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.

Randomised controlled trials (RCTs) in sports injury research: authors-please report the compliance with the intervention.

BACKGROUND: In randomised controlled trials (RCTs) of interventions that aim to prevent sports injuries, the intention-to-treat principle is a recommended analysis method and one emphasised in the Consolidated Standards of Reporting Trials (CONSORT) statement that guides quality reporting of such trials. However, an important element of injury prevention trials-compliance with the intervention-is not always well-reported. The purpose of the present educational review was to describe the compliance during follow-up in eight large-scale sports injury trials and address compliance issues that surfaced. Then, we discuss how readers and researchers might consider interpreting results from intention-to-treat analyses depending on the observed compliance with the intervention. METHODS: Data from seven different randomised trials and one experimental study were included in the present educational review. In the trials that used training programme as an intervention, we defined full compliance as having completed the programme within ±10% of the prescribed running distance (ProjectRun21 (PR21), RUNCLEVER, Start 2 Run) or time-spent-running in minutes (Groningen Novice Running (GRONORUN)) for each planned training session. In the trials using running shoes as the intervention, full compliance was defined as wearing the prescribed running shoe in all running sessions the participants completed during follow-up. RESULTS: In the trials that used a running programme intervention, the number of participants who had been fully compliant was 0 of 839 (0%) at 24-week follow-up in RUNCLEVER, 0 of 612 (0%) at 14-week follow-up in PR21, 12 of 56 (21%) at 4-week follow-up in Start 2 Run and 8 of 532 (1%) at 8-week follow-up in GRONORUN. In the trials using a shoe-related intervention, the numbers of participants who had been fully compliant at the end of follow-up were 207 of 304 (68%) in the 21 week trial, and 322 of 423 (76%), 521 of 577 (90%), 753 of 874 (86%) after 24-week follow-up in the other three trials, respectively. CONCLUSION: The proportion of runners compliant at the end of follow-up ranged from 0% to 21% in the trials using running programme as intervention and from 68% to 90% in the trials using running shoes as intervention. We encourage sports injury researchers to carefully assess and report the compliance with intervention in their articles, use appropriate analytical approaches and take compliance into account when drawing study conclusions. In studies with low compliance, G-estimation may be a useful analytical tool provided certain assumptions are met.

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.

Time-to-event analysis for sports injury research part 2: time-varying outcomes.

BACKGROUND: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. CONTENT: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. CONCLUSION: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.

Knee Injuries in Normal-Weight, Overweight, and Obese Runners: Does Body Mass Index Matter?

OBJECTIVE: To investigate whether the proportion of running-related knee injuries differed in normal-weight, overweight, and obese runners. DESIGN: Comparative study. METHODS: Data from 4 independent prospective studies were merged (2612 participants). The proportion of running-related knee injuries out of the total number of running-related injuries was calculated for normal-weight, overweight, and obese runners, respectively. The measure of association was absolute difference in proportion of running-related knee injuries with normal-weight runners as the reference group. RESULTS: A total of 571 runners sustained a running-related injury (181 running-related knee injuries and 390 running-related injuries in other anatomical locations). The proportion of running-related knee injuries was 13% lower (95% confidence interval: -22%, -5%; P = .001) among overweight runners compared with normal-weight runners. Similarly, the proportion of running-related knee injuries was 12% lower (95% confidence interval: -23%, -1%; P = .042) among obese runners compared with normal-weight runners. CONCLUSION: Overweight and obese runners had a lower proportion of running-related knee injuries than normal-weight runners. J Orthop Sports Phys Ther 2020;50(7):397-401. doi:10.2519/jospt.2020.9233.

The Garmin-RUNSAFE Running Health Study on the aetiology of running-related injuries: rationale and design of an 18-month prospective cohort study including runners worldwide.

INTRODUCTION: Running injuries affect millions of persons every year and have become a substantial public health issue owing to the popularity of running. To ensure adherence to running, it is important to prevent injuries and to have an in-depth understanding of the aetiology of running injuries. The main purpose of the present paper was to describe the design of a future prospective cohort study exploring if a dose-response relationship exists between changes in training load and running injury occurrence, and how this association is modified by other variables. METHODS AND ANALYSIS: In this protocol, the design of an 18-month observational prospective cohort study is described that will include a minimum of 20 000 consenting runners who upload their running data to Garmin Connect and volunteer to be a part of the study. The primary outcome is running-related injuries categorised into the following states: (1) no injury; (2) a problem; and (3) injury. The primary exposure is change in training load (eg, running distance and the cumulative training load based on the number of strides, ground contact time, vertical oscillation and body weight). The change in training load is a time-dependent exposure in the sense that progression or regression can change many times during follow-up. Effect-measure modifiers include, but is not limited to, other types of sports activity, activity of daily living and demographics, and are assessed through questionnaires and/or by Garmin devices. ETHICS AND DISSEMINATION: The study design, procedures and informed consent have been evaluated by the Ethics Committee of the Central Denmark Region (Request number: 227/2016 - Record number: 1-10-72-189-16).

