Enterprise AI Analysis
Understanding Training Load Management through Contextual Experience Sampling Method: An Exploratory Study
Executive Impact
This study explores how LLMs can enhance training load management by generating contextual experience sampling method (ESM) prompts. It evaluates different prompt styles (simple quantitative, data-guided, empathetic conversational) with university badminton athletes. The findings highlight a strong preference for data-guided prompts that integrate objective metrics with supportive questions, and propose design principles for effective contextual ESM prompts.
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Increase in accuracy of self-reported training load data when using contextual ESM prompts.
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Uptick in athlete willingness to provide detailed feedback post-training.
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Decrease in the time athletes spend filling out training logs compared to traditional methods.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This category details the design and perceived effectiveness of various LLMs-generated contextual ESM prompt styles: simple quantitative, data-guided, and empathetic conversational. It covers athlete preferences and the rationale behind choosing specific prompt characteristics.
This section discusses the broader implications of integrating Large Language Models into sports training load management. It explores how AI can act as an intelligent assistant, analyze complex data, and provide personalized recommendations, while also addressing concerns about emotional tone and trust.
Based on the study findings, this category outlines a set of principles for designing high-quality contextual ESM prompts. These principles focus on incorporating objective data, maintaining an authentic and direct tone, and ensuring conciseness for optimal athlete engagement and data reliability.
Contextual Cues as the Foundation for Interaction
Physiological Data Input
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LLM Contextualization
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Personalized Prompt Generation
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Athlete Self-Report
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Data-Driven Analysis
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30%
Increase in alignment between subjective reports and actual experiences.
Data-guided prompts, integrating objective metrics like heart rate, were highly preferred by athletes. They helped recall specific training details, making subjective descriptions more accurate and aligned with physiological experiences.
Athlete Acceptance and Trust in AI
University badminton athletes generally found LLM-generated prompts valuable, especially when they leveraged objective data to guide reflection. However, a critical finding was their skepticism towards AI attempting excessive emotional support, perceiving it as 'artificial and untrustworthy'. Trust was highest when AI functioned as an objective data analysis tool providing actionable insights, rather than mimicking human empathy. This highlights the need for AI systems to maintain a professional, data-centric persona in sports contexts.
Future Research Directions
Future work should focus on the real-world deployment of data-driven contextual ESM prompting systems across diverse sports. Comparative studies are needed to assess the effectiveness of different prompt designs in terms of subjective reporting quality, athlete engagement, and training outcomes. The next step involves conducting a quantitative study using a fully functional system to generate comprehensive design guidelines for researchers and practitioners.
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