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.
Male and female participants aged 20 to 70 years old;
Rehabilitation patients with stroke or motor injuries;
Loss of autonomous mobility;
Loss of consciousness;
Individuals with metallic implants (e.g., pacemakers);
Severe metabolic diseases (e.g., diabetes, other hereditary metabolic disorders);
Individuals aged 20 to 70 years, both male and female, who are recovering from stroke or motor injuries are eligible for this study; however, those with the following conditions are not permitted to participate: pregnancy, pre-pregnancy period, lactation, loss of consciousness or mobility, severe metabolic diseases (such as diabetes, other hereditary metabolic disorders, etc.), psychogenic anorexia, uremia, and inability to lie flat for one hour. Additionally, individuals with contraindications for magnetic resonance imaging (MRI) are excluded from this trial; these include a history of pacemaker implantation, presence of metallic implants in the body, and claustrophobia. This criterion ensures that all participants meet the necessary health and safety requirements for the study.
Cohort
Cross-sectional
Not listed
Height
Measured by seca 217 stable stadiometer. Subjects wearing no shoes.
Resting Energy Expenditure (REE)
REE will be measured via indirect calorimetry using a respiratory hood system (Cosmed). Participants will report to the laboratory after an overnight fast, lie supine on a flat bed, and have the hood placed over their head. Oxygen consumption and carbon dioxide production will be recorded for 40 minutes, with the final 10 minutes used for analysis. The system will be quality-checked regularly using a turbine test, and monthly validation will be conducted via an alcohol combustion test.
Total Energy Expenditure (TEE)
TEE will be assessed using two complementary methods: Doubly Labeled Water (DLW) Method: Urine samples from the DLW subset will be stored at -20 °C and shipped on dry ice to Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. Isotope ratios will be analyzed using a Liquid Water Isotope Analyzer (ABB). CO₂ production will be calculated using the latest equation from Speakman et al. (2021, Cell Reports Medicine), and TEE will be estimated using the Weir equation. Metabolic Chamber: TEE will also be measured using a whole-body metabolic chamber (Maastricht Instruments, Netherlands), which continuously tracks oxygen consumption and carbon dioxide production. Participants will remain in the chamber for 48 hours, engaging in routine daily activities. Data from the second day will be analyzed to estimate TEE.
Physical Activity Level
During the measurement period, all participants will be required to wear a three-axis accelerometer (Actigraph GT3XP-BTLE, USA) for 15 days, covering 24 hours a day, including sleep. Participants can remove the accelerometer only when showering, bathing, swimming, or engaging in other water activities. This device will provide multiple parameters of physical activity levels, including raw acceleration data, activity counts, and vector magnitudes. Raw acceleration data captures subtle changes in individual movements, activity counts measure the intensity of physical activity over specific time periods, and vector magnitudes integrate acceleration information from all directions. These three indicators were used to comprehensively evaluate participants' physical activity levels.
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