Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.2
4.8
Journals
Nutrients
Special Issues
Human Nutrition Research in the Data Era
nutrients-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Nutrients Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Human Nutrition Research in the Data Era

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition Methodology & Assessment".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2656

Special Issue Editors

Dr. Mikołaj Kamiński

E-Mail Website
Guest Editor
Department of Treatment of Obesity, Metabolic Disorders and Clinical Dietetics, Poznan University of Medical Sciences, 60-569 Poznan, Poland
Interests: probiotics; Google Trends; internet; twitter; smoking; tobacco
Dr. Damian Skrypnik

E-Mail Website
Guest Editor
Department of Treatment of Obesity, Metabolic Disorders and Clinical Dietetics, Poznan University of Medical Sciences, 60-569 Poznan, Poland
Interests: obesity; diabetes; hypertension; metabolic syndrome; obesity in pregnancy; leptin; VEGF
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We live in a transformative era, where the computerization of our daily lives has enabled scientists to utilize new, extensive sources of data. A significant number of these new datasets pertain to human nutrition and lifestyle.

I would like to invite you to collaborate on a Special Issue of Nutrients titled “Human Nutrition Research in the Data Era”. The scope of this Special Issue is very broad, encompassing research based on registers, search engine data, social media, commercially available medical devices, and more. I encourage you to submit both original research articles and review papers related to the theme of this Special Issue.

Dr. Mikołaj Kamiński
Dr. Damian Skrypnik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Twitter
  • social media
  • Google Trends
  • infodemiology
  • big data
  • search engines
  • data repository
  • AI
  • machine learning
  • Facebook
  • Instagram
  • TikTok

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 681 KiB  
Article
Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients
by Leon Kopitar, Leon Bedrač, Larissa J. Strath, Jiang Bian and Gregor Stiglic
Nutrients 2025, 17(9), 1492; https://doi.org/10.3390/nu17091492 - 29 Apr 2025
Viewed by 168
Abstract
Background/Objectives: Identifying and decomposing compound ingredients within meal plans presents meal customization and nutritional analysis challenges. It is essential for accurately identifying and replacing problematic ingredients linked to allergies or intolerances and helping nutritional evaluation. Methods: This study explored the effectiveness of three [...] Read more.
Background/Objectives: Identifying and decomposing compound ingredients within meal plans presents meal customization and nutritional analysis challenges. It is essential for accurately identifying and replacing problematic ingredients linked to allergies or intolerances and helping nutritional evaluation. Methods: This study explored the effectiveness of three large language models (LLMs)—GPT-4o, Llama-3 (70B), and Mixtral (8x7B), in decomposing compound ingredients into basic ingredients within meal plans. GPT-4o was used to generate 15 structured meal plans, each containing compound ingredients. Each LLM then identified and decomposed these compound items into basic ingredients. The decomposed ingredients were matched to entries in a subset of the USDA FoodData Central repository using API-based search and mapping techniques. Nutritional values were retrieved and aggregated to evaluate accuracy of decomposition. Performance was assessed through manual review by nutritionists and quantified using accuracy and F1-score. Statistical significance was tested using paired t-tests or Wilcoxon signed-rank tests based on normality. Results: Results showed that large models—both Llama-3 (70B) and GPT-4o—outperformed Mixtral (8x7B), achieving average F1-scores of 0.894 (95% CI: 0.84–0.95) and 0.842 (95% CI: 0.79–0.89), respectively, compared to an F1-score of 0.690 (95% CI: 0.62–0.76) from Mixtral (8x7B). Conclusions: The open-source Llama-3 (70B) model achieved the best performance, outperforming the commercial GPT-4o model, showing its superior ability to consistently break down compound ingredients into precise quantities within meal plans and illustrating its potential to enhance meal customization and nutritional analysis. These findings underscore the potential role of advanced LLMs in precision nutrition and their application in promoting healthier dietary practices tailored to individual preferences and needs. Full article
(This article belongs to the Special Issue Human Nutrition Research in the Data Era)
►▼ Show Figures

