaviours [26, 27].

Path B (Fig. 4) can be divided into two distinct steps. The primary objective of Path B is to explore the role of theories in explaining preventive behaviours and to verify whether their component behavioural determinants are linked to the adoption of such behaviours (Step 2). However, before this verification, a preliminary step is necessary to validate the instruments used to measure behavioural determinants (Step 1). While we can directly observe and estimate variables such as height, width, and length, the same cannot be said for latent psychological constructs, such as attitudes, beliefs, and self-efficacy [52].

Fig. 4.

Paths B and C – Validation of the targets/Manipulating behavioural determinants. BCTs Behaviour change techniques

Individual cognitions arise from mental processes that are neither directly observable nor measurable [53]. The roles of theories and behavioural determinants can only be investigated if we employ high-quality instruments that accurately capture these theoretical constructs [54]. Instruments must undergo thorough psychometric (i.e., measurement properties) testing, with ample evidence supporting their validity, reliability, and responsiveness before their use [52, 54]. For instance, if we are interested in exploring the role of the HAPA model constructs, such as task self-efficacy, in explaining the adoption of the FIFA 11+, we must first verify that our instrument accurately reflects the constructs of interest, producing reliable and consistent results across various populations and conditions. Otherwise, the validity of findings may be compromised [52].

Assuming the adequacy of the instruments (Step 1), the second step (Step 2) in Path B is to examine the relationship between different behavioural determinants and the connection of each behavioural determinant with preventive behaviour. This information can be obtained through observational and experimental studies, preferably employing study designs and methodological approaches that account for causal inference [27, 55–57].

Path B builds on the findings of Path A + by identifying the behavioural determinants most relevant to adopting efficacious preventive measures. The validated determinants then inform the design of behaviour change techniques in Path C, creating a seamless progression from theory to practice.

Path C. Manipulating Target Behavioural Determinants

Box 4. Example of research question:

Assuming that task self-efficacy presents the strongest association with coaches’ adoption of the FIFA 11+ during the season, which BCTs could be used to influence this behavioural determinant during the implementation of the intervention?

Changing behaviour through the influence of behavioural determinants requires knowledge about the most effective strategies or techniques to manipulate target determinants in specific populations [27]. Path C (Fig. 5) focuses on designing and evaluating interventions that manipulate behavioural determinants to promote behaviour change. Using relevant behavioural determinants mapped in Path B, Path C identifies and tests BCTs to influence these determinants and, in turn, promote the adoption of preventive behaviours. This step bridges theoretical knowledge and practical application, ensuring interventions are grounded in empirical evidence. Behavioural sciences have significantly progressed in this issue in the last years [23, 26, 58]. Interventions aiming at changing behaviour usually have multiple components. A poor description of all intervention components makes it difficult to replicate research, apply practical evidence, and synthesise key components for behaviour change [26, 46]. In 2013, a consensus-based approach allowed the development of the ‘Behavior Change Technique Taxonomy (v1)’, an interdisciplinary and cross-behaviour classification of 93 behaviour change techniques to standardise reporting of interventions [46]. Additionally, in 2019, a group of researchers made a substantial effort to synthesise evidence and create a heat map linking 74 BCTs to 26 mechanisms of action, known as the ‘Theory & Techniques tool’ [59–61].

Fig. 5.

Path D – Testing the full path. BCTs Behaviour change techniques

The ‘Theory & Techniques tool’ offers a comprehensive, evidence-based framework for researchers to link BCTs to mechanisms of action while designing interventions [61]. For instance, if task self-efficacy and outcome expectancies are key determinants for coaches adopting the FIFA 11+, interventions could include BCTs such as ‘problem-solving’, where researchers and coaches analyse factors influencing adoption and select strategies to address perceived barriers and facilitators, and ‘salience of consequences’, where performance improvements are monitored by team staff and consistently reported to coaches. To evaluate the effectiveness of selected BCTs, researchers can employ randomised controlled trials or factorial designs, comparing the effects of implementation strategies that explicitly incorporate ‘problem-solving’ and ‘salience of consequences’ with implementation lacking additional support. In these trials, it is crucial to measure the adoption of preventive behaviours and the intermediate effects on the targeted determinants, such as increases in task self-efficacy and outcome expectancies.

The ‘Theory & Techniques tool’ is transdisciplinary and incorporates evidence from various fields [61]. However, this resource serves as a foundation for sports injury prevention, offering a strong basis for researchers to design interventions aimed at altering preventive behaviours by manipulating target behavioural determinants.

Path C translates the validated behavioural determinants from Path B into actionable interventions that are subsequently tested in hybrid effectiveness-implementation designs in Path D. This iterative process ensures that interventions are both theoretically sound and effective in promoting behaviour change.

Path D. Testing the Full Path

Box 5. Example of research question:

Is a pre-season workshop for coaches considering BCTs targeting self-efficacy (e.g., ‘problem solving’ and ‘demonstration of the behaviour’) effective to improve the adoption of the FIFA 11+ during the season? Was the effect observed mediated by self-efficacy? Was there an impact on injury outcomes?

