Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art

被引:8
作者
Abeltino, Alessio [1 ,2 ]
Riente, Alessia [1 ,2 ]
Bianchetti, Giada [1 ,2 ]
Serantoni, Cassandra [1 ,2 ]
De Spirito, Marco [1 ]
Capezzone, Stefano [3 ]
Esposito, Rosita [4 ]
Maulucci, Giuseppe [1 ,2 ,5 ]
机构
[1] Univ Cattolica Sacro Cuore, Dept Neurosci, Metab Intelligence Lab, Rome, Italy
[2] Fdn Policlin Univ A Gemelli IRCCS, Complex operat unit Phys life Sci, Rome, Italy
[3] Grp Fastal Blu Sistemi, Rome, Italy
[4] Chirale Srl, Digital Innovat Hub Roma, Rome, Italy
[5] Univ Cattolica Sacro Cuore, Dept Neurosci, Largo Francesco Vito 1, I-00168 Rome, Italy
关键词
digital applications; precision nutrition; wearable devices; chewing and glucose monitoring; WEARABLE DEVICE; GREY LITERATURE; GLUCOSE; MANAGEMENT; EVERSENSE; SOFTWARE; DENSITY; QUALITY; TRACKER; ENERGY;
D O I
10.1093/nutrit/nuae035
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.
引用
收藏
页码:e574 / e601
页数:28
相关论文
共 119 条
[61]   Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement [J].
Lee, Changhun ;
Kim, Soohyeok ;
Lim, Chiehyeon ;
Kim, Jayun ;
Kim, Yeji ;
Jung, Minyoung .
KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2021, :3150-3160
[62]   My Fitness Pal calorie tracker usage in the eating disorders [J].
Levinson, Cheri A. ;
Fewell, Laura ;
Brosof, Leigh C. .
EATING BEHAVIORS, 2017, 27 :14-16
[63]   The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition [J].
Limketkai, Berkeley N. ;
Mauldin, Kasuen ;
Manitius, Natalie ;
Jalilian, Laleh ;
Salonen, Bradley R. .
CURRENT SURGERY REPORTS, 2021, 9 (07)
[64]   Interference Assessment of Various Endogenous and Exogenous Substances on the Performance of the Eversense Long-Term Implantable Continuous Glucose Monitoring System [J].
Lorenz, Carrie ;
Sandoval, Wendolyn ;
Mortellaro, Mark .
DIABETES TECHNOLOGY & THERAPEUTICS, 2018, 20 (05) :344-352
[65]   Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study [J].
Lubitz, Steven A. ;
Faranesh, Anthony Z. ;
Atlas, Steven J. ;
McManus, David D. ;
Singer, Daniel E. ;
Pantelopoulos, Alexandros ;
Pagoto, Sherry ;
Foulkes, Andrea S. .
AMERICAN HEART JOURNAL, 2021, 238 :16-26
[66]   Blockchain technology for the management of food sciences researches [J].
Machado, Thelma B. ;
Ricciardi, Leonardo ;
Oliveira, M. Beatriz P. P. .
TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2020, 102 :261-270
[67]   Developing a method to create a digital food atlas for use in Nutritics professional nutrition analysis software [J].
Mahmood, R. ;
Courtney, A. ;
McNulty, B. A. ;
Kelly, D. O' ;
Douglas, F. E. .
PROCEEDINGS OF THE NUTRITION SOCIETY, 2018, 77 (OCE3) :E92-E92
[68]   Combined use of a wristband and a smartphone to reduce body weight in obese children: randomized controlled trial [J].
Mameli, C. ;
Brunetti, D. ;
Colombo, V. ;
Bedogni, G. ;
Schneider, L. ;
Penagini, F. ;
Borsani, B. ;
Zuccotti, G. V. .
PEDIATRIC OBESITY, 2018, 13 (02) :81-87
[69]  
Marling CR., 1996, INTEGRATING CASE BAS
[70]  
Mastrototaro JJ., 2000, MINIMED CONTINUOUS G, V2