: Since numerical calorie labels have limited effects on less-calorie food ordering, an alternative called physical activity calorie equivalent (PACE) labels, which exhibit calories using visible symbols and the minutes of exercise to burn off the calories, may be more effective in reducing calories ordered. : By using a choice experiment (CE) approach, the aims of this study were to estimate the effects of PACE labels on consumer preferences for healthy and unhealth food. Red date walnuts and potato chips were used as the representatives of healthy and unhealthy foods respectively in this study. Moreover, future time perspective (FTP) is an individual trait variable of consumers, which has been recognized as a significant driver of healthy behaviors. We also included FTP into the interaction with PACE labels. : Firstly, the results were opposite between the healthy and unhealthy food groups. Respondents showed significantly more positive attitudes toward red date walnuts (i.e., healthy food) with PACE labels, while they showed significantly more negative preferences for chips (i.e., unhealthy food) with PACE labels. Secondly, people with higher FTP are preferred red date walnuts with PACE labels, while PACE labels on chips could undermine the preferences of respondents with higher FTP. Thirdly, we found that women (vs. men) were less inclined to choose healthy food with standard calorie labels and labels showing the minutes of running to burn off the calories, as well as that the elderly (vs. younger) people in the healthy food group preferred the labels showing the minutes of running to burn off the calories. People with a higher body mass index (BMI) were reluctant to purchase walnuts with the information about the minutes of walking. : Results from this study showed that PACE labels have significant effects on consumers' preferences for food products.
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http://dx.doi.org/10.3390/ijerph18041860 | DOI Listing |
Diagnostics (Basel)
December 2024
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability.
View Article and Find Full Text PDFNat Commun
January 2025
Replicate Bioscience Inc, San Diego, CA, USA.
Self-replicating RNA (srRNA) technology, in comparison to mRNA vaccines, has shown dose-sparing by approximately 10-fold and more durable immune responses. However, no improvements are observed in the adverse events profile. Here, we develop an srRNA vaccine platform with optimized non-coding regions and demonstrate immunogenicity and safety in preclinical and clinical development.
View Article and Find Full Text PDFPacing Clin Electrophysiol
December 2024
Department of Cardiology, Holy Family Hospital, Mumbai, India.
Background: The degree and time course of improvement in left ventricular (LV) function with treatment in patients with tachycardiomyopathy (TCMP) is highly variable. This study aims to clinically characterize the recovery of TCMP based on the extent and course of improvement in LV function and identify predictors of complete myocardial recovery.
Methods: In this prospective, single-center, observational study, patients with suspected TCMP who underwent successful tachyarrhythmia termination/control were included.
Tob Control
December 2024
Institute of Social Marketing and Health, University of Stirling, Stirling, UK.
Front Dev Psychol
May 2024
Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI.
Background: Infants and toddlers engage with digital media about 1-3 hours per day with a growing proportion of time spent on YouTube.
Aim: Examined content of YouTube videos viewed by children 0-35.9 months of age and predictors of YouTube content characteristics.
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