Distinguishing between causal and non-causal associations: implications for sports medicine clinicians

Is Travel Associated With Match Performance in Elite North American Professional Soccer? An Exploratory Study

PURPOSE

Travel fatigue impacts cognitive and physiologic systems, but its association with elite soccer match performance is unclear. In this retrospective observational study, we aimed to explore the association between travel and match outcomes in elite North American soccer.

METHODS

Travel data and match outcomes (team points or goals scored and conceded) and physical performance outcomes from 26 elite professional soccer teams and their players were analyzed (148 matches [team-based data] and 1252 player matches from 297 players; age 22.7 [4.5] y). Player- and match-level correlations between performance measures and both acute and cumulated travel metrics were analyzed.

RESULTS

Cumulative travel metrics were positively associated with team (travel distance [r = .20; 95% CI, .03-.25], travel time [r = .20; .06-.37], and time away [r = .20; .06-.37]) and individual player (travel distance, [r = .14; .08-.19], travel time [r = .17-.23], and time away [r = .13; .07-.18]) high-intensity running. Cumulative time away was negatively associated with team points (r = -.14; -.28 to -.001) and positively associated with goals conceded (r = .14; .01-.27); no clear association between acute travel metrics and match outcomes or physical performance was observed.

CONCLUSIONS

As travel cumulated, away teams and their players ran more but for less reward (team points), although the magnitude of these associations was small. These data are exploratory and do not imply a causal relationship; however, further research should consider cumulation of travel.

Understanding Training Load as Exposure and Dose

Various terms used in sport and exercise science, and medicine, are derived from other fields such as epidemiology, pharmacology and causal inference. Conceptual and nomological frameworks have described training load as a multidimensional construct manifested by two causally related subdimensions: external and internal training load. In this article, we explain how the concepts of training load and its subdimensions can be aligned to classifications used in occupational medicine and epidemiology, where exposure can also be differentiated into external and internal dose. The meanings of terms used in epidemiology such as exposure, external dose, internal dose and dose-response are therefore explored from a causal perspective and their underlying concepts are contextualised to the physical training process. We also explain how these concepts can assist in the validation process of training load measures. Specifically, to optimise training (i.e. within a causal context), a measure of exposure should be reflective of the mediating mechanisms of the primary outcome. Additionally, understanding the difference between intermediate and surrogate outcomes allows for the correct investigation of the effects of exposure measures and their interpretation in research and applied settings. Finally, whilst the dose-response relationship can provide evidence of the validity of a measure, conceptual and computational differentiation between causal (explanatory) and non-causal (descriptive and predictive) dose-response relationships is needed. Regardless of how sophisticated or "advanced" a training load measure (and metric) appears, in a causal context, if it cannot be connected to a plausible mediator of a relevant response (outcome), it is likely of little use in practice to support and optimise the training process.

Adolescents with Sport-Related Concussion Who Adhere to Aerobic Exercise Prescriptions Recover Faster

PURPOSE

There is growing evidence that subsymptom threshold aerobic exercise speeds recovery from sport-related concussion (SRC). It is not known whether there is a direct relationship between adherence to a personalized exercise prescription and recovery or if initial symptom burden affects adherence to the prescription.

METHODS

This study was a planned secondary analysis of one arm of a randomized controlled trial. Male and female adolescent athletes (age 13-18 yr) presenting within 10 d of SRC were given aerobic exercise prescriptions based on their heart rate threshold at the point of exercise intolerance on a graded treadmill test. Adherence was determined objectively with HR monitors and compared against time to recovery. Participants who completed at least two-thirds of their aerobic exercise prescription were considered to be adherent.

RESULTS

Sixty-one percent of adolescents met the adherence criterion. Those who were adherent were more symptomatic and were more exercise intolerant at their initial visit, yet they recovered faster than those who were not adherent (median recovery time, 12 (interquartile range, 9-22) d vs 21.5 (interquartile range, 13-29.8) d; P = 0.016). On linear regression, adherence during week 1 was inversely related to recovery time ( β = -0.002 (-0.003, 0.0), P = 0.046) and to initial exercise tolerance ( β = -0.886 (-1.385, -0.387), P < 0.001), but not to initial symptom severity ( β = 0.545 (-0.232, 1.323), P = 0.146). No adverse events or near misses were reported.

