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Nutrients
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Mobile Health and Nutrition (2nd Edition)
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Mobile Health and Nutrition (2nd Edition)

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 9174

Special Issue Editors

Dr. Stavroula Mougiakakou

E-Mail Website
Guest Editor
AI in Health and Nutrition Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
Interests: artificial intelligence; AI-based dietary monitoring, assessment and management; machine learning; computer vision; medical decision-support systems
Prof. Dr. Keiji Yanai

E-Mail Website
Guest Editor
Department of Informatics, the University of Electro-Communications, Tokyo, Japan
Interests: computer vision; deep learning; food image analysis
Dr. Dario Allegra

E-Mail Website
Co-Guest Editor
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: image processing; computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in artificial intelligence, wearable technologies, big data analytics and healthcare technology in general have ushered in the era of mHealth, revolutionizing healthcare services. This transformation is not only reshaping the way we approach healthcare but also paving the way for personalized nutrition and healthier lifestyles to prevent diseases and achieve effective self-management of chronic conditions.

A significant challenge in nutrition research is acquiring high-quality, precise nutrition information in an economically viable manner. Mobile and wearable technologies, with their flexibility and efficiency, coupled with the capabilities of artificial intelligence enable the analysis of large volumes of multi-level and heterogeneous data, pattern detection, risk prediction and intervention guidance. This fusion holds immense potential for promoting healthier dietary habits and behaviors and facilitating communication between caregivers and care recipients.

Our Special Issue serves as a platform to showcase groundbreaking research at the intersection of mHealth, eHealth, artificial intelligence, wearable technologies, big data and nutrition. We invite your contributions, also welcoming intervention and observational trials designed to monitor food intake for research purposes, promoting balanced diets and effective management of health conditions. We emphasize personalization, health promotion and inclusivity.

Collectively, we want to contribute to a healthier world where personalized nutrition and better lifestyle choices are accessible to all, empowered by the transformative capabilities of artificial intelligence and cutting-edge technologies. Your expertise is vital in advancing this vision.

Dr. Stavroula Mougiakakou
Prof. Dr. Keiji Yanai
Dr. Dario Allegra
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

  • food recognition
  • food segmentation
  • portion estimation
  • mHealth
  • eHealth
  • calorie and nutrient
  • wearables
  • dietary monitoring and assessment
  • dietary management
  • dietary behavior

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Published Papers (3 papers)

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Research

13 pages, 1918 KiB  
Article
Calorie Compensation Patterns Observed in App-Based Food Diaries
by Amruta Pai and Ashutosh Sabharwal
Nutrients 2023, 15(18), 4007; https://doi.org/10.3390/nu15184007 - 16 Sep 2023
Cited by 2 | Viewed by 2370
Abstract
Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies [...] Read more.
Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual’s calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload–test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user’s compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain. Full article
(This article belongs to the Special Issue Mobile Health and Nutrition (2nd Edition))
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14 pages, 5617 KiB  
Article
The Nutritional Content of Meal Images in Free-Living Conditions—Automatic Assessment with goFOODTM
by Ioannis Papathanail, Lubnaa Abdur Rahman, Lorenzo Brigato, Natalie S. Bez, Maria F. Vasiloglou, Klazine van der Horst and Stavroula Mougiakakou
Nutrients 2023, 15(17), 3835; https://doi.org/10.3390/nu15173835 - 2 Sep 2023
Cited by 7 | Viewed by 3335
Abstract
A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct automatic nutritional assessment from meal images, providing a convenient, efficient, and [...] Read more.
A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct automatic nutritional assessment from meal images, providing a convenient, efficient, and accurate method for continuous diet evaluation. We now extend the goFOODTM automatic system to perform food segmentation, recognition, volume, as well as calorie and macro-nutrient estimation from single images that are captured by a smartphone. In order to assess our system’s performance, we conducted a feasibility study with 50 participants from Switzerland. We recorded their meals for one day and then dietitians carried out a 24 h recall. We retrospectively analysed the collected images to assess the nutritional content of the meals. By comparing our results with the dietitians’ estimations, we demonstrated that the newly introduced system has comparable energy and macronutrient estimation performance with the previous method; however, it only requires a single image instead of two. The system can be applied in a real-life scenarios, and it can be easily used to assess dietary intake. This system could help individuals gain a better understanding of their dietary consumption. Additionally, it could serve as a valuable resource for dietitians, and could contribute to nutritional research. Full article
(This article belongs to the Special Issue Mobile Health and Nutrition (2nd Edition))
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13 pages, 5852 KiB  
Article
Long-Tailed Food Classification
by Jiangpeng He, Luotao Lin, Heather A. Eicher-Miller and Fengqing Zhu
Nutrients 2023, 15(12), 2751; https://doi.org/10.3390/nu15122751 - 15 Jun 2023
Cited by 14 | Viewed by 2572
Abstract
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, foods in real-world scenarios are typically long-tail distributed, where a small number of food types are consumed more frequently than others, [...] Read more.
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, foods in real-world scenarios are typically long-tail distributed, where a small number of food types are consumed more frequently than others, which causes a severe class imbalance issue and hinders the overall performance. In addition, none of the existing long-tailed classification methods focus on food data, which can be more challenging due to the inter-class similarity and intra-class diversity between food images. In this work, two new benchmark datasets for long-tailed food classification are introduced, including Food101-LT and VFN-LT, where the number of samples in VFN-LT exhibits real-world long-tailed food distribution. Then, a novel two-phase framework is proposed to address the problem of class imbalance by (1) undersampling the head classes to remove redundant samples along with maintaining the learned information through knowledge distillation and (2) oversampling the tail classes by performing visually aware data augmentation. By comparing our method with existing state-of-the-art long-tailed classification methods, we show the effectiveness of the proposed framework, which obtains the best performance on both Food101-LT and VFN-LT datasets. The results demonstrate the potential to apply the proposed method to related real-life applications. Full article
(This article belongs to the Special Issue Mobile Health and Nutrition (2nd Edition))
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