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Mobilize & Restore Centers Webinar: Energetics and Big Data Approach Explain Ecological Running Speeds

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  • Mobilize & Restore Centers Webinar: Energetics and Big Data Approach Explain Ecological Running Speeds

    The Mobilize Center and Restore Center at Stanford University invite you to join our next webinar, featuring Jess Selinger from Queens University.


    DETAILS

    Title: Energetics and Big Data Approach Explain Ecological Running Speeds

    Speaker: Jess Selinger, Queens University

    Time: Wednesday, August 31st, 2022 at 9:00 AM Pacific Time

    Registration: Click here to register


    ABSTRACT

    Human runners have long been thought to have the ability to consume a near-constant amount of energy per distance traveled, regardless of speed, allowing speed to be adapted to particular task demands with minimal energetic consequence. However, recent and more precise laboratory measures indicate that humans may in fact have an energy-optimal running speed. Here, Dr. Selinger and colleagues use activity tracking data from thousands of free-living runners to determine if real-world preferred speeds are consistent with task- or energy-dependent objectives.

    In the first half of the webinar, Dr. Selinger will show that free-living runners, most often out for a jog and not a race, prefer a particular running speed largely independent of run distance. Moreover, these preferred speeds are remarkably consistent with the objective of minimizing energy expenditure. These findings offer insight into the biological objectives that shape human running preferences in the real world—an important consideration when examining human ecology or creating training strategies to improve performance and prevent injury.

    In the second half of the webinar, Dr. Selinger will discuss best practices for, and share lessons learned from, working with large-scale physical activity data from wearables. In particular, she will present examples from her work of how to: 1. identify and combine datasets to answer your question of interest, 2. clean and prepare your datasets for analysis, and 3. challenge and verify the robustness of your conclusions.
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