What do runners themselves think makes a race great or terrible?

What do runners themselves think makes a race great or terrible?

Based on runners' own reflections on Strava, the study uncovers how weather, trails, social dynamics and pressure to perform work together to influence how people feel during and after their runs.

Study: Running with Feeling: A Qualitative Content Analysis of Runner Mood in Metro Vancouver. Photo credit: Sergey Mironov/Shutterstock.com

A novel study from Vancouver, Canada examines runners' preferences and challenges as expressed in their own narratives, shedding light on how people emotionally experience everyday runs. The study appeared in the journalWell-being, space and society.

According to the authors, this is one of the first studies to apply qualitative mixed-methods analysis at scale to thousands of narrative posts created directly by runners themselves using social media data.

How the environment influences the emotional side of running

Running is becoming increasingly popular as an urban activity around the world. It requires little equipment, is safe and inexpensive. However, it is not equally pleasant or safe everywhere. Several factors influence how runners feel about the sport during and after a run and how these feelings affect their well-being.

Regular running improves both physical and mental well-being and reduces cardiovascular risk and stress. The greatest sense of well-being is associated with running in blue or green areas with prominent natural bodies of water or green spaces. In fact, running therapy is a useful mental health intervention.

In contrast to activities such as cycling or walking, understanding of the factors that impact the runner experience is relatively limited, making it difficult to develop guidelines for safe and inclusive running environments. The current study was based on Strava, a social media application that tracks fitness and is widely used by runners. However, Strava users are not fully representative of all runners, as they tend to favor higher-income and more engaged users, limiting the generalizability of the results.

How Strava posts became a qualitative research dataset

The researchers used a qualitative dataset derived from Strava posts from 72 men and 65 women in the Greater Vancouver area. The researchers chose a mixed-methods approach that combines quantitative and qualitative methods to analyze the factors that contribute to extremely positive or negative feelings in runners.

For example, previous quantitative studies using crowdsourced data from apps like Strava have found a preference for blue or green spaces, low-traffic areas, comfortable, well-maintained surfaces, and fewer crosswalk signals. These are likely related to the socioeconomic status of neighborhoods and users.

While such data is often based on geolocation, the aggregated nature of this information prevents personal experiences and movements from being captured and included in the analysis. Therefore, the current study used NLP for sentiment and content analysis to identify factors associated with the running experience.

In particular, NLP sentiment modeling is based on big data, which may underrepresent people of lower socioeconomic status. The authors explicitly acknowledge these structural biases, and the current study also included content analysis with the aim of challenging the inherent biases of the NLP model through iterative interpretation and refinement by researchers.

Six themes explain why runs feel great or terrible

The researchers first conducted sentiment analysis to stratify posts along the emotional spectrum from -1 to +1, representing extreme negativity and extreme positivity, respectively. This method quickly classified sentiment in thousands of posts with high but imperfect accuracy, validated it against manual coding, and supplemented it with repeated checks for misclassifications.

This was followed by a content analysis of strongly positive and strongly negative contributions. This selective analysis identified the themes most likely to influence the runner experience.

The researchers decided to use an inductive content analysis. This method of qualitative text analysis aims to identify and organize patterns in the data based on interpretation rather than predefined categories. These patterns form the basis for valid and reproducible conclusions.

The content analysis revealed six categories and 26 subcategories. The most frequently mentioned categories included the following categories:

1. Psychologische Aspekte
2. Zwischenmenschliche Erfahrung
3. Wetter
4. Umgebung
5. Körperliche Erfahrung
6. Weg

Each had its subcategories. For example, psychological aspects found in 645 posts were classified into the categories joy, motivation, well-being and perceived performance.

Psychological aspects

The runners enjoyed running, their surroundings and the fun they had. All of this contributed to improved well-being.

Running can improve mood, relieve fatigue, and free up time for yourself at the same time. Reasons for running include health and social connections, while some also wanted to become better runners. Apps like Strava provided recognition and motivation through awards and performance tracking, which were key to endurance and improvement, especially during the pandemic that closed out other races.

Some users desperately wanted social recognition for their running on Strava, a tendency that men in the data set disproportionately expressed. These runners were less likely to explicitly focus their runs on their well-being, and previous research suggests that such comparison-oriented motivation may increase vulnerability to obsessive training patterns and burnout. However, the study itself did not directly measure risk.

Interpersonal experiences

The politeness of other runners, hikers or cyclists, as well as other people using the same space, influenced interpersonal experiences as well as feelings of safety. Congested areas were more likely to be associated with unpleasant experiences.

Supporting the running community and their families improved wellbeing. The increase in Strava running clubs suggests a broader acceptance of the social well-being associated with group running. While social running featured prominently in the data, the authors note that this is likely due to recruitment by running clubs and may overrepresent socially oriented runners. Many participants ran with others, but many also preferred to run alone.

During the pandemic, running was often used for socializing in public spaces. This correlates with previous observations that running activity has increased during the pandemic.

Weather

For the most part, pleasant weather was preferred for running. Most runners used paths or green spaces. Certain groups, especially trail runners, preferred more extreme conditions, including harsh terrain, cold, wind, and rain, to take risks, feel a sense of adventure, and discover unusual views or places. Winter roads were both dangerous and unpleasant for runners, especially road runners without snow or ice specific equipment.

Vicinity

The runners enjoyed being able to connect with nature in their surroundings, such as parks and trails, while also feeling more connected to the community and having a better sense of well-being. Encounters with wild animals triggered contradictory feelings. Runners preferred good lighting, especially when running in darker or less populated areas.

The traffic was disruptive, but many runners still used routes with moderate or high traffic. This may indicate restrictions on route selection, such as: B. time constraints, competing priorities, security considerations and distance limitations. The availability of public facilities was very important to runners, but was mentioned by few.

Physical experience

Most runners enjoyed the physical experience of running. Nausea, muscle soreness, or stiffness were sometimes thought to help build endurance and mental toughness, although runners clearly distinguished between manageable discomfort and injury-related pain. Injuries were common and often led to interruptions in training routines.

Away

Finally, runners overall did not show a clear preference for steep paths, despite the greater challenge and, for some, better scenery. Most runners liked smooth, well-maintained trails with semi-soft surfaces and good traction. Uninterrupted running rhythms are important for fun running.

What cities can learn from runners’ experiences

The results of this study demonstrate how personal, environmental and technological factors interact to influence runners' preferences and experiences. Unlike previous quantitative studies, this study combined sentiment analysis with inductive content analysis to foreground runners' own descriptions of their experiences at a narrative level rarely available in large datasets.

The study results are preliminary and require validation with larger, more diverse samples, including runners who do not use Strava, as well as further refinement of sentiment analysis methods. However, the use of inductive content analysis helped uncover hidden assumptions in large data models and enable analytical adjustments.

Given the global reach of Strava and similar platforms, this methodology has the potential to explore other types of physical activity to better promote environments that support different types of active travel.

The findings provide insights for urban planners and public health researchers while underscoring the need to exercise caution when implementing platform-specific data into policy.

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