shapecoder: a new method for visual quantification of body mass index in young children

Supporting families to achieve a healthy weight development for their child with overweight/obesity using the STARKIDS intervention: study protocol for a cluster-randomized controlled trial

Background

Childhood and adolescent overweight and obesity are among the most serious health challenges today. Structured weight reduction programs can be helpful to reduce severe health consequences but evidence is partly scarce. The STARKIDS program aims to improve on some of these limitations and is designed to be a structured, stepwise, digitally supported intervention program for the whole family. It is divided into two intervention steps spanning over 1.5 years and aims at promoting a healthy weight development of children/adolescents with overweight/obesity and an increase in quality of life.

Methods

The STARKIDS intervention is evaluated in a cluster-randomized study design by comparing it with a control group receiving a one-time structured counselling in the pediatric practice. The study aims to include 1000 families with children/adolescents with overweight/obesity from 100 pediatric practices. The main outcomes are reduction in body mass index percentiles and improvements in children’s/adolescent’s quality of life, secondary outcomes refer to the contents of the intervention such as diet, physical activity, stress, and media habits. All outcomes are measured on three measurement time points: (T0) at baseline/inclusion in the study, (T1) baseline + 12 months which is the end of step 1 of the STARKIDS intervention, and (T2) baseline + 18 months which is the end of step 2 of the STARKIDS intervention.

Discussion

The stepwise, e-health-supported STARKIDS program is a low-threshold intervention program for families with children/adolescents with overweight/obesity. With the proof of concept, STARKIDS provides the potential to be implemented as a standard care tool for the prevention and intervention of childhood/adolescence obesity in the German health system.

Trial registration

German Clinical Trials Register (DRKS) DRKS00022813 (acknowledged primary register of the World Health Organization). Registered on 27 November 2020 (Universal Trial Number U1111-1254-9536).

Studying Children's Eating at Home: Using Synchronous Videoconference Sessions to Adapt to COVID-19 and Beyond

The COVID-19 pandemic has disrupted many facets of developmental research, including research that measures children's eating behavior. Here, children's food intake is often measured by weighing foods that children are offered before and after in-person testing sessions. Many studies also examine children's food ratings (the extent to which they like or dislike a food), assessed via picture categorization tasks or hedonic scales. This paper reviews existing research on different methods for characterizing children's eating behavior (with a focus on food intake, preferences, and concepts) and presents a feasibility study that examined whether children's eating behaviors at home (including their food intake and ratings) can be measured via live video-chat sessions. The feasibility analyses revealed that an observational feeding paradigm at home yielded a majority (more than 70%) of video-chat recordings that had a sufficient view of the child and adequate sound and picture quality required for observational coding for the majority of the session's duration. Such positioning would enable behavioral coding of child food intake, parent food talk, and meal characteristics. Moreover, children were able to answer questions to stories and express their preferences via researcher screen-share methods (which can assess children's self-reported food preferences and beliefs) with low rates of exclusion across studies. The article ends with a discussion on the opportunities and challenges of using online platforms to conduct studies on children's eating behaviors in their home environments during the COVID-19 pandemic and beyond.

Development and Application of Digital Human Models in the Field of Vehicle Collisions: A Review

In the human-vehicle-road system of collisions, the human is the most important factor, and digital human models (DHMs) are developed with the aim of preventing or at least reducing human injury. Because most of the relevant literature is focused mainly on collisions in traffic accidents (TAs), only some of the literature reviewed in this paper involves research results on other aspects of collisions. In this review, based on the background of DHMs and the application of DHMs regarding human injury biomechanics in collisions field, research results regarding the development of DHMs are described, the methods for verifying such models are introduced, and the application of the research results is discussed based on the aspect of human injury biomechanics. From the research literature, the development and validation of DHMs and their application in human injury biomechanics are summarized, and future research trends are proposed and discussed.

Shape-based Three-dimensional Body Composition Extrapolation Using Multimodality Registration

The ubiquity of commodity-level optical scan devices and reconstruction technologies has enabled the general public to monitor their body shape related health status anywhere, anytime, without assistance from professionals. Commercial optical body scan systems extract anthropometries from the virtual body shapes, from which body compositions are estimated. However, in most cases, these estimations are limited to the quantity of fat in the whole body instead of a fine-granularity voxel-level fat distribution estimation. To bridge the gap between the 3D body shape and fine-granularity voxel-level fat distribution, we present an innovative shape-based voxel-level body composition extrapolation method using multimodality registration. First, we optimize shape compliance between a generic body composition template and the 3D body shape. Then, we optimize data compliance between the shape-optimized body composition template and a body composition reference from the DXA pixel-level body composition assessment. We evaluate the performance of our method with different subjects. On average, the Root Mean Square Error (RMSE) of our body composition extrapolation is 1.19%, and the R-squared value between our estimation and the ground truth is 0.985. The experimental result shows that our algorithm can robustly estimate voxel-level body composition for 3D body shapes with a high degree of accuracy.