Key result
Body composition measurement techniques offer unique advantages and limitations across clinical and research populations.
Why the study?
Body composition measurement techniques still face problems related to measurement reliability and applicability across different populations despite rapid technological advances.
This comprehensive overview highlights the shift towards personalized and multi-dimensional body composition assessment for health management, disease prevention, and clinical monitoring across different populations.
May guide technique selection in monitoring; extends summaries but leaves open standardized validation across populations.
The measurement of body composition is one of the most important tools for evaluating an individual's health and nutritional status. Its field has developed rapidly in recent years due to technological advances and growing application needs. Currently, widely used measurement methods include dual-energy x-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and smartphone software based on Computer Vision (CV) and Artificial Intelligence (AI). Each method has its own advantages in terms of accuracy and convenience. However, these techniques still face problems related to measurement reliability and applicability across different populations. The purpose of this paper is to provide a comprehensive overview of the technologies, target populations, and research fields related to body composition measurement. Body composition measurement is an important part of health management, non-communicable disease management, clinical medicine, and sports and physical training. Along with new scientific advances, this article also presents potential future research directions in body composition measurement.
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Jing et al. (2026) conducted a review in Body composition measurement. Body composition measurement techniques was evaluated. Various body composition measurement techniques, including DXA, BIA, MRI, CT, and AI-based smartphone software, offer unique advantages and limitations across different clinical and research populations.
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