CONCLUSIONS

Adherence to individualized subsymptom threshold aerobic exercise within the first week of evaluation is associated with faster recovery from SRC. The data suggest that initial degree of exercise intolerance, but not initial symptom severity, affects adherence to aerobic exercise prescribed to adolescents within 10 d of SRC.

Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

Elite sport practitioners increasingly use data to support training process decisions related to athletes' health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration

Misuse of statistics in medical and sports science research is common and may lead to detrimental consequences to healthcare. Many authors, editors and peer reviewers of medical papers will not have expert knowledge of statistics or may be unconvinced about the importance of applying correct statistics in medical research. Although there are guidelines on reporting statistics in medical papers, a checklist on the more general and commonly seen aspects of statistics to assess when peer-reviewing an article is needed. In this article, we propose a CHecklist for statistical Assessment of Medical Papers (CHAMP) comprising 30 items related to the design and conduct, data analysis, reporting and presentation, and interpretation of a research paper. While CHAMP is primarily aimed at editors and peer reviewers during the statistical assessment of a medical paper, we believe it will serve as a useful reference to improve authors' and readers' practice in their use of statistics in medical research. We strongly encourage editors and peer reviewers to consult CHAMP when assessing manuscripts for potential publication. Authors also may apply CHAMP to ensure the validity of their statistical approach and reporting of medical research, and readers may consider using CHAMP to enhance their statistical assessment of a paper.

A Novel Approach to Training Monotony and Acute-Chronic Workload Index: A Comparative Study in Soccer

Load is a multifactorial construct, but usually reduced to parameters of volume and intensity. In the last decades, other constructs have been proposed for assessing load, but also relying on relationships between volume and intensity. For example, Foster's Training Monotony has been used in athletes' load management simply by computing mean weekly load divided by its standard deviation, often multiplied by session rate of perceived exertion. Meanwhile, the Acute to Chronic Workload Ratio (ACWR) has been debated by the sport scientists as a useful monitoring metric and related to so-called injury prevention. None of these models includes parameters that are representative of training specificity, namely load orientation. The aim of this study is to present broader conceptual approaches translated by new indices for assessing Intraweek Training Monotony (ITM) and Acute to Chronic Workload Index (ACWI) while incorporating load orientation, session duration and weekly density (frequency normalized) in addition to parameters related to proxies of external and/or internal load. Our ITM and Foster's Training Monotony were similar in terms of average values, but very different for individualized analysis, illustrating how average values may be deceiving. While Foster's model provided clusters of values, ITM provided more scattered, individualized data. ACWI and ACWR provided very distinct qualitative information, and the two models were uncorrelated. Therefore, the models incorporating training load orientation presented in this study provide distinct and not redundant information when compared to previous models. More importantly, ITM and ACWI are metrics that are compatible to each other and might fit to coaches' monitoring targets in the short and medium terms, respectively. Because our models include several parameters, including load orientation, we contend that might provide a more complete monitoring tool. However, we suggest they are used for intraindividual comparisons and not so strongly for interindividual comparisons.

Training Load and Injury Part 1: The Devil Is in the Detail-Challenges to Applying the Current Research in the Training Load and Injury Field

BACKGROUND

This article sets the scene for a critique of the research underpinning 2 common clinical assumptions: (1) training workload is a key factor influencing sports injury risk, and (2) training workload can be manipulated to reduce injury risk. In this clinical commentary, we address why it is important for clinicians to critically evaluate the evidence behind research conclusions.

CLINICAL QUESTION

Has research been designed and conducted well enough to help clinicians answer the questions, "What is the relationship between training workload and sports injury risk?" and "Can the metrics based on training workload be used to decrease injury risk?"

KEY RESULTS

In the past decade, many sports injury researchers have developed new measures of exposure, based on internal and external training workload, to study the relationship between training load and injury. Some of these metrics may have been embraced by researchers and clinicians because (1) they are apparently supported by the scientific literature, (2) they are simple to calculate and use (averages and their ratio), and (3) there is an apparent reasonable rationale/narrative to support using workload metrics. However, intentional or unintentional questionable research practices and overinterpretation of research results undermine the trustworthiness of research in the training load and sports injury field.

CLINICAL APPLICATION

Clinicians should always aim to critically examine the credibility of the evidence behind a research conclusion before implementing research findings in practice. Something that initially looks promising and inviting might not be as revolutionary or useful as one first anticipated. J Orthop Sports Phys Ther 2020;50(10):574-576. Epub 1 Aug 2020. doi:10.2519/jospt.2020.9675.

Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls

The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.

Statement on methods in sport injury research from the 1st METHODS MATTER Meeting, Copenhagen, 2019

High quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best practice methods—methods matter (greatly!). The 1st METHODS MATTER Meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting.

Statement on Methods in Sport Injury Research From the First METHODS MATTER Meeting, Copenhagen, 2019

High-quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best-practice methods-methods matter (greatly!). The first METHODS MATTER meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting. Meeting participants agreed that the definition of sport injury depends on the research question and context. It was considered essential to be explicit about the goal of the research effort and to use frameworks to illustrate the assumptions that underpin measurement and the analytical strategy. Complex systems were discussed to illustrate how potential risk factors can interact in a nonlinear way. This approach is often a useful alternative to identifying single risk factors. Investigating changes in exposure status over time is important when analyzing sport injury etiology, and analyzing recurrent injury, subsequent injury, or injury exacerbation remains challenging. The choice of statistical model should consider the research question, injury measure (eg, prevalence, incidence), type and granularity of injury data (categorical or continuous), and study design. Multidisciplinary collaboration will be a cornerstone for future high-quality sport injury research. Working outside professional silos in a diverse, multidisciplinary team benefits the research process, from the formulation of research questions and designs to the statistical analyses and dissemination of study results in implementation contexts. This article has been copublished in the British Journal of Sports Medicine and the Journal of Orthopaedic & Sports Physical Therapy. J Orthop Sports Phys Ther 2020;50(5):226-233. doi:10.2519/jospt.2020.9876.

Methods matter: exploring the 'too much, too soon' theory, part 1: causal questions in sports injury research

BACKGROUND

It is widely accepted that athletes sustain sports injury if they train 'too much, too soon'. However, not all athletes are built the same; some can tolerate more training than others. It is for this reason that prescribing the same training programme to all athletes to reduce injury risk is not optimal from a coaching perspective. Rather, athletes require individualised training plans. In acknowledgement of athlete diversity, it is therefore essential to ask the right causal research question in studies examining sports injury aetiology.

PURPOSE

In this first part of a British Journal of Sports Medicine educational series, we present four different causal research questions related to the 'too much, too soon' theory and critically discuss their relevance to sports injury prevention.

CONTENT

If it is true that there is no 'one size fits all' training programme, then we need to consider by how much training can vary depending on individual athlete characteristics. To provide an evidence-base for subgroup-specific recommendations, a stronger emphasis on the following questions is needed: (1) How much training is 'too much' before athletes with different characteristics sustain sports-related injury? and (2) Does the risk of sports injury differ among athletes with a certain characteristic (eg, high experience) compared with athletes with other characteristics (eg, low experience) depending on how much training they perform? CONCLUSION: We recommend that sports injury researchers aiming to examine the 'too much, too soon' theory should carefully consider how they, assisted by coaches, athletes and clinicians, pose their causal research question. In the light of the limitations of population-based prevention that intends to provide all athletes with the same advice, we argue that a stronger emphasis on research questions targeting subgroups of athletes is needed. In doing so, researchers may assist athletes, clinicians and coaches to understand what training advice/programme works best, for whom and under what circumstances.

Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries.

Can We Trust the Literature on Risk Factors and Triggers for Low Back Pain? A Systematic Review of a Sample of Contemporary Literature

Background

Risk factors (RFs) for the "disease" of low back pain (LBP) are probably different from the triggers of new episodes of LBP. Investigating RFs for the onset of the "disease" and the triggers of LBP is problematic if researchers fail to discern the different types of pain-free status of participants at and before baseline. There is a difference between never having had LBP and having been pain-free for a certain period only. In this review, we assessed the dependability of contemporary literature on RFs and triggers of LBP, in relation to the "disease" and the episodes, respectively.

Methods

A literature search from 2010 until 2017 was performed. Information on the definitions of LBP, potential RFs/triggers, and study design was extracted. Studies were reclassified based on the type of LBP concerning the "disease," episode, or mixed/unclear/chronic. RFs and triggers were grouped into major domains, and positive associations listed, respectively, for the "disease" and episodes.

