Comparison of a Telehealth-Based Intensive Treatment Program with a Rewarding App vs. On-Site Care for Youth with Obesity: A Historical Cohort Study.

The recommended treatment for children with obesity includes numerous consultations by a multidisciplinary team, which is very cumbersome. Telehealth can assist in administering frequent care to children with obesity, yet the exact approaches and modes of delivery are still explored. During the COVID-19 pandemic, we developed an intensive telehealth-based treatment program that included a rewarding app for children with obesity. The aim of this study was to compare 6-month changes in body mass index (BMI) and body fat percent between participants in the program (n = 70) vs. children that underwent historic on-site care (n = 87). After 6 months, more participants in the telehealth group continued treatment compared to the on-site group (79% vs. 60%, p < 0.001). A significant reduction in the median BMI z-score (zBMI) was seen after 6 months in both groups (p < 0.01), with a similar proportion of zBMI reductions (71% in the telehealth group, 75% in the comparison group, p = 0.76). No statistically significant differences were found between the study groups in 6-month changes in BMI, zBMI, body fat percent or fat z-scores. We conclude that our telehealth program, which was executed during the COVID-19 pandemic, resulted in a high proportion of children with zBMI reduction that was comparable with the more personal on-site care.

Pediatric obesity and body weight following the COVID-19 pandemic.

BACKGROUND: The SARS-CoV-19 pandemic and its associated lockdowns affected children's lifestyle dramatically. The effect of such changes on children's weight and obesity status is unknown. The aim of this study was to compare body weight and obesity rates in children from before the pandemic to 6 months after the major periods of lockdowns in Israel. METHODS: We used data from medical records of pediatric emergency department visits, where weight is routinely measured, to compare weight and obesity prevalence in the fourth quartile of 2020 (n = 2468) as compared with the fourth quartiles of 2018-2019 (n = 5300). Weight was transformed to age- and sex-specific standard-deviation-scores (SDS) for analysis. RESULTS: Weight-SDS increased by a mean of 0.07 during the first 6 months of the pandemic, yet this was only significant in preschoolers. Obesity rates also increased in this age group only, by 37%, from 8.1% to 11.1% (p = 0.01). CONCLUSIONS: Weight-SDS and obesity prevalence increased during the SARS-CoV-19 pandemic, yet only in younger children. Additional studies from other populations are needed.

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

SUMMARY: Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). AVAILABILITY AND IMPLEMENTATION: MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. CONTACT: dslun@rutgers.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOST: a software environment for constraint-based metabolic modeling and strain design.

SUMMARY: MOST (metabolic optimization and simulation tool) is a software package that implements GDBB (genetic design through branch and bound) in an intuitive user-friendly interface with excel-like editing functionality, as well as implementing FBA (flux balance analysis), and supporting systems biology markup language and comma-separated values files. GDBB is currently the fastest algorithm for finding gene knockouts predicted by FBA to increase production of desired products, but GDBB has only been available on a command line interface, which is difficult to use for those without programming knowledge, until the release of MOST. AVAILABILITY AND IMPLEMENTATION: MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. CONTACT: dslun@rutgers.edu.