There's an App for That: Development of an Application to Operationalize the Global Diet Quality Score.


Journal

The Journal of nutrition
ISSN: 1541-6100
Titre abrégé: J Nutr
Pays: United States
ID NLM: 0404243

Informations de publication

Date de publication:
23 10 2021
Historique:
received: 01 03 2021
revised: 19 04 2021
accepted: 25 05 2021
entrez: 24 10 2021
pubmed: 25 10 2021
medline: 11 11 2021
Statut: ppublish

Résumé

The global diet quality score (GDQS) is a simple, standardized metric appropriate for population-based measurement of diet quality globally. We aimed to operationalize data collection by modifying the quantity of consumption cutoffs originally developed for the GDQS food groups and to statistically evaluate the performance of the operationalized GDQS relative to the original GDQS against nutrient adequacy and noncommunicable disease (NCD)-related outcomes. The GDQS application uses a 24-h open-recall to collect a full list of all foods consumed during the previous day or night, and automatically classifies them into corresponding GDQS food group. Respondents use a set of 10 cubes in a range of predetermined sizes to determine if the quantity consumed per GDQS food group was below, or equal to or above food group-specific cutoffs established in grams. Because there is only a total of 10 cubes but as many as 54 cutoffs for the GDQS food groups, the operationalized cutoffs differ slightly from the original GDQS cutoffs. A secondary analysis using 5 cross-sectional datasets comparing the GDQS with the original and operationalized cutoffs showed that the operationalized GDQS remained strongly correlated with nutrient adequacy and was equally sensitive to anthropometric and other clinical measures of NCD risk. In a secondary analysis of a longitudinal cohort study of Mexican teachers, there were no differences between the 2 modalities with the beta coefficients per 1 SD change in the original and operationalized GDQS scores being nearly identical for weight gain (-0.37 and -0.36, respectively, P < 0.001 for linear trend for both models) and of the same clinical order of magnitude for waist circumference (-0.52 and -0.44, respectively, P < 0.001 for linear trend for both models). The operationalized GDQS cutoffs did not change the performance of the GDQS and therefore are recommended for use to collect GDQS data in the future.

Sections du résumé

BACKGROUND
The global diet quality score (GDQS) is a simple, standardized metric appropriate for population-based measurement of diet quality globally.
OBJECTIVES
We aimed to operationalize data collection by modifying the quantity of consumption cutoffs originally developed for the GDQS food groups and to statistically evaluate the performance of the operationalized GDQS relative to the original GDQS against nutrient adequacy and noncommunicable disease (NCD)-related outcomes.
METHODS
The GDQS application uses a 24-h open-recall to collect a full list of all foods consumed during the previous day or night, and automatically classifies them into corresponding GDQS food group. Respondents use a set of 10 cubes in a range of predetermined sizes to determine if the quantity consumed per GDQS food group was below, or equal to or above food group-specific cutoffs established in grams. Because there is only a total of 10 cubes but as many as 54 cutoffs for the GDQS food groups, the operationalized cutoffs differ slightly from the original GDQS cutoffs.
RESULTS
A secondary analysis using 5 cross-sectional datasets comparing the GDQS with the original and operationalized cutoffs showed that the operationalized GDQS remained strongly correlated with nutrient adequacy and was equally sensitive to anthropometric and other clinical measures of NCD risk. In a secondary analysis of a longitudinal cohort study of Mexican teachers, there were no differences between the 2 modalities with the beta coefficients per 1 SD change in the original and operationalized GDQS scores being nearly identical for weight gain (-0.37 and -0.36, respectively, P < 0.001 for linear trend for both models) and of the same clinical order of magnitude for waist circumference (-0.52 and -0.44, respectively, P < 0.001 for linear trend for both models).
CONCLUSION
The operationalized GDQS cutoffs did not change the performance of the GDQS and therefore are recommended for use to collect GDQS data in the future.

Identifiants

pubmed: 34689193
pii: S0022-3166(22)00483-7
doi: 10.1093/jn/nxab196
pmc: PMC8542098
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

176S-184S

Informations de copyright

Copyright © The Author(s) on behalf of the American Society for Nutrition 2021.

Références

Lancet. 2017 Sep 16;390(10100):1345-1422
pubmed: 28919119
N Engl J Med. 2020 Feb 13;382(7):644-654
pubmed: 32053300
Lancet Planet Health. 2020 Aug;4(8):e352-e370
pubmed: 32800153
J Acad Nutr Diet. 2021 May;121(5):854-871.e6
pubmed: 33602635

Auteurs

Mourad Moursi (M)

Intake - Center for Dietary Assessment, FHI Solutions, Washington DC, USA.

Sabri Bromage (S)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Teresa T Fung (TT)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Sheila Isanaka (S)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Mika Matsuzaki (M)

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Carolina Batis (C)

Health and Nutrition Research Center, National Institute of Public Health, Cuernavaca, Mexico.

Analí Castellanos-Gutiérrez (A)

Health and Nutrition Research Center, National Institute of Public Health, Cuernavaca, Mexico.

Erick Angulo (E)

Health and Nutrition Research Center, National Institute of Public Health, Cuernavaca, Mexico.

Nick Birk (N)

Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.

Shilpa N Bhupathiraju (SN)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Yuna He (Y)

National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.

Yanping Li (Y)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Wafaie Fawzi (W)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Armen Danielyan (A)

Digital Development, FHI 360, Washington DC, USA.

Sachit Thapa (S)

Digital Development, FHI 360, Washington DC, USA.

Liseteli Ndiyoi (L)

Independent consultant, Lusaka, Zambia.

Marieke Vossenaar (M)

Intake - Center for Dietary Assessment, FHI Solutions, Washington DC, USA.

Alexandra Bellows (A)

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Joanne E Arsenault (JE)

Intake - Center for Dietary Assessment, FHI Solutions, Washington DC, USA.

Walter C Willett (WC)

Department of Nutrition, Global Health and Population, and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.

Megan Deitchler (M)

Intake - Center for Dietary Assessment, FHI Solutions, Washington DC, USA.

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