The basis of heat generated by the human body has been a source of speculation and research for more than 2,000 years. Basal heat production, now usually referred to as resting energy expenditure (REE), is currently recognized as deriving from biochemical reactions at subcellular and cellular levels that are expressed in the energy expended by the body’s 78 organs and tissues. These organs and tissues, and the 11 systems to which they belong, influence body size and shape. Connecting these subcellular‐/cellular‐level reactions to organs and tissues, and then on to body size and shape, provides a comprehensive understanding of individual differences in REE, a contemporary topic of interest in obesity research and clinical practice. This review critically examines these linkages, their association with widely used statistical and physiological REE prediction formulas, and often‐unappreciated aspects of measuring basal heat production in humans. image

Anthropometric measurements have long been used to study human anatomic features related to body size and shape (1). The versatility and practicality of anthropometry led to the introduction of digital scanners in the 1980s designed to accurately capture the needed body dimensions for clothing manufacture (2-4). Rapid technological advancements over the past several decades have led to increased use of digital anthropometry in health care settings for acquiring clinically relevant anatomic measurements (5,6). However, these newer devices are still relatively costly and are typically nontransportable.The limitations of three-dimensional (3D) optical imaging devices prompted the development of smartphone applications (apps) that are designed to quantify body anthropometric dimensions comparable to those acquired by larger and more costly systems. To our knowledge, few studies have so far examined the accuracy and precision of these apps relative to the more conventional digital systems and to reference estimates using expert-measured body dimensions quantified with a flexible tape.The aim of the current study was to fill this gap by comparing anthropometric (circumference) measurements acquired with a free downloadable smartphone app (MeThreeSixty; Size Stream LLC,

Body composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. Body mass index (BMI) and other clinical or commercially available tools for quantifying body fat (BF) such as DXA, MRI, CT, and photonic scanners (3DPS) are often inaccurate, cost prohibitive, or cumbersome to use. The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF). The VBC algorithm is based on a state-of-the-art convolutional neural network (CNN). The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. 134 healthy adults ranging in age (21–76 years), sex (61.2% women), race (60.4% White; 23.9% Black), and body mass index (BMI, 18.5–51.6 kg/m2) were evaluated at two clinical sites (N = 64 at MGH, N = 70 at PBRC). Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems. The PBRC participants also had air displacement plethysmography (ADP) measured. %BF measured by dual-energy x-ray absorptiometry (DXA) was set as the reference against which all other %BF measurements were compared. To test our scientific hypothesis we run multiple, pair-wise Wilcoxon signed rank tests where we compare each competing measurement tool (VBC, BIA, …) with respect to the same ground-truth (DXA). Relative to DXA, VBC had the lowest mean absolute error and standard deviation (2.16 ± 1.54%) compared to all of the other evaluated methods (p < 0.05 for all comparisons). %BF measured by VBC also had good concordance with DXA (Lin’s concordance correlation coefficient, CCC: all 0.96; women 0.93; men 0.94), whereas BMI had very poor concordance (CCC: all 0.45; women 0.40; men 0.74). Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias −0.42%, R2 = 0.03; p = 0.062; LOA −5.5% to +4.7%), whereas all other evaluated methods had significant (p < 0.01) bias and wider limits of agreement. Bias in Bland-Altman analyses is defined as the discordance between the y = 0 axis and the regressed line computed from the data in the plot. In this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference; VBC performance exceeded those of all other BIA and ADP methods evaluated. The wide availability of smartphones suggests that the VBC method for evaluating %BF could play an important role in quantifying adiposity levels in a wide range of settings.Trial registration: ClinicalTrials.gov Identifier: NCT04854421.