Motivational Differences between 5K Runners, Marathoners and Ultramarathoners in Poland

Analysis of the 10-day ultra-marathon using a predictive XG boost model

OBJECTIVE

Ultra-marathon running races are held as distance-limited or time-limited events, ranging from 6 h to 10 days. Only a few runners compete in 10-day events, and so far, we have little knowledge about the athletes' origins, performance, and event characteristics. The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. The model explainability tools were then used to investigate how each independent variable would influence the predicted running speed.

RESULTS

The model rated the origin of the athlete as the most important predictor, followed by age group, running on dirt path, gender, running on asphalt, and event location. Running on dirt path led to a significant reduction of running speed, while running on asphalt showed faster running speeds compared to other surfaces. Most athletes came from USA, followed by Russia, Germany, Ukraine, the Czech Republic, and Slovakia. Most of the runners competed in USA. The fastest 10-day runners were from Finland and Israel. The fastest 10-day races were held in Greece.

CONCLUSIONS

Most 10-day runners originated from USA, but the fastest runners originate from Finland and Israel. The fastest race courses were in Greece. Running on dirt paths leads to a significant reduction in running speed while running on asphalt leads to faster running speeds.

Trails, traits, and mental states: Psychological differences between competitive and recreational sub-ultra and ultramarathon runners

Psychological trait differences between ultrarunners (completing more than a standard 42.195 km marathon) and sub-ultrarunners (≤42.195 km) have been assumed, but not robustly examined. The current study investigated trait differences between competitive and recreational runners at sub-ultra and ultramarathon distances. Six hundred and eleven participants (334 recreational, 400 sub-ultrarunner) completed an online, anonymous survey assessing five traits: mental toughness (MT), grit, motivation, general self-efficacy (GSE), and personality. Bayesian ANOVAs indicated at least moderate evidence for differences within Distance for grit-perseverance of effort (PE) and intrinsic motivation (IM), such that ultrarunners scored higher than sub-ultrarunners. Similarly, we observed at least moderate evidence that competitive runners scored higher than recreational runners on overall grit, grit-PE, GSE, IM, and extrinsic motivation (EM). Interaction effects provided moderate evidence for greater MT and overall grit in competitive ultrarunners. Evidence for other differences were anecdotal or favoured the null hypothesis. This study represents a novel investigation into the psychological makeup of runners. We suggest that aspects of motivation, grit, and self-efficacy, but not personality, may differentiate competitive from recreational runners, and ultrarunners from sub-ultrarunners. The identification of these trait differences may benefit runners and coaches wanting to progress in distance or competition level, with broader application to workplace or academic contexts. Further research is needed to develop a clearer conceptualisation of mental toughness, and build upon the novel findings of this study, particularly given the paucity of literature that exists within this context.

Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events

The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete's age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25-29 years, but the average speeds of groups 20-24 and 30-34 years are close. Participation, however, peaks for the age group 40-44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.

Motivation of Marathon and Ultra-Marathon Runners. A Narrative Review

This narrative review study seeks to consolidate the existing knowledge on motivational factors that influence marathon and ultramarathon runners. The primary aim is to collect and summarize the understanding of the factors that drive both marathon and ultramarathon runners. Furthermore the review seeks to explore how variables like gender, age and experience impact motivational drivers within these running communities to highlight the intricate nature of factors, in endurance running and stress the significance of tailored training approaches and community backing to enhance participation and achievement. The study aimed to thoroughly review research papers using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. After the evaluation of 302 publications that could potentially be relevant, 61 papers were selected for inclusion in the review. The investigation uncovers unique motivating patterns within these endurance sports communities. The desire for good health, physical fitness, and social interaction drives marathon runners. Their motivations develop as they train and become more involved in the running community. On the other hand, ultramarathon runners are motivated by internal psychological variables like self-esteem and personal exploration, which are significantly shaped by the ultramarathon community's impact on their identity and running approach. Additional research indicates that gender, age, and experience affect the motivational elements in both groups differently. Specifically, experienced ultramarathon runners prioritize personal achievements and health more than time. This review underscores the intricacy of motivating elements in endurance running, highlighting the necessity for tailored methodologies in training and communal assistance to cultivate involvement and achievement.

Motivation and Eco-Attitudes among Night Runners during the COVID-19 Pandemic

Running surrounded by nature at night, often with limited contact with other people, is one of the safest physical activities during the COVID-19 pandemic. The aim of this article is to determine what motivates night recreational runners and to analyse the differences between men and women according to age, and also to see whether the fact of having had COVID-19 or not matters when it comes to athletes’ motivation behind participating in runs. Runners were also asked about participating in cities or out-of-town events in terms of verifying the cleanliness of the air before running, using masks or running in green areas. 233 individuals in total participated in this descriptive, quantitative, cross-sectional study. The questionnaire uses the division of motives used in the Marathon Motivation Scale (MOMS) and a number of additional questions on environmental factors. Overall, the main motivation that drives night-time runners is health orientation, weight concern, personal goal achievement, psychological coping, life meaning and self-esteem. The research also showed higher scores for health orientation, recognition, psychological coping, life meaning and self-esteem among runners who had had COVID-19 than runners who had not had the virus. In addition, respondents clearly indicated that night running makes it easier to fall asleep and improves the quality of sleep. Most runners rated their mental health as being good, did not run in the morning despite better air levels, did not choose running events outside the urban area and did not check the air quality index before running. Therefore, it will be important for coaches, event organisers and other professionals to consider athletes’ age, gender, whether they have had COVID-19 and runners’ approaches to eco-attitudes when trying to understand their reasons for participating in different sports or leisure events, especially those that focus on a priority in shaping environmental attitudes.