Path D tests the entire intervention pathway by outlining an intervention that integrates evidence-based preventive measures, delivery modes, and BCTs targeting crucial behavioural determinants. This path assesses whether the intervention affects behaviour and injury outcomes in real-world contexts. By incorporating targeted BCTs into the intervention, Path D concurrently evaluates the performance of the preventive measure in sports settings and the influence of active behavioural components on adherence.

Hybrid effectiveness-implementation designs, such as hybrid type 1, type 2, and type 3 trials, are ideal for Path D, which includes preventive and implementation outcomes [34]. For example, a hybrid type 2 trial could evaluate the FIFA 11 + programme delivered by youth coaches. This trial might compare two implementation strategies: one that explicitly includes BCTs, such as ‘problem-solving’ and ‘salience of consequences,’ and a standard approach without any active components supporting behaviour change [46]. Outcomes should be assessed at three levels: (1) changes in targeted behavioural determinants (e.g., increased task self-efficacy), (2) adherence to preventive behaviours (e.g., frequency of FIFA 11 + use), and (3) injury-related outcomes (e.g., reduction in injury incidence or severity) [34].

Measuring outcomes at different levels is necessary but not sufficient in this case. Mediation analysis is crucial to determine whether changes in behavioural determinants (e.g., task self-efficacy) mediate the relationship between the intervention and adherence and/or injury outcomes [57, 62]. For example, researchers might analyse whether increases in task self-efficacy, driven by selected BCTs, explain improved adherence to the FIFA 11 + and subsequent reductions in injury rates. Such information is helpful to determine the action mechanisms of BCTs, and also to provide data supporting (or not) the value of specific BSSTMs to target preventive behaviours [22, 26]. At this point, two considerations should be noted. First, it is important to realise that some BCTs can directly influence behaviour; that is, their effect may not need mediation through behavioural determinants. Second, we should acknowledge that for some preventive behaviours, higher adherence rates could not necessarily lead to better preventive outcomes, outlining the role of other variables and the need to investigate dose-response relationships [63]. For many behaviours, minimum adherence rates needed to elicit a preventive effect, as well as maximum adherence rates from which no additional preventive effect is obtained, remains unclear [64].

Path E. Supporting, Reviewing, and Updating Theories and Models and Accumulating Evidence to Guide BCT’s Choice

Box 6. Example of research question:

Do HAPA model and alternative theories and models explain significant variance of coaches’ adoption of injury prevention exercise programmes (IPEPs) during the season?

Based on available evidence, do current theories and models need updates? Are key behavioural determinants not being considered by theories?

Is a new theory or model necessary to explain or predict the adoption of IPEPs in youth football?

Path E is not meant to serve as a place for conducting new studies. Instead, it promotes the development of new research based on previous paths (Fig. 1). Path E encourages researchers to critically evaluate the existing evidence regarding the phenomenon of interest (e.g., preventive behaviour). New evidence from Paths A to D becomes available regularly, and researchers should integrate and apply this information when designing new interventions and planning their implementation in sports contexts.

The first step in designing behaviour change interventions is defining the overall approach that will be used to change behaviour [65]. Behaviours can be changed through the manipulation of the internal state of individuals (e.g., enhancing motivation and/or capability) and/or external factors (e.g., new policy implementation) [23, 66]. While for some behaviours the change process is ‘individual-driven’ (i.e., changes in self-efficacy foster a new behaviour), for others the process is ‘environmental-driven’ (i.e., changes in environmental constraints drive a new behaviour) [65]. Researchers should decide whether to adopt single or multiple approaches when designing their interventions. Ultimately, this decision should be underpinned by theories and models [25, 65].

Theories are not static. The development of theory-based interventions, the selection of relevant behavioural determinants to monitor, and the theories to test when attempting to explain and predict specific preventive behaviours should be guided by updated evidence [67]. The emergence of new evidence may require researchers to reassess and revise theories to enhance their ability to explain, predict, and change preventive behaviour [25, 67]. Ultimately, the adoption of such new practices by researchers would advance the field of sports injury prevention further.

Summary and Applications

The BIS framework systematically addresses the continuum of evidence needed to integrate behavioural sciences into sports injury prevention. Path A evaluates the efficacy of interventions under controlled conditions. Path A+ tests their effectiveness and implementation in real-world settings. Path B validates theoretical behavioural determinants influencing behaviour change, while Path C identifies and tests BCTs to manipulate these determinants. Path D integrates all these components to evaluate the complete intervention pathway. Path E highlights the dynamic and iterative nature of the BIS process by facilitating updates and cumulative knowledge along the pathway. Table S1 (Supplementary Material) summarises the practical steps that should be adopted by researchers while designing research projects to address research questions described in each path of the BIS.

While the BIS framework offers a comprehensive approach, its application requires significant resources and interdisciplinary collaboration. For instance, developing validated instruments (Path B) or testing behaviour change techniques (Path C) can be time-consuming and require expertise from psychologists and behavioural scientists [27]. Conducting hybrid effectiveness-implementation studies (Path D) also requires expertise in different disciplines, including implementation science, qualitative research and statistics [34, 68]. Interventions and data collection often target different stakeholder groups, demanding multilevel analyses and larger sample sizes [34]. Furthermore, translating findings across various sports and cultural contexts could compromise fidelity, requiring adaptations and iterative testing [41]. The development of interdisciplinary networks working collaboratively in sports injury prevention research may overcome these challenges.

