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Development of a Diet Quality Screener for Global Use: Evaluation in a Sample of US Women

Published:February 15, 2021DOI:



      Valid and efficient tools for measuring and tracking diet quality globally are lacking.


      The objective of the study was to develop and evaluate a new tool for rapid and cost-efficient diet quality assessment.


      Two screener versions were designed using Prime Diet Quality Score (PDQS), one in a 24-hour recall (PDQS-24HR) and another in a 30-day (PDQS-30D) food frequency format. Participants completed two 24-hour diet recalls using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24) and 2 web-based diet quality questionnaires 7 to 30 days apart in April and May 2019. Both dichotomous/trichotomous and granular scoring versions were tried for each screener.


      The study included 290 nonpregnant, nonlactating US women (mean age ± standard deviation 41 ± 11 years) recruited via Amazon Mechanical Turk.

      Main outcome measures

      The main outcome measures were Spearman rank correlation coefficients and linear regression beta-coefficients between ASA24 nutrient intakes from foods and beverages and PDQS values.

      Statistical analyses performed

      The Spearman rank correlation and linear regression were used to evaluate associations of the PDQS values with ASA24 nutrient intakes from food, both crude and energy-adjusted. Correlations were de-attenuated for within-person variation in 24-hour recalls. Wolfe’s test was used to compare correlations of the 2 screening instruments (PDQS-24HR and PDQS-30D) with the ASA24. Associations between the ASA24 Healthy Eating Index 2015 and the PDQS values were also evaluated.


      Positive, statistically significant rank correlations between the PDQS-24HR values and energy-adjusted nutrients from ASA24 for fiber (r = 0.53), magnesium (r = 0.51), potassium (r = 0.48), vitamin E (r = 0.40), folate (r = 0.37), vitamin C (r = 0.36), vitamin A (r = 0.33), vitamin B6 (r = 0.31), zinc (r = 0.25), and iron (r = 0.21); and inverse correlations for saturated fatty acids (r = –0.19), carbohydrates (r = –0.22), and added sugar (r = –0.34) were observed. Correlations of nutrient intakes assessed by ASA24 with the PDQS-30D were not significantly different from those with the PDQS-24HR. Positive, statistically significant correlations between the ASA24 Healthy Eating Index 2015 and the PDQS-24HR (r = 0.61) and the PDQS-30D (r = 0.60) were also found.


      The results of an initial evaluation of the PDQS-based diet quality screeners are promising. Correlations and associations between the PDQS values and nutrient intakes were of acceptable strength and in the expected directions, and the PDQS values had moderately strong correlations with the total Healthy Eating Index 2015 score. Future work should include evaluating the screeners in other population groups, including men, and piloting it across low- and middle-income countries.


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        • Afshin A.
        • Sur P.J.
        • Fay K.A.
        • et al.
        Health effects of dietary risks in 195 countries, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017.
        Lancet. 2019; 393: 1958-1972
        • Ezzati M.
        • Riboli E.
        Behavioral and dietary risk factors for noncommunicable diseases.
        N Engl J Med. 2013; 369: 954-964
        • Food Systems and Diets: Facing the Challenges of the 21st Century
        Global Panel on Agriculture and Food Systems for Nutrition. Published 2016. Accessed November 26, 2020.
        • Huybrechts I.
        • Aglago E.K.
        • Mullee A.
        • et al.
        Global comparison of national individual food consumption surveys as a basis for health research and integration in national health surveillance programmes.
        Proc Nutr Soc. 2017 Nov; 76: 549-567
        • Arimond M.
        • Deitchler M.
        Measuring Diet Quality for Women of Reproductive Age in Low- and Middle-Income Countries: Towards New Metrics for Changing Diets.
        Intake–Center for Dietary Assessment/FHI 360. 2019;
        • About the Sustainable Development Goals
        UN’s Sustainable Development Goals. Accessed August 6, 2020.
        • Macdiarmid J.I.
        • Kyle J.
        • Horgan G.W.
        • et al.
        Sustainable diets for the future: Can we contribute to reducing greenhouse gas emissions by eating a healthy diet?.
        Am J Clin Nutr. 2012; 96: 632-639
        • Macdiarmid J.I.
        Is a healthy diet an environmentally sustainable diet?.
        Proc Nutr Soc. 2013; 72: 13-20
        • Alkerwi A.
        Diet quality concept.
        Nutrition. 2014; 30: 613-618
        • Wirt A.
        • Collins C.E.
        Diet quality—What is it and does it matter?.
        Public Health Nutr. 2009; 12: 2473-2492
        • Kim S.
        • Haines P.S.
        • Siega-Riz A.M.
        • et al.
        The Diet Quality Index-International (DQI-I) provides an effective tool for cross-national comparison of diet quality as illustrated by China and the United States.
        J Nutr. 2003; 133: 3476-3484
        • Fung T.T.
        • Isanaka S.
        • Hu F.B.
        • et al.
        International food group-based diet quality and risk of coronary heart disease in men and women.
        Am J Clin Nutr. 2018; 107: 120-129
        • Kronsteiner-Gicevic S.
        • Gaskins A.J.
        • Fung T.T.
        • et al.
        Evaluating pre-pregnancy dietary diversity vs dietary quality scores as predictors of gestational diabetes and hypertensive disorders of pregnancy.
        PLoS One. 2018; 13e0195103
        • Ojeda-Rodríguez A.
        • Zazpe I.
        • Alonso-Pedrero L.
        • Zalba G.
        • et al.
        Association between diet quality indexes and the risk of short telomeres in an elderly population of the SUN project.
        Clin Nutr. 2020; 39: 2487-2494
        • Alvarez-Alvarez I.
        • Toledo E.
        • Lecea O.
        • et al.
        Adherence to a priori dietary indexes and baseline prevalence of cardiovascular risk factors in the PREDIMED-Plus randomised trial.
        Eur J Nutr. 2020; 59: 1219-1232
        • Madzorera I.
        • Isanaka S.
        • Wang M.
        • et al.
        Maternal dietary diversity and dietary quality scores in relation to adverse birth outcomes in Tanzanian women.
        Am J Clin Nutr. 2020; 112: 695-706
        • Erdman Jr JW
        MacDonald I.A. Zeisel S.H. Present Knowledge in Nutrition. John Wiley & Sons, 2012
        • Willett W.C.
        • Stampfer M.J.
        Current evidence on healthy eating.
        Annu Rev Public Health. 2013; 34: 77-95
        • Willett W.C.
        Nutritional Epidemiology.
        3rd ed. Oxford University Press, 2012: 101-147
      1. Amazon Mechanical Turk website. Accessed August 6, 2020. https://

