Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities
Daniele Pala
1, 2
,
Giorgia Petrini
2
,
Pietro Bosoni
2
,
Cristiana Larizza
2
,
Silvana Quaglini
2
,
Giordano Lanzola
2
Publication type: Journal Article
Publication date: 2024-04-01
scimago Q1
wos Q1
SJR: 1.189
CiteScore: 7.9
Impact factor: 4.1
ISSN: 13865056, 18728243
PubMed ID:
38295584
Health Informatics
Abstract
A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments. We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps. Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found. The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.
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Metrics
6
Total citations:
6
Citations from 2024:
4
(100%)
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GOST
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Pala D. et al. Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities // International Journal of Medical Informatics. 2024. Vol. 184. p. 105351.
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Pala D., Petrini G., Bosoni P., Larizza C., Quaglini S., Lanzola G. Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities // International Journal of Medical Informatics. 2024. Vol. 184. p. 105351.
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RIS
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TY - JOUR
DO - 10.1016/j.ijmedinf.2024.105351
UR - https://linkinghub.elsevier.com/retrieve/pii/S1386505624000145
TI - Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities
T2 - International Journal of Medical Informatics
AU - Pala, Daniele
AU - Petrini, Giorgia
AU - Bosoni, Pietro
AU - Larizza, Cristiana
AU - Quaglini, Silvana
AU - Lanzola, Giordano
PY - 2024
DA - 2024/04/01
PB - Elsevier
SP - 105351
VL - 184
PMID - 38295584
SN - 1386-5056
SN - 1872-8243
ER -
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BibTex (up to 50 authors)
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@article{2024_Pala,
author = {Daniele Pala and Giorgia Petrini and Pietro Bosoni and Cristiana Larizza and Silvana Quaglini and Giordano Lanzola},
title = {Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities},
journal = {International Journal of Medical Informatics},
year = {2024},
volume = {184},
publisher = {Elsevier},
month = {apr},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1386505624000145},
pages = {105351},
doi = {10.1016/j.ijmedinf.2024.105351}
}