Dataset Information

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology.

ABSTRACT: This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types-in terms of their attitudes, interests, and opinions (AIOs) with regard to running-and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed the questionnaire. Principal component analysis and cluster analysis were used to identify the different running types, and crosstabs obtained insights into the use of technology between different typologies. Based on the AIOs, four distinct runner types were identified: casual individual, social competitive, individual competitive, and devoted runners. Subsequently, we related the types to their use of sports watches and apps. Our results show a difference in the kinds of technology used by different runner types. Differentiation between types of runners can be useful for health professionals, policymakers involved in public health, engineers, and trainers or coaches to adapt their services to specific segments, in order to make use of the full potential of running-related systems to support runners to stay active and injury-free and contribute to a healthy lifestyle.

SUBMITTER: Janssen M

PROVIDER: S-EPMC7177805 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Understanding Different Types of Recreational Runners and How They Use Running-Related Technology.

Janssen Mark M Walravens Ruben R Thibaut Erik E Scheerder Jeroen J Brombacher Aarnout A Vos Steven S

International journal of environmental research and public health 20200327 7

This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types-in terms of their attitudes, interests, and opinions (AIOs) with regard to running-and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed ...[more]

PMID: 32230999

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Project description:BackgroundMonitoring technology is increasingly accessible to recreational runners. Our aim was to examine patterns of technology use in recreational runners, and its potential association with injury.MethodsWe conducted a cross-sectional questionnaire study in a sample of adult runners. Recruitment took place at three different 5 km parkrun event across Northern Ireland. Demographics, technology use, running behaviour and running-related injury (RRI) history were examined. Regression analyses were performed to determine relationships between variables.ResultsResponses were obtained from 192 of 483 eligible finishers (39.8% response rate). Average age was 45.9 years (SD 10.3), with males (47.1 years SD 9.7) slightly older than females (44.8 years SD 10.8). On average, participants ran 3.0 days per week (SD 1.3), with an average weekly distance of 22.6 km (SD 19.7). Males typically ran further (MD 6.2 km/week; 95% CI 0.4 to 12.0) than females. Monitoring technology was used by 87.4% (153/175); with GPS watches the most common device (87.6% (134/153)). Runners using monitoring technology ran further (MD 14.4 km/week; 95% CI 10.3 to 18.5) and more frequently (MD 1.3 days/week; 95% CI 0.7 to 1.9) than those who did not use monitoring technology. There was no significant difference in average age between runners who used monitoring technology and those who did not (MD 4.0 years; 95% CI -0.7 to 8.7). RRI was reported by 40.6% (71/175) of participants in the previous 12 months. In a univariate analysis, none of the selected predictors (age, number of days run per week, distance run per week, or usage of technology to modify training pattern) (p > 0.1) were associated with RRI.ConclusionsThis study found a high prevalence of monitoring technology usage among recreational runners. While the incidence of RRI remains high, it is not associated with the usage of monitoring technology. Further prospective research should examine if monitoring technology can reduce RRI incidence among recreational runners in future.

Project description:ContextThe combination of excessive increases in running pace and volume is essential to consider when investigating associations between running and running-related injury.ObjectivesTo complete a secondary analysis, using a dataset from a randomized trial, to evaluate the interactions between relative or absolute weekly changes in running volume and running pace on the occurrence of running injuries among a cohort of injury-free recreational runners in Denmark.DesignProspective cohort study.SettingRunning volume and pace were collected during a 24-week follow-up using global positioning systems data. Training data were used to calculate relative and absolute weekly changes in running volume and pace.Patients or other participantsA total of 586 recreational runners were included in the analysis. All participants were injury free at baseline.Main outcome measure(s)Running-related injury was the outcome. Injury data were collected weekly using a modified version of the Oslo Sports Trauma Research Centre questionnaire. Risk difference (RD) was the measure of injury risk.ResultsA total of 133 runners sustained running-related injuries. A relative weekly change of progression >10% in running volume and progression in running pace (RD = 8.1%, 95% CI = -9.3%, 25.6%) and an absolute weekly change of progression >5 km in running volume and progression in running pace (RD = 5.2%, 95% CI = -12.0%, 22.5%) were not associated with a statistically significant positive interaction.ConclusionsGiven that coaches, clinicians, and athletes may agree that excessive increases in running pace and running volume are important contributors to injury development, we analyzed the interaction between them. Although we did not identify a statistically significant positive interaction on an additive scale in runners who progressed both running pace and running volume, readers should be aware that an interaction is an important analytical approach that could be applied to other datasets in future publications.

Project description:Simple Summary In the last decades, performance models have helped to comprehend the mechanisms involved in long-term physical performance. In addition, predictive models have aided in the evaluation and prescription of physical training. Here, we tested the hypothesis that physiological assessments under inclined conditions would better explain hilly running performance. We also checked the predictive role of running biomechanical, anthropometric, and neuromuscular factors. Velocity associated with maximal oxygen consumption was more predictive when assessed in inclined conditions (7%) than testing at level. Secondarily, ventilatory thresholds submaximal heart rate improved the performance models at hilly and level conditions. Spatiotemporal, strength, and anthropometric factors were not determinants of performance. Physiological assessments in inclined conditions predict 5-km running performance at hilly terrains in higher degree than evaluations at level in endurance runners. Abstract Incline and level running on treadmills have been extensively studied due to their different cardiorespiratory and biomechanical acute responses. However, there are no studies examining the performance determinants of outdoor running on hilly terrains. We aimed to investigate the influence of anthropometrics, muscle strength, and cardiorespiratory and gait spatiotemporal parameters during level (0%) and inclined (+7%) running on performance in level and hilly 5-km races. Twenty male recreational runners completed two 5-km outdoor running tests (0% vs. +7% and −7%), and two submaximal (10 km·h−1) and incremental treadmill tests at 0 and 7% slopes, after complete laboratory evaluations. The velocity at maximal oxygen consumption (VO2max) evaluated at 7% incline and level treadmill running were the best performance predictors under both hilly (R2 = 0.72; p < 0.05) and level (R2 = 0.85; p < 0.01) conditions, respectively. Inclusion of ventilatory and submaximal heart rate data improved the predictive models up to 100%. Conversely, none of the parameters evaluated in one condition contributed to the other condition. The spatiotemporal parameters and the runners’ strength levels were not associated to outdoor performances. These results indicate that the vVO2max evaluated at similar slopes in the lab can be used to predict 5-km running performances on both level and hilly terrains.

Dataset Information

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology.

Publications

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology.

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