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Nutrients
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A Path Towards Personalized Smart Nutrition
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A Path Towards Personalized Smart Nutrition

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 5767

Special Issue Editor

Prof. Dr. Veronica Mocanu

E-Mail Website
Guest Editor
Morpho-Functional Sciences II Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
Interests: nutritional and metabolic diseases; healthy lifestyle; obesity prevention and management; stress-induced eating behavior; emerging technologies in diet-related disease management

Special Issue Information

Dear Colleagues,

The rising demand for personalized nutrition needs innovative approaches to ensure accessibility, accuracy, and effectiveness for a diverse population. Personalized smart nutrition is a tailored approach to dietary planning and management utilizing advanced technology, data analytics, and artificial intelligence (AI) to create individualized nutrition plans. This concept combines insights from various sources, such as genetic data, metabolic profiles, lifestyle habits, and health goals, to recommend optimal dietary choices for an individual. Additionally, it requires robust infrastructure for data privacy and security and collaboration among healthcare providers, nutrition experts, and tech companies to deliver comprehensive and user-friendly solutions. Possible innovative solutions include 1. genetic and biomarker analysis; 2. metabolic profiling, 3. data-driven recommendations; 4. behavioral insights; and 5. continuous monitoring and feedback. The tools and technologies involved are wearable devices (track physical activity, heart rate, and other vital statistics), mobile apps (assist with food logging, and nutrient tracking, and provide real-time recommendations), AI and machine learning (analyze large datasets to deliver personalized nutrition advice), and genomic testing kits (provide insights into how an individual’s DNA influences their nutritional needs).

Prof. Dr. Veronica Mocanu
Guest Editor

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

  • personalized smart nutrition
  • personalized diet plan
  • nutrient monitoring
  • remote health monitoring systems
  • wearable devices
  • mobile apps
  • virtual reality
  • genomic testing
  • metabolic profiling
  • AI and machine learning

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Published Papers (3 papers)

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Research

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23 pages, 1079 KiB  
Article
Optimizing Nutritional Care with Machine Learning: Identifying Sarcopenia Risk Through Body Composition Parameters in Cancer Patients—Insights from the NUTritional and Sarcopenia RIsk SCREENing Project (NUTRISCREEN)
by Giuseppe Porciello, Teresa Di Lauro, Assunta Luongo, Sergio Coluccia, Melania Prete, Ludovica Abbadessa, Elisabetta Coppola, Annabella Di Martino, Anna Licia Mozzillo, Emanuela Racca, Arianna Piccirillo, Vittoria Di Giacomo, Martina Fontana, Maria D’Amico, Elvira Palumbo, Sara Vitale, Davide D’Errico, Valeria Turrà, Ileana Parascandolo, Tiziana Stallone, Livia S. A. Augustin, Anna Crispo, Egidio Celentano and Sandro Pignataadd Show full author list remove Hide full author list
Nutrients 2025, 17(8), 1376; https://doi.org/10.3390/nu17081376 - 18 Apr 2025
Viewed by 261
Abstract
Background/Objectives: Cancer and related treatments can impair body composition (BC), increasing the risk of malnutrition and sarcopenia, poor prognosis, and Health-Related Quality of Life (HRQoL). To enhance BC parameter interpretation through Bioelectrical Impedance Analysis (BIA), we developed a predictive model based on [...] Read more.
Background/Objectives: Cancer and related treatments can impair body composition (BC), increasing the risk of malnutrition and sarcopenia, poor prognosis, and Health-Related Quality of Life (HRQoL). To enhance BC parameter interpretation through Bioelectrical Impedance Analysis (BIA), we developed a predictive model based on unsupervised approaches including Principal Component Analysis (PCA) and k-means clustering for sarcopenia risk in cancer patients at the Istituto Nazionale Tumori IRCCS “Fondazione G. Pascale” (Naples). Methods: Sarcopenia and malnutrition risks were assessed using the NRS-2002 and SARC-F questionnaires, anthropometric measurements, and BIA. HRQoL was evaluated with the EORTC QLQ-C30 questionnaire. PCA and clustering analysis were performed to identify different BC profiles. Results: Data from 879 cancer patients (mean age: 63 ± 12.5 years) were collected: 117 patients (13%) and 128 (15%) were at risk of malnutrition and sarcopenia, respectively. PCA analysis identified three main components, and k-means determined three clusters, namely HMP (High Muscle Profile), MMP (Moderate Muscle Profile), and LMP (Low Muscle Profile). Patients in LMP were older, with a higher prevalence of comorbidities, malnutrition, and sarcopenia. In the multivariable analysis, age, lung cancer site, diabetes, and malnutrition risk were significantly associated with an increased risk of sarcopenia; among the clusters, patients in LMP had an increased risk of sarcopenia (+62%, p = 0.006). Conclusions: The NUTRISCREEN project, part of the ONCOCAMP study (ClinicalTrials.gov ID: NCT06270602), provides a personalized nutritional pathway for early screening of malnutrition and sarcopenia. Using an unsupervised approach, we provide distinct BC profiles and valuable insights into the factors associated with sarcopenia risk. This approach in clinical practice could help define risk categories, ensure the most appropriate nutritional strategies, and improve patient outcomes by providing data-driven care. Full article
(This article belongs to the Special Issue A Path Towards Personalized Smart Nutrition)
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16 pages, 2340 KiB  
Article
Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots
by Hüsna Kaya Kaçar, Ömer Furkan Kaçar and Amanda Avery
Nutrients 2025, 17(2), 206; https://doi.org/10.3390/nu17020206 - 7 Jan 2025
Cited by 1 | Viewed by 4073
Abstract
Background/Objectives: With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatbots—Gemini, Microsoft Copilot, and ChatGPT 4.0—in designing weight-loss [...] Read more.
Background/Objectives: With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatbots—Gemini, Microsoft Copilot, and ChatGPT 4.0—in designing weight-loss diet plans across varying caloric levels and genders. Methods: This comparative study assessed the diet quality of meal plans generated by the chatbots across a calorie range of 1400–1800 kcal, using identical prompts tailored to male and female profiles. The Diet Quality Index-International (DQI-I) was used to evaluate the plans across dimensions of variety, adequacy, moderation, and balance. Caloric accuracy was analysed by calculating percentage deviations from requested targets and categorising discrepancies into defined ranges. Results: All chatbots achieved high total DQI-I scores (DQI-I > 70), demonstrating satisfactory overall diet quality. However, balance sub-scores related to macronutrient and fatty acid distributions were consistently the lowest, showing a critical limitation in AI algorithms. ChatGPT 4.0 exhibited the highest precision in caloric adherence, while Gemini showed greater variability, with over 50% of its diet plans deviating from the target by more than 20%. Conclusions: AI-driven chatbots show significant promise in generating nutritionally adequate and diverse weight-loss diet plans. Nevertheless, gaps in achieving optimal macronutrient and fatty acid distributions emphasise the need for algorithmic refinement. While these tools have the potential to revolutionise personalised nutrition by offering precise and inclusive dietary solutions, they should enhance rather than replace the expertise of dietetic professionals. Full article
(This article belongs to the Special Issue A Path Towards Personalized Smart Nutrition)
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Review