Figure 1

15 pages, 5001 KiB  
Article
Reasoning-Driven Food Energy Estimation via Multimodal Large Language Models
by Hikaru Tanabe and Keiji Yanai
Nutrients 2025, 17(7), 1128; https://doi.org/10.3390/nu17071128 - 24 Mar 2025
Viewed by 402
Abstract
Background/Objectives: Image-based food energy estimation is essential for user-friendly food tracking applications, enabling individuals to monitor their dietary intake through smartphones or AR devices. However, existing deep learning approaches struggle to recognize a wide variety of food items, due to the labor-intensive nature [...] Read more.
Background/Objectives: Image-based food energy estimation is essential for user-friendly food tracking applications, enabling individuals to monitor their dietary intake through smartphones or AR devices. However, existing deep learning approaches struggle to recognize a wide variety of food items, due to the labor-intensive nature of data annotation. Multimodal Large Language Models (MLLMs) possess extensive knowledge and human-like reasoning abilities, making them a promising approach for image-based food energy estimation. Nevertheless, their ability to accurately estimate food energy is hindered by limitations in recognizing food size, a critical factor in energy content assessment. Methods: To address this challenge, we propose two approaches: fine-tuning, and volume-aware reasoning with fine-grained estimation prompting. Results: Experimental results on the Nutrition5k dataset demonstrated the effectiveness of these approaches in improving estimation accuracy. We also validated the effectiveness of adapting LoRA to enhance food energy estimation performance. Conclusions: These findings highlight the potential of MLLMs for image-based dietary assessment and emphasize the importance of integrating volume-awareness into food energy estimation models. Full article
(This article belongs to the Special Issue Human Nutrition Research in the Data Era)
►▼ Show Figures

Figure 1

12 pages, 295 KiB  
Article
Detrimental Industries’ Sponsorship in Football Clubs Across Ten Major Leagues from 2000–2022: A Retrospective Study
by Mikołaj Kamiński, Wiktor Szymajda, Ada Kaczmarek, Matylda Kręgielska-Narożna and Paweł Bogdański
Nutrients 2024, 16(21), 3606; https://doi.org/10.3390/nu16213606 - 24 Oct 2024
Viewed by 1549
Abstract
Background: Sponsorship of football teams by detrimental industries may negatively impact fans’ dietary and behavioral choices. The study aimed to determine the proportion of sponsors on the jerseys of teams in the top ten football leagues that comprise companies producing alcohol or unhealthy [...] Read more.
Background: Sponsorship of football teams by detrimental industries may negatively impact fans’ dietary and behavioral choices. The study aimed to determine the proportion of sponsors on the jerseys of teams in the top ten football leagues that comprise companies producing alcohol or unhealthy food, or engaging in gambling. Methods: We conducted a retrospective study, incorporating data from first-division football teams in 10 countries (Argentina, Brazil, England, France, Germany, Italy, Poland, the Netherlands, Spain, and the United States) playing from 2000–2022. Data were collected on the primary sponsors displayed on team jerseys and categorized into alcohol, unhealthy food (defined as producers of ultra-processed food according to the NOVA classification), gambling, or other, based on the nature of the products or services offered by the sponsors. We performed descriptive statistical analyses and multivariate linear regression analyses. Results: A total of 4452 sponsorship records were analyzed. The majority were classified as “other” (81.8%), followed by gambling (6.9%), alcohol (2.6%), and unhealthy food (2.6%). We did not identify any sponsor representing the tobacco industry. The prevalence of gambling sponsors surged from 1.7% in 2000 to 16.3% in 2022. Conversely, alcohol-related sponsorships dwindled from 6.2% in 2000 to 1.0% in 2022. In the multivariate linear regression model, these trends were statistically significant. The alcohol industry remained visible in the Spanish league. Conclusions: A significant proportion of sponsorships on the jerseys of top football teams across the world represents alcohol, ultra-processed food, or the gambling industry. Trends in the types of sponsors on the jerseys of leading football clubs across the Western world are diverse. Particularly concerning is the recent increase in the percentage of clubs sponsored by the gambling industry. To limit the detrimental effects of the promotion of unhealthy products, novel policies should be considered. Full article
(This article belongs to the Special Issue Human Nutrition Research in the Data Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop