ICE lab work on human metabolism is recognized at RACMEM 2025
© 2025 Chinese Academy of Science
On October 13-17, 2025, Prof. Dolaana Khovalyg and Fariza Sabit, a second-year PhD student at the ICE lab, traveled to Shenzhen (China) for the prestigious Recent Advances and Controversies in Measuring Energy Metabolism (RACMEM 2025) conference. Hosted by a renowned biologist, Prof. John Speakman, this gathering brings together the world’s leading voices to tackle the grand challenge of measuring energy expenditure in daily life.
We are thrilled to announce that the team’s work received significant international recognition, with Fariza winning the Life Metabolism Scholarship, a distinction awarded to only three projects globally.
The "Activity Paradox": Why Your Smartwatch Might Be Wrong
Fariza presented "The Activity Paradox: A New Framework for Metabolic Modeling," a systematic review of 63 studies that deconstructs why laboratory-validated models often fail when we step out into the real world. The study identifies a fundamental flaw called the "Activity Paradox": models fall short because they are designed to isolate the energy cost of motion while ignoring the dynamic, non-resting metabolic context — like what you ate, the time of day, and the temperature around you — in which that activity occurs.
A New Framework for Precision
To bridge this "lab-to-life" chasm, the ICE lab proposes a paradigm shift that deconstructs metabolic variability along two intertwined axes:
The axis of temporal resolution (Capturing the "Right Now"):
- While daily totals might look accurate, they often hide substantial minute-by-minute errors.
- At a granular level, "invisible" factors like the energy used to digest food (Postprandial thermogenesis) become glaring liabilities for current models.
- This creates a "Resolution Trilemma": a tug-of-war between the need for detailed insight, the demand for statistical accuracy, and the practical limits of wearable tech.
The Axis of Personalization (Capturing the "Unique You"):
- The lab's "Ladder of Personalization" moves us away from generic population averages that treat everyone as metabolically uniform.
- The goal is to climb toward fully individualized models that use real-time signals, such as muscle activity (EMG) and behavioral signatures, to "learn" a person's unique metabolic parameters over time.
- This approach represents a promising path toward resolving the challenge of biological diversity.
"It was an honor to present our framework to such a specialized audience and receive validation from leaders in the field," said Fariza.
In addition to the award-winning presentation, the team showcased a poster titled “Metabolic rate without movement: The role of cognitive load,” exploring how mental effort impacts our metabolic baseline — a perfect example of the "non-resting" factors current models often miss.
Congratulations to the ICE lab for their contribution to the global dialogue on energy metabolism!
