Beyond Body Mass Index: Accurate Metabolic Disease-Risk Phenotyping With 3D Smartphone Application.

Objective: Smartphone applications (apps) with optical imaging capabilities are transforming the field of physical anthropometry; digital measurements of body size and shape in clinical settings are increasingly feasible. Currently available apps are usually designed around the capture of two-dimensional images that are then transformed with app software to three-dimensional (3D) avatars that can be used for digital anthropometry. The aim of the current study was to compare waist circumference (WC), hip circumference (HC), four other circumferences (right/left upper arm, thigh) and WC/HC evaluated with a novel high-precision 3D smartphone app to ground-truth measurements made with a flexible tape by a trained anthropometrist.

Are Lean Body Mass and Fat-Free Mass the Same or Different Body Components? A Critical Perspective.

The 2-component molecular-level model dividing body mass into fat and fat-free mass (FFM) is a cornerstone of contemporary body composition research across multiple disciplines. Confusion prevails, however, as the term lean body mass (LBM) is frequently used interchangeably with FFM in scientific discourse. Are LBM and FFM the same or different body components? Captain Albert R.

Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.

Objective: To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings.

Methods: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.

Objective: To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.

Methods: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations.