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);