Measuring energy needs during rehabilitation
Official title: Active Rehabilitation Training System for Motor and Cognitive Function Technologies and Systems for Assessing Human Energy Metabolism and Nutritional Rehabilitation
Run by Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences · NCT07056504 · Data updated 2026-11-12
This project will try to use metabolic measurements and machine learning to predict calorie needs for adults recovering from stroke, traumatic brain injury, or major surgery.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 20 Years to 70 Years |
| Sex | All |
| Enrollment | 80 (estimated) |
| Sponsor | Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences Academic / other |
| Locations | 1 site (Foshan, Guangdong) |
| Status | Recruiting |
| Started | July 9, 2025 |
| Main results expected | December 31, 2025 |
| Study ends | December 31, 2025 |
| Registry record updated | September 2, 2025 |
| Trial ID | NCT07056504 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 20–70 undergoing rehabilitation for stroke, traumatic brain injury, motor injuries, or major surgical recovery who can lie flat for testing, are not pregnant, and have no metallic implants are ideal candidates.
✕Probably not a fit
Patients with loss of autonomous mobility or consciousness, metallic implants (e.g., pacemakers), severe metabolic diseases (such as uncontrolled diabetes), claustrophobia, anorexia nervosa, uremia, or inability to lie flat for one hour are excluded and unlikely to benefit from participation.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
Researchers will combine a doubly labeled water (DLW) database from healthy people with detailed, multimodal data collected from rehabilitation patients to build predictive models of energy requirements. Participants will undergo anthropometry, body composition, resting metabolic rate by indirect calorimetry, total energy expenditure by DLW and a metabolic chamber, activity monitoring, dietary intake and hunger ratings, sleep and cognitive testing, noninvasive brain monitoring, and gait analysis. Urine and other biological samples will be collected and DLW samples will be analyzed at a central laboratory, with machine learning used to develop injury-specific prediction algorithms. The goal is to characterize how energy metabolism changes across rehabilitation and to create tools to estimate individualized energy needs.
Why it matters
Potential benefit: If successful, clinicians could use the models to personalize nutrition plans during rehabilitation, reducing under- or over-feeding and potentially improving recovery outcomes.
How similar studies have gone: Doubly labeled water and indirect calorimetry are well-established methods for measuring energy expenditure and have been successful in healthy and some patient populations, but applying machine-learning to create injury-specific energy-prediction models is a relatively novel approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Male and female participants aged 20 to 70 years old; * Rehabilitation patients with stroke or motor injuries; Exclusion Criteria: * Loss of autonomous mobility; * Loss of consciousness; * Individuals with metallic implants (e.g., pacemakers); * Severe metabolic diseases (e.g., diabetes, other hereditary metabolic disorders); * Patients with claustrophobia; * Individuals with anorexia nervosa; * Women in the preconception period, pregnancy, or lactation; * Uremic patients; * Inability to lie flat for one hour.
Where it's running
-
Guangdong Jianxiang Hospital Group
Foshan, Guangdong, China Recruiting
Study contacts
- Principal investigator: Xueying Zhang, PhD — Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
- Study coordinator: Xueying Zhang, Doctor
- Email: zhangxy@siat.ac.cn
- Phone: 86+18201296155
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.