Run Clever - No difference in risk of injury when comparing progression in running volume and running intensity in recreational runners:A randomised trial

Background/aimThe Run Clever trial investigated if there was a difference in injury occurrence across two running schedules, focusing on progression in volume of running intensity (Sch-I) or in total running volume (Sch-V). It was hypothesised that 15% more runners with a focus on progression in volume of running intensity would sustain an injury compared with runners with a focus on progression in total running volume.MethodsHealthy recreational runners were included and randomly allocated to Sch-I or Sch-V. In the first eight weeks of the 24-week follow-up, all participants (n=839) followed the same running schedule (preconditioning). Participants (n=447) not censored during the first eight weeks entered the 16-week training period with a focus on either progression in intensity (Sch-I) or volume (Sch-V). A global positioning system collected all data on running. During running, all participants received real-time, individualised feedback on running intensity and running volume. The primary outcome was running-related injury (RRI).ResultsAfter preconditioning a total of 80 runners sustained an RRI (Sch-I n=36/Sch-V n=44). The cumulative incidence proportion (CIP) in Sch-V (reference group) were CIP2 weeks4.6%; CIP4 weeks8.2%; CIP8 weeks13.2%; CIP16 weeks28.0%. The risk differences (RD) and 95% CI between the two schedules were RD2 weeks=2.9%(−5.7% to 11.6%); RD4 weeks=1.8%(−9.1% to 12.8%); RD8 weeks=−4.7%(−17.5% to 8.1%); RD16 weeks=−14.0% (−36.9% to 8.9%).ConclusionA similar proportion of runners sustained injuries in the two running schedules.</jats:sec

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

Classification of the height and flexibility of the medial longitudinal arch of the foot

<p>Abstract</p> <p>Background</p> <p>The risk of developing injuries during standing work may vary between persons with different foot types. High arched and low arched feet, as well as rigid and flexible feet, are considered to have different injury profiles, while those with normal arches may sustain fewer injuries. However, the cut-off values for maximum values (subtalar position during weight-bearing) and range of motion (ROM) values (difference between subtalar neutral and subtalar resting position in a weight-bearing condition) for the medial longitudinal arch (MLA) are largely unknown. The purpose of this study was to identify cut-off values for maximum values and ROM of the MLA of the foot during static tests and to identify factors influencing foot posture.</p> <p>Methods</p> <p>The participants consisted of 254 volunteers from Central and Northern Denmark (198 m/56 f; age 39.0 ± 11.7 years; BMI 27.3 ± 4.7 kg/m²). Navicular height (NH), longitudinal arch angle (LAA) and Feiss line (FL) were measured for either the left or the right foot in a subtalar neutral position and subtalar resting position. Maximum values and ROM were calculated for each test. The 95% and 68% prediction intervals were used as cut-off limits. Multiple regression analysis was used to detect influencing factors on foot posture.</p> <p>Results</p> <p>The 68% cut-off values for maximum MLA values and MLA ROM for NH were 3.6 to 5.5 cm and 0.6 to 1.8 cm, respectively, without taking into account the influence of other variables. Normal maximum LAA values were between 131 and 152° and normal LAA ROM was between -1 and 13°. Normal maximum FL values were between -2.6 and -1.2 cm and normal FL ROM was between -0.1 and 0.9 cm. Results from the multivariate linear regression revealed an association between foot size with FL, LAA, and navicular drop.</p> <p>Conclusions</p> <p>The cut-off values presented in this study can be used to categorize people performing standing work into groups of different foot arch types. The results of this study are important for investigating a possible link between arch height and arch movement and the development of injuries.</p

Diagnoses and time to recovery among injured recreational runners in the RUN CLEVER trial.

PURPOSE:The purpose of the present study was to describe the incidence proportion of different types of running-related injuries (RRI) among recreational runners and to determine their time to recovery. METHODS:A sub-analysis of the injured runners included in the 839-person, 24-week randomized trial named Run Clever. During follow-up, the participants reported levels of pain in different anatomical areas on a weekly basis. In case injured, runners attended a clinical examination at a physiotherapist, who provided a diagnosis, e.g., medial tibial stress syndrome (MTSS), Achilles tendinopathy (AT), patellofemoral pain (PFP), iliotibial band syndrome (ITBS) and plantar fasciopathy (PF). The diagnose-specific injury proportions (IP) and 95% confidence intervals (CI) were calculated using descriptive statistics. The time to recovery was defined as the time from the first registration of pain until total pain relief in the same anatomical area. It was reported as medians and interquartile range (IQR) if possible. RESULTS:A total of 140 runners were injured at least once leading to a 24-week cumulative injury proportion of 32% [95% CI: 26%; 37%]. The diagnoses with the highest incidence proportion were MTSS (IP = 16% [95% CI: 9.3%; 22.9%], AT (IP = 8.9% [95% CI: 3.6%; 14.2%], PFP (IP = 8% [95% CI: 3.0%; 13.1%]. The median time to recovery for all types of injuries was 56 days (IQR = 70 days). Diagnose-specific time-to-recoveries included 70 days (IQR = 89 days) for MTSS, 56 days (IQR = 165 days) for AT, 49 days (IQR = 63 days) for PFP. CONCLUSION:The most common running injuries among recreational runners were MTSS followed by AT, PFP, ITBS and PF. In total, 77 injured participants recovered their RRI and the median time to recovery for all types of injuries was 56 days and MTSS was the diagnosis with the longest median time to recovery, 70 days

A step towards understanding the mechanisms of running-related injuries

AbstractObjectivesTo investigate the association between training-related characteristics and running-related injury using a new conceptual model for running-related injury generation, focusing on the synergy between training load and previous injuries, short-term running experience or body mass index (> or <25kgm−2).DesignProspective cohort study with a 9-month follow-up.MethodsThe data of two previous studies using the same methodology were revisited. Recreational runners (n=517) reported information about running training characteristics (weekly distance, frequency, speed), other sport participation and injuries on a dedicated internet platform. Weekly volume (dichotomized into <2h and ≥2h) and session frequency (dichotomized into <2 and ≥2) were the main exposures because they were considered necessary causes for running-related injury. Non-training-related characteristics were included in Cox regression analyses as effect-measure modifiers. Hazard ratio was the measure of association. The size of effect-measure modification was calculated as the relative excess risk due to interaction.ResultsOne hundred sixty-seven runners reported a running-related injury. Crude analyses revealed that weekly volume <2h (hazard ratio=3.29; 95% confidence intervals=2.27; 4.79) and weekly session frequency <2 (hazard ratio=2.41; 95% confidence intervals=1.71; 3.42) were associated with increased injury rate. Previous injury was identified as an effect-measure modifier on weekly volume (relative excess risk due to interaction=4.69; 95% confidence intervals=1.42; 7.95; p=0.005) and session frequency (relative excess risk due to interaction=2.44; 95% confidence intervals=0.48; 4.39; p=0.015). A negative synergy was found between body mass index and weekly volume (relative excess risk due to interaction=−2.88; 95% confidence intervals=−5.10; −0.66; p=0.018).ConclusionsThe effect of a runner's training load on running-related injury is influenced by body mass index and previous injury. These results show the importance to distinguish between confounding and effect-measure modification in running-related injury research