Background: Consumption of ultra-processed foods (UPFs) is responsible for an increasing proportion of non-communicable diseases and premature mortality. Recognition of the commercial and social determinants of UPF consumption represents an important advance in public health, with implications for interventions that emphasize regulatory policies rather than individual motivation. However, it is important not to lose sight of the motivational mechanisms through which commercial and social determinants exert their effects on unhealthy behavior.
Objective: This commentary highlights "Big Food's" exploitation of psychological hedonism-a fundamental human motivational process-as the critical mechanism of UPF consumption, with disproportionate effects on historically marginalized communities. It is not mere availability of UPFs that is the problem. It is the intentional and strategic engineering of UPFs to appeal to the most basic human motivational system that drives our desire and consumption of UPFs. The framing of UPF consumption as the exploitation of natural human motivational tendencies has the potential for increasing the public's acceptance of food regulation policies.
Conclusion: In bolstering public support for UPF-regulation we should proliferate the following message: Just like Big Tobacco, Big Food strategically engineers UPFs to manipulate fundamental human motivational processes and amass profits at the expense of public health.
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http://dx.doi.org/10.1016/j.pmedr.2024.102902 | DOI Listing |
Diab Vasc Dis Res
January 2025
Department of Endocrinology and Metabolism, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Background: This study aimed to investigate the effects of oral semaglutide on the changes in food preference of Japanese patients with type 2 diabetes.
Methods: This retrospective multicenter study included 75 patients with type 2 diabetes who received oral semaglutide. The primary outcome was the change in the score of brief-type self-administered diet history questionnaire (BDHQ) score 3 months after the initiation of oral semaglutide treatment.
Aim: Human carbonic anhydrases (hCAs) are involved in many physiological processes including respiration, pH control, ion transport, bone resorption, and gastric fluid secretion. Recently, CA IX and CA XII have been studied for their role in cancer diseases, motivating the design of inhibitors of these isoforms.
Material And Method: Here, we used the tail approach to design a new series of monoaryl () and bicyclic () benzensulfonamide derivatives CA IX and CA XII inhibitors.
J Rural Health
January 2025
Department of Health Disparities Research, Division of Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Purpose: This qualitative study assessed internet access and use, barriers and facilitators to participating in digital health interventions or programs, and the engagement experience in virtual versus in-person health interventions among rural adults and rural cancer survivors.
Methods: Rural adults (n = 10) and rural cancer survivors (n = 10) were recruited from previous studies to participate in an in-depth interview. The interview guide contained eight open-ended questions related to participation in technology-based programs.
Front Public Health
January 2025
Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Republic of Korea.
Objective: This study assessed the effects of transcranial direct current stimulation (tDCS) on cue reactivity and craving for game-related cues using event-related potentials (ERPs) in internet gaming disorder (IGD) patients.
Methods: At baseline, a series of game-related and neutral pictures were shown to both IGD and healthy controls (HCs) while ERPs were recorded. Late positive potentials (LPP) were used to investigate cue reactivity.
FACCT 24 (2024)
June 2024
IBM Research Cambridge, Massachusetts, USA.
The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classification and analysis. At the same time, the potentially significant impacts of these models have motivated the development of a range of explainable AI (XAI) methods that aim to explain model predictions given certain image inputs. However, many of these methods are not developed or evaluated with domain experts, and explanations are not contextualized in terms of medical expertise or domain knowledge.
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