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25 pages, 469 KiB  
Review
Using VR Supermarket for Nutritional Research and Education: A Scoping Review
by Cristiana Amalia Onita, Daniela-Viorelia Matei, Ilie Onu, Daniel-Andrei Iordan, Elena Chelarasu, Nicoleta Tupita, Diana Petrescu-Miron, Mihaela Radeanu, Georgiana Juravle, Calin Corciova, Robert Fuior and Veronica Mocanu
Nutrients 2025, 17(6), 999; https://doi.org/10.3390/nu17060999 - 12 Mar 2025
Viewed by 789
Abstract
According to “The World Health Organization”, obesity during childhood is directly associated with multiple complications and with an increased risk of the installation of various pathologies. Considering the increase in this pathology among children and teenagers, new instruments of prevention are needed. Virtual [...] Read more.
According to “The World Health Organization”, obesity during childhood is directly associated with multiple complications and with an increased risk of the installation of various pathologies. Considering the increase in this pathology among children and teenagers, new instruments of prevention are needed. Virtual reality is an innovative tool that offers several advantages over classical therapies, becoming important in various medical fields, starting from phobia treatment, pain relief, and body image perception to education. This technology has been successfully used to study the influence of virtual cues on behavioral responses and can be useful in nutritional education as well as understanding eating behavior. The objective of this scoping review study is to understand the impact of virtual supermarket exposure on individuals’ food choices and to explore the potential of technology on nutrition education in the general population. It seeks to explore purchasing based on product appearance and placement, food prices, nudging conditions and under-pressure decision making. A manual literature search was conducted using the databases Web of Science, SCOPUS and Google Scholar. Included articles were published between 2012 and 2024 using immersive virtual and augmented supermarket environments as a tool to understand food choices and education. The results showed that using higher immersion can be efficient in understanding food choices, rather than a lower immersive tool. The advantage of immersive virtual reality is highlighted by the sense of presence it offers, compared to other devices, providing a safe, controlled environment for users. Full article
(This article belongs to the Special Issue A Path Towards Personalized Smart Nutrition)
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