Background: American adults have gained weight during the COVID-19 pandemic. Little is known about how patients who are medically managed for overweight and obesity, including patients who are prescribed antiobesity pharmacotherapy, have fared.
Objective: To assess the COVID-19 pandemic's effect on weight, food choices, and health behaviors in patients receiving medical treatment for overweight or obesity.
Methods: Adult patients treated at an urban academic weight management center between 1 May 2019 and 1 May 2020 were electronically surveyed between 23 February and 23 March 2021. The survey assessed changes in weight, eating, behaviors, and the use of antiobesity medications (AOMs) following issuance of social distancing/stay-at-home policies in March 2020.
Results: In 970 respondents, median percent weight change for those taking AOMs was -0.459% [interquartile range -5.46%-(+3.73%)] compared to +2.33% [IQR -1.92%-(+6.52%)] for those not taking AOMs ( < 0.001). More participants achieved ≥5% weight loss if they were taking AOMs compared to those who were not (26.7% vs. 15.8%, = 0.004), and weight gain ≥5% was also lower in those taking AOMs (19.8% vs. 30.3%, = 0.004). Patients with pre-pandemic BMI ≥30 kg/m taking AOMs experienced the greatest weight reduction, and there was greater weight loss associated with increased physical activity.
Conclusions And Relevance: Medical weight management protected against weight gain during this period of the COVID-19 pandemic. Increased physical activity, decreased alcohol intake, and use of AOMs were factors that contributed to this protective effect.
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http://dx.doi.org/10.1002/osp4.601 | DOI Listing |
Sci Rep
December 2024
Department of Movement and Sport Sciences, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
The transition from secondary school to college or university is a well-known and well-studied risk period for weight and/or fat gain and not meeting the dietary recommendations. Higher education acts as a promising setting to implement nutrition interventions. An important condition for intervention success is that interventions are implemented as intended by the protocol and integrated in the institutional policy.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computing and Information Systems, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).
View Article and Find Full Text PDFJ Econ Entomol
December 2024
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, China.
Insects provide important pollination services for cops. While land use intensification has resulted in steep declines of wild pollinator diversity across agricultural landscapes, releasing managed honeybees has been proposed as a countermeasure. However, it remains uncertain whether managed honeybees can close the pollination gap of sunflower (Helianthus annuus L.
View Article and Find Full Text PDFBackground: It is essential to understand factors influencing young adult cardiovascular health (CVH) to reduce morbidity and mortality.
Objective: Evaluate longitudinal changes in CVH among young adults in a weight management intervention.
Methods: Life's Essential 8 (LE8) metrics were calculated for young adults with overweight and obesity enrolled in a randomized controlled trial (n = 459).
Ecol Lett
January 2025
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Ecosystem models are often used to predict the consequences of management interventions in applied ecology and conservation. These models are often high-dimensional and nonlinear, yet limited data are available to calibrate or validate them. Consequently, their utility as decision-support tools is unclear.
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