Social media platforms such as TikTok are significant sources of nutrition information for adolescents and young adults, who are vulnerable to unregulated, algorithm-driven content. This often spreads nutrition misinformation, impacting adolescent and young adult health and dietary behaviors. While previous research has explored misinformation on other platforms, TikTok remains underexamined, so this study aimed at evaluating the landscape of nutrition-related content on TikTok. This study evaluated TikTok nutrition-related content by (1) identifying common nutrition topics and content creator types; (2) assessing the quality and accuracy of content using evidence-based frameworks, and (3) analyzing engagement metrics such as likes, comments, and shares. The most common creators were health and wellness influencers (32%) and fitness creators (18%). Recipes (31%) and weight loss (34%) dominated the list of topics. When evaluating TikTok posts for quality, 82% of applicable posts lacked transparent advertising, 77% failed to disclose conflicts of interest, 63% promoted stereotypical attitudes, 55% did not provide evidence-based information, 75% lacked balanced and accurate content, and 90% failed to point out the risk and benefits of the advice presented. A total of 36% of posts were considered completely accurate, while 24% were mostly inaccurate, and 18% completely inaccurate. No statistical significance was associated between the level of accuracy or evidence and engagement metrics ( > 0.05). : TikTok prioritizes engagement over accuracy, exposing adolescents to harmful nutrition misinformation. Stricter moderation and evidence-based nutrition content are essential to protect adolescent and young adult health. Future research should explore interventions to reduce the impact of misinformation on adolescent dietary behaviors and mental well-being.
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http://dx.doi.org/10.3390/nu17050781 | DOI Listing |
Research (Wash D C)
March 2025
The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121012, China.
The metaverse enables immersive virtual healthcare environments, presenting opportunities for enhanced care delivery. A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications, which is a problem that needs to be addressed. This paper proposes a novel multimodal learning framework for metaverse healthcare, MMLMH, based on collaborative intra- and intersample representation and adaptive fusion.
View Article and Find Full Text PDFFront Plant Sci
February 2025
Automatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, Germany.
This is the first study who presents an approach to predict secondary metabolites content in tomatoes using multivariate time series classification of greenhouse sensor data, which includes climatic conditions as well as photosynthesis and transpiration rates. The aim was to find the necessary conditions in a greenhouse to determine the maximum content of secondary metabolites, as higher levels in fruits can promote human health. For this, we defined multiple classification tasks and derived suitable classification function.
View Article and Find Full Text PDFFront Genet
February 2025
State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China.
Introduction: plants are highly valued for their ornamental qualities. However, traditional morphological identification methods are inefficient for discriminating species. DNA barcoding has emerged as a powerful alternative for species identification, but research on DNA barcodes is still limited.
View Article and Find Full Text PDFFront Physiol
February 2025
Sports Coaching College, Beijing Sport University, Beijing, China.
Objective: To investigate the influence of physical and mental fatigue of different intensities (mild, moderate or severe) on basketball shooting accuracy, with the aim of informing more effective training protocols and competition strategies.
Methods: Literature searches were conducted on Web of Science, PubMed, and EBSCO databases up to 25 June 2024. Inclusion and exclusion criteria were specified, and data extraction sheets were prepared.
Front Oncol
February 2025
Department of Clinical Pharmacy, King Fahad Medical City, Riyadh, Saudi Arabia.
Introduction: This systematic review and meta-analysis aim to evaluate the efficacy of artificial intelligence (AI) models in identifying prognostic and predictive biomarkers in lung cancer. With the increasing complexity of lung cancer subtypes and the need for personalized treatment strategies, AI-driven approaches offer a promising avenue for biomarker discovery and clinical decision-making.
Methods: A comprehensive literature search was conducted in multiple electronic databases to identify relevant studies published up to date.
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