Is it just a score? Understanding Training Load Management Practices Beyond Sports Tracking

Title of the Paper

Is it just a score? Understanding Training Load Management Practices Beyond Sports Tracking

Paper Information

• Subject Areas: Human-Computer Interaction (HCI), Sports Science, Personal Informatics
• Keywords: Training Load Management, Sports HCI, Running, Self-Tracking, Data Interaction, Sports Monitoring, Personal Data, Running-Related Injuries, Health Management, Behavioral Regulation

Research Background and Issues

• Research Questions and Challenges:

  • Runners need to optimize performance and reduce the risk of injuries through "Training Load Management" (TLM). However, while current sports trackers provide relevant data, their practical application in TLM remains unclear, and they lack features to help runners interpret and understand their training data.
  • There is a significant gap between the use of quantifiable, objective data and subjective perceptions (e.g., bodily signals).
  • The way data is edited and presented may not effectively help athletes avoid injuries or optimize performance. It may even lead runners to over-focus on single metrics, neglecting overall training goals.

• Research Objectives and Significance:

  • Investigate runners' data usage habits in real-world TLM practices through surveys and interviews.
  • Provide innovative directions for the design of sports trackers, aiming to develop the next generation of devices that better support TLM.
  • Lay the foundation for interdisciplinary collaboration between the scientific community and the HCI field, promoting the development of personalized sports management tools.

Solutions

• Research Methods:

  • Survey study (N=249): Collected behavioral data on runners' exercise habits, monitoring preferences, and trust in data.
  • In-depth interviews (N=24): Explored details not addressed by the survey, further investigating how runners understand, integrate, and adjust their training loads.

• Research Innovations and Contributions:

  • Proposed two TLM models: Guided TLM (relying on data and coach recommendations to enhance reasoning ability) and Autonomous TLM (a load management model based on individual perception).
  • Suggested a design shift for sports trackers from being data generators to learning supporters.
  • Highlighted the complex interaction between running enthusiasts and technological tools, emphasizing the shifting weight of trust, intuition, and data.

• Data and Technology Applications:

  • Runner data sources include directly measured metrics (e.g., heart rate, cadence) and derived metrics (e.g., recovery time, running quality scores).
  • Multi-perspective analysis: Combined runners' perceptions (bodily feedback) with objective metrics (sports tracker data) in dynamic interactions.

Research Findings

• Specific Findings:

  • Dynamic Relationship Between Data and Bodily Signals: Most runners tend to combine subjective feelings with data recommendations for TLM. However, as their experience grows, they increasingly rely on bodily sensations.
  • Prioritization of Running Tracker Data: Metrics like heart rate and distance are most favored, while trust in model-based results (e.g., recovery time) is relatively low.
  • Relationship Between Trust in Technology and Usage Intentions: Runners' trust in sports trackers directly influences their adherence to data recommendations. Transparency and personalized data presentation can improve trust.
  • Transition Between Autonomous and Guided TLM: Novice runners rely more on tracker recommendations, while experienced runners focus more on individual perception and training adjustment capabilities.

• Comparison with Existing Research:

  • Further confirmed the cognitive and decision-support role of sports data for non-professional athletes, while emphasizing the dominant role of "subjective perception" in actual training management.
  • Enhanced the Sports HCI field's understanding of the importance of integrating data with individual feedback, proposing that sports tracking tools should gradually serve to enhance runners' learning abilities rather than merely presenting data.

• Study Limitations and Future Directions:

  • The sample primarily consisted of males and was concentrated in Europe, lacking cultural and gender diversity. Future research should be more inclusive.
  • Research on TLM applications for other types of sports (e.g., team sports, swimming) remains underdeveloped.
  • Standardization and transparency of data remain key technical challenges; future studies could explore the application of artificial intelligence in deriving personalized training load recommendations.

Output Format Suggestions

To optimize the design of sports trackers related to TLM based on the actual needs of target users (runners):

• Enhance synergy with individual perception: Support runners in jointly adjusting training loads using subjective perception and data.
• Simplify data interpretation: Use intuitive interfaces to present comprehensive training recommendations rather than single metrics.
• Personalization and progressive support: Dynamically adjust the depth and complexity of TLM recommendations based on experience levels.

Through interdisciplinary collaboration (HCI, sports science), foster user trust and improve the practical use of running-related data, providing guidance for health and performance optimization across different types of sports.