Essential dataset features in a successful obesity registry: a systematic review.

dataset minimum dataset obesity obesity registry overweight registry

Journal

International health
ISSN: 1876-3405
Titre abrégé: Int Health
Pays: England
ID NLM: 101517095

Informations de publication

Date de publication:
16 Feb 2024
Historique:
received: 23 09 2023
revised: 17 01 2024
accepted: 30 01 2024
medline: 17 2 2024
pubmed: 17 2 2024
entrez: 17 2 2024
Statut: aheadofprint

Résumé

The prevalence of obesity and the diversity of available treatments makes the development of a national obesity registry desirable. To do this, it is essential to design a minimal dataset to meet the needs of a registry. This review aims to identify the essential elements of a successful obesity registry. We conducted a systematic literature review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis recommendations. Google Scholar, Scopus and PubMed databases and Google sites were searched to identify articles containing obesity or overweight registries or datasets of obesity. We included English articles up to January 2023. A total of 82 articles were identified. Data collection of all registries was carried out via a web-based system. According to the included datasets, the important features were as follows: demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history, clinical information, medication history, family medical history, prenatal history, quality-of-life assessment and eating disorders. In this study, the essential features in the obesity registry dataset were demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history and clinical analysis items.

Sections du résumé

BACKGROUND BACKGROUND
The prevalence of obesity and the diversity of available treatments makes the development of a national obesity registry desirable. To do this, it is essential to design a minimal dataset to meet the needs of a registry. This review aims to identify the essential elements of a successful obesity registry.
METHODS METHODS
We conducted a systematic literature review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis recommendations. Google Scholar, Scopus and PubMed databases and Google sites were searched to identify articles containing obesity or overweight registries or datasets of obesity. We included English articles up to January 2023.
RESULTS RESULTS
A total of 82 articles were identified. Data collection of all registries was carried out via a web-based system. According to the included datasets, the important features were as follows: demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history, clinical information, medication history, family medical history, prenatal history, quality-of-life assessment and eating disorders.
CONCLUSIONS CONCLUSIONS
In this study, the essential features in the obesity registry dataset were demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history and clinical analysis items.

Identifiants

pubmed: 38366720
pii: 7609166
doi: 10.1093/inthealth/ihae017
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Mashhad University of Medical Sciences

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Auteurs

Mina Nosrati (M)

International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Najmeh Seifi (N)

International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Nafiseh Hosseini (N)

International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Gordon A Ferns (GA)

Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK.

Khalil Kimiafar (K)

Department of Medical Records and Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.

Majid Ghayour-Mobarhan (M)

International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Classifications MeSH