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Nutrients
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Digital Transformations in Nutrition
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Digital Transformations in Nutrition

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 9365

Special Issue Editor

Dr. Sasan Adibi

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Geelong, Australia
Interests: health informatics; digital health; mHealth; eHealth; nutrition; health diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of nutrition is continuously evolving, with growing awareness of the significant impact diet has on health and well-being. Simultaneously, the digital health landscape is expanding, offering new avenues for personalized health management. This Special Issue aims to explore the integration of digital health technologies into the realm of nutrition and dietary management. We invite researchers and experts in nutrition, health informatics, and digital health to contribute their insights, studies, and innovations in this emerging field.

Topics of interest:

-Nutrition tracking apps: evaluating the effectiveness and accuracy of mobile applications and wearable devices for tracking dietary intake, calorie counting, and nutrient monitoring.

-Personalized dietary recommendations: investigating the use of artificial intelligence and machine learning algorithms to provide tailored dietary advice based on an individual's health status, preferences, and goals.

-Behavioral change interventions: exploring digital interventions, such as gamification and social support platforms, in promoting healthy eating habits and behavior change.

-Nutrigenomics and genetic profiling: examining the role of genetic data in customizing dietary recommendations and predicting individual responses to different diets.

-Telehealth and telemedicine: assessing the effectiveness of telehealth platforms in delivering remote nutritional counseling and support, especially in underserved populations.

-Big Data and analytics: discussing the potential of Big Data analytics in uncovering dietary patterns, trends, and nutritional insights from vast datasets.

-Blockchain in food traceability: investigating the use of blockchain technology to enhance transparency in the food supply chain, allowing consumers to make informed choices about the nutritional value and origin of their food.

-Ethical and privacy considerations: addressing the ethical and privacy concerns related to the collection and use of personal dietary and health data in digital health interventions.

We welcome original research articles, reviews, case studies, and opinion pieces that contribute to the understanding of how digital health is transforming the field of nutrition. Submissions should adhere to the journal's guidelines for manuscript preparation and formatting.

Dr. Sasan Adibi
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

  • nutrition
  • digital
  • health
  • applications
  • tracking
  • personalized
  • telehealth

Published Papers (9 papers)

