Evidence map›Paper›PMID 38722240›Full record

ReviewNutrition reviews2025

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

Alessio Abeltino, Alessia Riente, Giada Bianchetti, Cassandra Serantoni, Marco De Spirito, Stefano Capezzone, Rosita Esposito, Giuseppe Maulucci

Abstract readReview
In one paragraph

Review in Nutrition reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Review
  6. Review
  7. Can Artificial Intelligence Chatbots Plan Therapeutic Ketogenic Diets for Children With Epilepsy?Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2026
    Article
  8. Article
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  18. Article
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  20. What Do We Know About Prevention of Frailty in Women?American journal of lifestyle medicine · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alessio AbeltinoDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Alessia RienteDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Giada BianchettiDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Cassandra SerantoniDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Marco De SpiritoDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Stefano CapezzoneGruppo Fastal Blu Sistemi, Rome, Italy.ORCID 0009-0001-1857-5043
Rosita EspositoChirale S.r.l., Digital Innovation Hub Roma, Rome, Italy.
Giuseppe MaulucciDepartment of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.ORCID 0000-0002-2154-319X

Funding

Intervento per il rafforzamento della ricerca nel Lazio-incentivi per i dottorati di innovazione per le impreseRAN InnovationRAN Innovation (2021)
6 · The paper itself

Abstract

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.

Indexed as

Precision MedicineArtificial IntelligenceDietDiet RecordsHumansMobile ApplicationsNutritional StatusNutrition Assessmentchewing and glucose monitoringdigital applicationsprecision nutritionwearable devices

Identifiers

PMID38722240
PMCPMC11986332

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.