Technologies and Systems for Assessing Human Energy Metabolism and Nutritional Rehabilitation
NCT07056504 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 80
Last updated 2025-09-02
Summary
The objective of this observational study is to integrate the doubly labeled water (DLW) database from healthy individuals with multimodal data-including, but not limited to, weight, height, age, sex, race, elevation-from cohorts undergoing rehabilitation following movement impairments or neurological injuries. Machine learning algorithms will be used to develop injury-specific predictive models of energy requirements.
The primary research question is:
How does energy metabolism change during the rehabilitation process in individuals recovering from traumatic brain injury, stroke, or major surgical procedures? To answer this, participants will undergo a comprehensive set of assessments, including measurements of height and weight, body composition, resting metabolic rate, physical activity levels, total energy expenditure, psychological health, food intake and hunger ratings, sleep quality, cognitive performance, non-invasive brain function monitoring, and gait analysis. Fecal and blood samples will also be collected for untargeted metabolomics analysis.
Conditions
- Energy Metabolism
- Nutrition, Healthy
Sponsors & Collaborators
-
Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
lead OTHER
Principal Investigators
-
Xueying Zhang, PhD · Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
-
John R Speakman, PhD · Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
Eligibility
- Min Age
- 20 Years
- Max Age
- 70 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2025-07-09
- Primary Completion
- 2025-12-31
- Completion
- 2025-12-31
Countries
- China
Study Locations
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