Results

In 42 of the included 47 articles, it was not clear if the authors investigated RFs for the "disease" of LBP or triggers of new episodes. Only one study properly reported RFs for the onset of the "disease" of LBP, and four studies were deemed suitable to investigate triggers for a new episode of LBP. No study reproduced the results of other included studies.

Conclusion

Trustworthy information regarding RFs and triggers of LBP is rare in the current literature. Future research needs to use precise definitions of LBP (onset of the "disease" vs. episodes) and nominate the timing of the associated factors in relation to the types of LBP as these are two critical factors when studying causes of LBP.

Estimating Determinants of Attrition in Eating Disorder Communities on Twitter: An Instrumental Variables Approach

BACKGROUND

The use of social media as a key health information source has increased steadily among people affected by eating disorders (EDs). Research has examined characteristics of individuals engaging in online communities, whereas little is known about discontinuation of engagement and the phenomenon of participants dropping out of these communities.

OBJECTIVE

This study aimed to investigate the characteristics of dropout behaviors among eating disordered individuals on Twitter and to estimate the causal effects of personal emotions and social networks on dropout behaviors.

METHODS

Using a snowball sampling method, we collected a set of individuals who self-identified with EDs in their Twitter profile descriptions, as well as their tweets and social networks, leading to 241,243,043 tweets from 208,063 users. Individuals' emotions are measured from their language use in tweets using an automatic sentiment analysis tool, and network centralities are measured from users' following networks. Dropout statuses of users are observed in a follow-up period 1.5 years later (from February 11, 2016 to August 17, 2017). Linear and survival regression instrumental variables models are used to estimate the effects of emotions and network centrality on dropout behaviors. The average levels of attributes among an individual's followees (ie, people who are followed by the individual) are used as instruments for the individual's attributes.

RESULTS

Eating disordered users have relatively short periods of activity on Twitter with one half of our sample dropping out at 6 months after account creation. Active users show more negative emotions and higher network centralities than dropped-out users. Active users tend to connect to other active users, whereas dropped-out users tend to cluster together. Estimation results suggest that users' emotions and network centralities have causal effects on their dropout behaviors on Twitter. More specifically, users with positive emotions are more likely to drop out and have shorter lasting periods of activity online than users with negative emotions, whereas central users in a social network have longer lasting participation than peripheral users. Findings on users' tweeting interests further show that users who attempt to recover from EDs are more likely to drop out than those who promote EDs as a lifestyle choice.

CONCLUSIONS

Presence in online communities is strongly determined by the individual's emotions and social networks, suggesting that studies analyzing and trying to draw condition and population characteristics through online health communities are likely to be biased. Future research needs to examine in more detail the links between individual characteristics and participation patterns if better understanding of the entire population is to be achieved. At the same time, such attrition dynamics need to be acknowledged and controlled when designing online interventions so as to accurately capture their intended populations.

Time-to-event analysis for sports injury research part 1: time-varying exposures

BACKGROUND

'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology.

AIM

To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes.

CONTENT

Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills.

CONCLUSION

To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data.

Study protocol of a 52-week Prospective Running INjury study in Gothenburg (SPRING)

INTRODUCTION

It is assumed that a running-related (overuse) injury occurs when a specific structure of the human body is exposed to a load that exceeds that structures' load capacity. Therefore, monitoring training load is an important key to understanding the development of a running-related injury. Additionally, other distribution, magnitude and capacity-related factors should be considered when aiming to understand the causal chain of injury development. This paper presents a study protocol for a prospective cohort study that aims to add comprehensive information on the aetiology of running-related injuries and present a new approach for investigating changes in training load with regard to running-related injuries.

METHODS AND ANALYSIS

This study focused on recreational runners, that is, runners exposed to a minimum weekly average of 15 km for at least 1 year. Participants will undergo baseline tests consisting of a clinical/anthropometrical examination and biomechanical measurements. Furthermore, participants will log all training sessions in a diary on a weekly basis for 1 year. The primary exposure variable is changes in training load. A medical practitioner will examine runners suffering from running-related pain and, if possible, make a clear diagnosis. Finally, additional time-varying exposure variables will be included in the main analysis, whereas the analysis for the secondary purpose is based on time-fixed baseline-related risk factors.

ETHICS AND DISSEMINATION

Ethical approval (DNR: 712-15) for the study and its design was obtained from the Gothenburg regional ethical review board. The results of the study will be published in peer-reviewed journals.