We believe that by adopting the BIS, researchers can ensure that interventions are effective in controlled trials and feasible, scalable, and impactful in diverse sports settings. This approach encourages the accumulation of evidence across contexts, ultimately resulting in more robust and sustainable injury prevention strategies.

Future research should focus on gathering evidence across all paths of the BIS for commonly used interventions, such as warm-up programmes for injury prevention, while investigating its application to newer methods, such as digital health technologies and data-driven injury prediction models. Also, researchers should consider innovative approaches to measure adherence [69]. Currently, adherence measures frequently rely solely on self-report measurements or reports from others (e.g., coaches, physiotherapists, etc.) regarding adherence (e.g., warm-up programmes). Other variables, including the quality of completion may be objectively measured (e.g., wearable devices). In this sense, adherence could also be conceived as a system [70].

An illustration of BIS can be derived from the research questions provided in Boxes 1, 2, 3, 4, 5, using the FIFA 11 + as an example. Sufficient evidence has demonstrated the efficacy of the FIFA 11+. A systematic review with meta-analysis, including six randomised controlled trials, indicated an average relative risk of 0.70 (95% CI 0.52 to 0.93) [71]. The effectiveness of the FIFA 11 + has also been explored in a cluster-randomised controlled trial addressing adherence rates to the programme in youth female football [72]. Considering this data and adopting the HAPA model as a theoretical framework, we can seek evidence covering Paths B and C. Limited evidence is available regarding Path B. A cross-sectional study examined the association between HAPA constructs (i.e., behavioural determinants) and the intention to adopt FIFA 11 + among young female football coaches [73]. The results revealed that task self-efficacy had the strongest correlation with adopting FIFA 11 + among the evaluated HAPA constructs. At this point, we should consider methodological aspects regarding the evidence surrounding Path B. First, the sample size was small, and the cross-sectional design does not allow us to assume a causal direction of effect between task self-efficacy and intention. Second, while face validation has been conducted, we require extensive data on other measurement properties to confirm that the instrument used to evaluate HAPA constructs accurately represents theoretical cognitions. Third, although a connection between task self-efficacy and intention was observed, actual behaviour was not measured. Despite these limitations, we can progress to Path C if we use the available data as a starting point and hypothetically assume that task self-efficacy is a key behavioural determinant for adopting FIFA 11 + among coaches. According to the evidence synthesis provided by the ‘Theory & Techniques Tool,’ ‘problem-solving’ and ‘demonstration of the behaviour’ are BCTs that can influence behaviour through their impact on self-efficacy [59, 60]. Evidence covering Paths A to C could be integrated by researchers when developing research protocols targeting Path D. A hybrid effectiveness-implementation study, using the FIFA 11 + as the preventive measure, could be designed to compare the effects of implementation strategies that explicitly incorporate and do not incorporate ‘problem-solving’ and ‘demonstration of the behaviour’ on preventive behaviour (e.g., adherence) and injury outcomes (e.g., injury incidence). Behavioural determinants (e.g., task self-efficacy) should also be measured to investigate the effects of mediation. Accumulating evidence surrounding Path D would enhance the development of implementation strategies for researchers and implementers in the future (Path E).

Conclusions

Integrating behavioural sciences into sports injury prevention could help bridge the gap between efficacy and effectiveness. The application of behavioural sciences has been limited, and broadening their use may enhance the impact of evidence-based preventive measures in sports settings worldwide. Such enhancements require evidence addressing various research questions to connect theories to behaviour change. Accumulating evidence that encompasses all paths of the BIS framework requires interdisciplinary approaches and collaboration among researchers with varied expertise, besides the involvement of other professionals, decision-makers, stakeholders, and the end-users.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BCTs

Behaviour change techniques

BIS

Behaviour Integration Scheme

BSSTMs

Behavioural and social sciences theories and models

HAPA

Health Action Process Approach

IPEPs

Injury prevention exercise programmes

NHE

Nordic hamstring exercise

Author Contributions

Luiz Augusto Borges Gomes and Evert Verhagen developed the reasoning for this article. Luiz Augusto Borges Gomes drafted the first version of the manuscript. Evert Verhagen and Luiz Hespanhol reviewed and commented on various versions of the manuscript. All authors read and approved the final version of the manuscript before submission.

Funding

Luiz Augusto Borges Gomes is a PhD candidate supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001. Luiz Hespanhol received a Research Productivity Fellowship (PQ) from the National Council for Scientific and Technological Development – Brazil (CNPq), process 310943/2023-0. No other sources of funding were used to support the preparation of this article.

Data Availability

Not applicable.

Declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

Luiz Augusto Borges Gomes, Evert Verhagen, and Luiz Hespanhol have no conflict of interest to declare that are relevant to the content of this article.