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Research

Jump to: Review

15 pages, 2008 KiB  
Article
Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program
by Bingjie Zhou, Susan B. Roberts, Sai Krupa Das and Elena N. Naumova
Nutrients 2024, 16(8), 1224; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16081224 - 19 Apr 2024
Viewed by 339
Abstract
The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early [...] Read more.
The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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15 pages, 429 KiB  
Article
Patient-Led, Technology-Assisted Malnutrition Risk Screening in Hospital: A Feasibility Study
by Shelley Roberts, Andrea P. Marshall, Leisa Bromiley, Zane Hopper, Joshua Byrnes, Lauren Ball, Peter F. Collins and Jaimon Kelly
Nutrients 2024, 16(8), 1139; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16081139 - 12 Apr 2024
Viewed by 545
Abstract
Malnutrition risk screening is crucial to identify at-risk patients in hospitals; however, screening rates can be suboptimal. This study evaluated the feasibility, acceptability, and potential cost-effectiveness of patient-led, technology-assisted malnutrition risk screening. A prospective multi-methods study was conducted in a 750-bed public hospital [...] Read more.
Malnutrition risk screening is crucial to identify at-risk patients in hospitals; however, screening rates can be suboptimal. This study evaluated the feasibility, acceptability, and potential cost-effectiveness of patient-led, technology-assisted malnutrition risk screening. A prospective multi-methods study was conducted in a 750-bed public hospital in Australia. Patients were recruited from seven wards and asked to complete an electronic version of the Malnutrition Screening Tool (e-MST) on bedside computer screens. Data were collected on feasibility, acceptability, and cost. Feasibility data were compared to pre-determined criteria on recruitment (≥50% recruitment rate) and e-MST completion (≥75% completion rate). Quantitative acceptability (survey) data were analyzed descriptively. Patient interview data were analyzed thematically. The economic evaluation was from the perspective of the health service using a decision tree analytic model. Both feasibility criteria were met; the recruitment rate was 78% and all 121 participants (52% male, median age 59 [IQR 48-69] years) completed the e-MST. Patient acceptability was high. Patient-led e-MST was modeled to save $3.23 AUD per patient and yield 6.5 more true malnutrition cases (per 121 patients) with an incremental cost saving per additional malnutrition case of 0.50 AUD. Patient-led, technology-assisted malnutrition risk screening was found to be feasible, acceptable to patients, and cost-effective (higher malnutrition yield and less costly) compared to current practice at this hospital. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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7 pages, 2102 KiB  
Communication
Generative Artificial Intelligence as a Tool for Teaching Communication in Nutrition and Dietetics Education—A Novel Education Innovation
by Lisa A. Barker, Joel D. Moore and Helmy A. Cook
Nutrients 2024, 16(7), 914; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16070914 - 22 Mar 2024
Viewed by 746
Abstract
Although effective communication is fundamental to nutrition and dietetics practice, providing novice practitioners with efficacious training remains a challenge. Traditionally, human simulated patients have been utilised in health professions training, however their use and development can be cost and time prohibitive. Presented here [...] Read more.
Although effective communication is fundamental to nutrition and dietetics practice, providing novice practitioners with efficacious training remains a challenge. Traditionally, human simulated patients have been utilised in health professions training, however their use and development can be cost and time prohibitive. Presented here is a platform the authors have created that allows students to interact with virtual simulated patients to practise and develop their communication skills. Leveraging the structured incorporation of large language models, it is designed by pedagogical content experts and comprises individual cases based on curricula and student needs. It is targeted towards the practice of rapport building, asking of difficult questions, paraphrasing and mistake making, all of which are essential to learning. Students appreciate the individualised and immediate feedback based on validated communication tools that encourage self-reflection and improvement. Early trials have shown students are enthusiastic about this platform, however further investigations are required to determine its impact as an experiential communication skills tool. This platform harnesses the power of artificial intelligence to bridge the gap between theory and practice in communication skills training, requiring significantly reduced costs and resources than traditional simulated patient encounters. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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18 pages, 9023 KiB  
Article
Empowering Young Women: A Qualitative Co-Design Study of a Social Media Health Promotion Programme
by Jessica A. Malloy, Joya A. Kemper, Stephanie R. Partridge and Rajshri Roy
Nutrients 2024, 16(6), 780; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16060780 - 09 Mar 2024
Viewed by 1019
Abstract
Social media platforms may be promising intervention tools to address the nutrition literacy and associated health behaviours of young women. We aimed to co-design a lifestyle intervention on social media targeting eating, physical activity, and social wellbeing that is evidence-based, acceptable, and engaging [...] Read more.
Social media platforms may be promising intervention tools to address the nutrition literacy and associated health behaviours of young women. We aimed to co-design a lifestyle intervention on social media targeting eating, physical activity, and social wellbeing that is evidence-based, acceptable, and engaging for young women aged 18–24 years. The study used a participatory design framework and previously published iterative mixed methods approach to intervention development. Matrices for workshop objectives were constructed using expert discussions and insights were sought from young women in participatory workshops. A 10-step qualitative data analysis process resulted in relevant themes, which guided intervention development. The resulting intervention, the Daily Health Coach, uses multiple features of Instagram to disseminate health information. Co-created nutrition content considers themes such as holism, food relationships, and food neutrality and acknowledges commonly experienced barriers associated with social media use such as nutrition confusion, body image concerns, and harmful comparison. This study may guide other researchers or health professionals seeking to engage young women in the co-design of women’s health promotion or intervention content on social media. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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15 pages, 1354 KiB  
Article
Exploring Eating Habits, Healthy Food Awareness, and Inclination toward Functional Foods of Italian Elderly People through Computer-Assisted Telephone Interviews (CATIs)
by Giulia Maria Daniele, Chiara Medoro, Nico Lippi, Marta Cianciabella, Massimiliano Magli, Stefano Predieri, Giuseppe Versari, Roberto Volpe and Edoardo Gatti
Nutrients 2024, 16(6), 762; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16060762 - 07 Mar 2024
Viewed by 732
Abstract
The continuous increase in life expectancy leads to progressive population ageing, especially in most developed countries. A healthy diet and better consumption of tailored functional foods may represent one of the strategies to postpone or slow down age-related decrements, thus increasing healthy ageing [...] Read more.