How Do Novice Runners With Different Body Mass Indexes Begin a Self-chosen Running Regime?

BACKGROUND: Overweight and obese novice runners are subjected to a higher load per stride than their normal-weight peers. Do they reduce their running dose accordingly when beginning a self-chosen running regime? OBJECTIVES: To describe and compare the preferred running dose in normal-weight, overweight, and obese novice runners when they commence a self-chosen running regime. METHODS: In this exploratory, 7-day prospective cohort study, 914 novice runners were categorized into 1 of 3 exposure groups, based on their body mass index (BMI): (1) normal weight (BMI less than 25 kg/m2, n = 405; reference group), (2) overweight (BMI of 25 to less than 30 kg/m2, n = 341), and (3) obese (BMI of 30 kg/m2 or greater, n = 168). All runners were equipped with a global-positioning-system running watch, which provided information about distance, duration, speed, and date of each running session during the first week of a self-chosen running regime. RESULTS: During the first session, overweight runners (difference, -0.5 km/h; 95% confidence interval [CI]: -0.8, -0.2 km/h; P<.05) and obese runners (-1.7 km/h; 95% CI: -2.0, -1.4 km/h; P<.05) ran slower than normal-weight runners. Obese runners also ran a shorter distance compared to normal-weight runners (-0.4 km; 95% CI: -0.7, -0.2 km; P<.05). During the first week, overweight runners (-0.5 km/h; 95% CI: -0.7, -0.2 km/h; P<.05) and obese runners (-1.7 km/h; 95% CI: -2.0, -1.4 km/h; P<.05) ran slower than normal-weight runners, while running distance and duration were similar. CONCLUSION: Overweight and obese runners selected a similar training dose to that of normal-weight runners when starting a self-chosen running regime. This may partly explain the higher running-injury risk among overweight and obese runners compared with normal-weight runners observed by other studies. J Orthop Sports Phys Ther 2018;48(11):873-877. Epub 22 Jun 2018. doi:10.2519/jospt.2018.8169.

Running more than three kilometers during the first week of a running regimen may be associated with increased risk of injury in obese novice runners.

BACKGROUND: Training guidelines for novice runners are needed to reduce the risk of injury. The purpose of this study was to investigate whether the risk of injury varied in obese and non-obese individuals initiating a running program at different weekly distances. METHODS: A volunteer sample of 749 of 1532 eligible healthy novice runners was included in a 3-week observational explorative prospective cohort study. Runners were categorized into one of six strata based on their body mass index (BMI) (≤30=low; >30=high) and running distance after 1 week (<3 km = low; 3 to 6 km = medium; >6 km = high). Data was collected for three weeks for the six strata. The main outcome measure was running-related injury. RESULTS: Fifty-six runners sustained a running-related injury during the 3-week data collection. A significantly greater number of individuals with BMI>30 sustained injuries if they ran between 3 to 6 km (cumulative risk difference (CRD) = 14.3% [95%CI: 3.3% to 25.3%], p<0.01) or more than 6 km (CRD = 16.2% [95%CI: 4.4% to 28.0%], p<0.01) the first week than individuals in the reference group (low distance and low BMI). The effect-measure modification between high running distance and BMI on additive scale was positive (11.7% [-3.6% to 27.0%], p=0.13). The number of obese individuals needed to change their running distance from high to low to avoid one injury was 8.5 [95%CI: 4.6 to 52]. CONCLUSIONS: Obese individuals were at greater risk of injury if they exceeded 3 km during the first week of their running program. Because of a considerable injury risk compared with their non-obese peers, individuals with a BMI>30 may be well advised to begin running training with an initial running distance of less than 3 km (1.9 miles) the first week of their running regime. Large-scale trials are needed to further describe and document this relationship. LEVEL OF EVIDENCE: Level 2b.