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

Abstract

IntroductionRunning 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 analysisIn 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 disseminationThe 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).

Urban Health and Wellbeing

“…In collaboration with Garmin, RUNSAFE has launched a worldwide study recruiting runners willing to monitor their running habits with a Garmin device and report their injury and health status on a weekly basis over an 18-month period. With other big data, the relationship between running activity, personal characteristics, and risk of running-related injuries will be investigated (Nielsen et al 2019). This data source is fundamental for BERTHA, as the fitness data will be combined with air pollution data to investigate if physical activity in polluted areas increases the risk of heart-rate variability as a sign of effects of air quality on the cardiovascular system.…”

Use of Wearables: Tracking and Retraining in Endurance Runners

“…The ability of wearable devices to capture massive sample sizes in running epidemiology studies will bolster statistical power in future studies to provide insight into less common and poorly understood running-related injuries, such as femoral stress fractures(4). One such ongoing study aims to collect data from 20,000 runners by combining self-reported injury data and running metrics captured by each runner's accelerometer-equipped GPS running watch (46).…”

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

“…These conditions and running-related injury, in general, are major obstacles to exercise activity [5], so prevention of patellar and Achilles tendon injuries are important. In-depth knowledge about forces applied to the involved anatomical structures is needed because an injury occurs when the cumulative tendon load exceeds the structure's capacity to withstand the load [6][7][8]. Cumulative tendon load is considered a superior metric for the prediction of injury compared to running distance [9], which has been widely used in the previous literature [10].…”

“…Therefore, developing a computationally simple algorithm to predict the cumulative force in the Achilles or the patellar tendon is an important step to improving the understanding of the etiology underpinning running injury in these structures. If successful, such algorithms can be used to obtain sessionspecific and structure-specific approximations of tissue loads in large-scale epidemiological studies examining the "too much training load, too soon"-theory [7]. Large-scale studies are needed to assess changes in the tendon force in different groups displaying different recovery patterns [7], running experience [6], previous injuries [6], and pain sensitivity [12], to name a few.…”

“…If successful, such algorithms can be used to obtain sessionspecific and structure-specific approximations of tissue loads in large-scale epidemiological studies examining the "too much training load, too soon"-theory [7]. Large-scale studies are needed to assess changes in the tendon force in different groups displaying different recovery patterns [7], running experience [6], previous injuries [6], and pain sensitivity [12], to name a few. In the ideal study, thousands of runners should be included to consider various effect-measure modifiers.…”