The continuous increase in life expectancy leads to progressive population ageing, especially in most developed countries. A healthy diet and better consumption of tailored functional foods may represent one of the strategies to postpone or slow down age-related decrements, thus increasing healthy ageing and reducing healthcare costs. This research aimed to explore elderly people’s (>65 years old) eating habits and assess their awareness of food-health correlation. In total, 511 Italian seniors answered a CATI (computer-assisted telephone interviewing) questionnaire through a deep, telephone interview to collect information about dietary habits, healthy food awareness, and inclination for functional foods. The elderly were divided into four groups according to gender and age: Early Elderly Female (n = 130), Early Elderly Men (n = 109), Late Elderly Female (n = 157), and Late Elderly Men (n = 115). The groups provided a positive self-assessment of health status and individual diet healthiness, which were both considered over “good enough” (5 on 10-point scale) and showed food consumption habits in line with the Mediterranean Diet (MD) principles. The daily diet was based on fruits, vegetables, bread, and pasta, with extra virgin olive oil as the main fat source, all over “often” consumed (4 on 5-point scale). Old people also showed awareness of different food’s healthy properties. Specifically, females were more aware of food’s impact on health, considered close to “extremely healthy” (9 on 10-point scale), and strictly followed a MD. Participants also expressed optimistic expectations about functional food efficiency, evaluated as close to “extremely desirable” (8 or 9 on 10-point scale), against age-related problems, highlighting the most important as diabetes, overweight, intestine problems, and low mood. The interviewed elderly were also involved in virtual functional food co-creation, indicating through a basic matrix which, among the most familiar foods, could be the ideal functional food, focusing on fruitsand vegetables. A pleasant odor/flavor, a liquid texture, and a warm serving temperature rather than cold characterized the virtual functional food created. Other positive attributes were liquid and thickness, while acidity and bitterness were among the least desired traits. These findings show how elderly people, despite predictable age-related sensory and cognitive loss, when properly involved and guided, can help envision foods that fit their needs and desires. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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21 pages, 2081 KiB  
Article
An Explainable Prediction for Dietary-Related Diseases via Language Models
by Insu Choi, Jihye Kim and Woo Chang Kim
Nutrients 2024, 16(5), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16050686 - 28 Feb 2024
Viewed by 726
Abstract
Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) [...] Read more.
Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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16 pages, 861 KiB  
Article
OptimalMe Program: A Mixed Method Investigation into the Engagement and Acceptability of a Preconception Digital Health Lifestyle Intervention with Individual Coaching for Women’s Health and Behaviour Change
by Bonnie R. Brammall, Rhonda M. Garad, Helena J. Teede, Susanne E. Baker and Cheryce L. Harrison
Nutrients 2024, 16(5), 572; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16050572 - 20 Feb 2024
Viewed by 882
Abstract
Preconception interventions, specifically addressing general health, lifestyle behaviours and weight management, are limited despite their importance in optimising women’s health. The objective of this study is to evaluate the engagement and acceptability of OptimalMe, a digital preconception intervention. Participants, (n = 298) [...] Read more.
Preconception interventions, specifically addressing general health, lifestyle behaviours and weight management, are limited despite their importance in optimising women’s health. The objective of this study is to evaluate the engagement and acceptability of OptimalMe, a digital preconception intervention. Participants, (n = 298) Australian women aged 18–44 with private health insurance planning to conceive within 12 months, received a standardised intervention, including access to a digital healthy lifestyle platform (educational materials, behaviour change activities, and self-monitoring resources), ongoing text messaging, and remotely delivered health coaching (two appointments) with randomised delivery methods (telephone/videoconference). Engagement and acceptability were assessed through mixed method analyses. The results show that 76.2% attended both coaching sessions, with similar participation rates for telehealth (75.2%) and videoconferencing (77.2%) (p = 0.469). All participants logged into the digital platform, with 90.6% accessing educational materials and 91.3% using behaviour change tools. Digital platform engagement declined over time, suggesting potential benefits from additional health coaching support for ongoing participation. The post-intervention evaluation (n = 217 participants) demonstrated that approximately 90% found the digital module engaging, meeting information needs, would recommend the program, and were satisfied with the support. OptimalMe demonstrated positive acceptability and engagement; however, further research is warranted to explore strategies for sustaining engagement with the digital interventions. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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17 pages, 625 KiB  
Article
Delighting Palates with AI: Reinforcement Learning’s Triumph in Crafting Personalized Meal Plans with High User Acceptance
by Maryam Amiri, Fatemeh Sarani Rad and Juan Li
Nutrients 2024, 16(3), 346; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16030346 - 24 Jan 2024
Viewed by 1222
Abstract
Eating, central to human existence, is influenced by a myriad of factors, including nutrition, health, personal taste, cultural background, and flavor preferences. The challenge of devising personalized meal plans that effectively encompass these dimensions is formidable. A crucial shortfall in many existing meal-planning [...] Read more.
Eating, central to human existence, is influenced by a myriad of factors, including nutrition, health, personal taste, cultural background, and flavor preferences. The challenge of devising personalized meal plans that effectively encompass these dimensions is formidable. A crucial shortfall in many existing meal-planning systems is poor user adherence, often stemming from a disconnect between the plan and the user’s lifestyle, preferences, or unseen eating patterns. Our study introduces a pioneering algorithm, CFRL, which melds reinforcement learning (RL) with collaborative filtering (CF) in a unique synergy. This algorithm not only addresses nutritional and health considerations but also dynamically adapts to and uncovers latent user eating habits, thereby significantly enhancing user acceptance and adherence. CFRL utilizes Markov decision processes (MDPs) for interactive meal recommendations and incorporates a CF-based MDP framework to align with broader user preferences, translated into a shared latent vector space. Central to CFRL is its innovative reward-shaping mechanism, rooted in multi-criteria decision-making that includes user ratings, preferences, and nutritional data. This results in versatile, user-specific meal plans. Our comparative analysis with four baseline methods showcases CFRL’s superior performance in key metrics like user satisfaction and nutritional adequacy. This research underscores the effectiveness of combining RL and CF in personalized meal planning, marking a substantial advancement over traditional approaches. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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Review

Jump to: Research

24 pages, 2066 KiB  
Review
Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review
by Tagne Poupi Theodore Armand, Kintoh Allen Nfor, Jung-In Kim and Hee-Cheol Kim
Nutrients 2024, 16(7), 1073; https://0-doi-org.brum.beds.ac.uk/10.3390/nu16071073 - 06 Apr 2024
Viewed by 1218
Abstract
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to [...] Read more.
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance. Full article
(This article belongs to the Special Issue Digital Transformations in Nutrition)
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