        • Cade J.
        • Thompson R.
        • Burley V.
        • et al.
        Development, validation and utilisation of food-frequency questionnaires—a review.
        Public Health Nutr. 2002; 5: 567-587
        • Automated Self-Administered 24-hour (ASA24®) Dietary Assessment Tool
        Version ASA24-2018. National Cancer Institute. Assessed on November 26, 2020.
        • US Department of Agriculture, Agricultural Research Service
        USDA Food and Nutrient Database for Dietary Studies 2013-14. Food Surveys Research Group. Accessed July 8, 2019.
        • Burggraf C.
        • Teuber R.
        • Brosig S.
        • et al.
        Review of a priori dietary quality indices in relation to their construction criteria.
        Nutr Rev. 2018; 76: 747-764
        • Shin J.Y.
        • Xun P.
        • Nakamura Y.
        • et al.
        Egg consumption in relation to risk of cardiovascular disease and diabetes: A systematic review and meta-analysis.
        Am J Clin Nutr. 2013; 98: 146-159
        • Djousse L.
        • Gaziano J.M.
        Egg consumption and risk of heart failure in the Physicians' Health Study.
        Circulation. 2008; 117: 512-516
        • Iannotti L.L.
        • Lutter C.K.
        • Bunn D.A.
        • et al.
        Eggs: The uncracked potential for improving maternal and young child nutrition among the world's poor.
        Nutr Rev. 2014; 72: 355-368
        • Mozaffarian D.
        Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: A comprehensive review.
        Circulation. 2016; 133: 187-225
        • Neuenschwander M.
        • Ballon A.
        • Weber K.S.
        • et al.
        Role of diet in type 2 diabetes incidence: Umbrella review of meta-analyses of prospective observational studies.
        BMJ. 2019; 366: l2368
        • Abete I.
        • Romaguera D.
        • Vieira A.R.
        • et al.
        Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: A meta-analysis of cohort studies.
        Br J Nutr. 2014; 112: 762-775
        • The Nurses’ Health Study, Nurses’ Health Study II and Nurses’ Health Study 3
        The Nurses’ Health Study. Accessed August 7, 2020.
        • WWEIA/NHANES overview
        What We Eat In America. Accessed August 7, 2020.
        • Trijsburg L.
        • Talsma E.F.
        • De Vries J.H.
        • et al.
        Diet quality indices for research in low-and middle-income countries: A systematic review.
        Nutr Rev. 2019; 77: 515-540
      2. Lime Survey. Accessed August 7, 2020.
        • Tooze J.A.
        • Kipnis V.
        • Buckman D.W.
        • et al.
        A mixed-effects model approach for estimating the distribution of usual intake of nutrients: The NCI method.
        Stat Med. 2010; 29: 2857-2868
        • Willett W.C.
        • Howe G.R.
        • Kushi L.H.
        Adjustment for total energy intake in epidemiologic studies.
        Am J Clin Nutr. 1997; 65 (discussion 1229S-1231S): 1220S-1228S
        • Krebs-Smith S.M.
        • Pannucci T.E.
        • Subar A.F.
        • et al.
        Update of the Healthy Eating Index: HEI-2015.
        J Acad Nutr Diet. 2018; 118: 1591-1602
      3. SAS [computer program]. Version 9.4. SAS Institute Inc, 2013
      4. R: A Language and Environment for Statistical Computing [computer program]. R Foundation for Statistical Computing; 2021.

        • Shan Z.
        • Rehm C.D.
        • Rogers G.
        • et al.
        Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999-2016.
        JAMA. 2019; 322: 1178-1187
        • Newman J.C.
        • Malek A.M.
        • Hunt J.K.
        • et al.
        Nutrients in the US diet: Naturally occurring or enriched/fortified food and beverage sources, plus dietary supplements: NHANES 2009–2012.
        J Nutr. 2019; 149: 1404-1412
        • 2015-2020 Dietary Guidelines for Americans
        Appendix 11: Food sources of calcium. US Department of Health and Human Services and US Department of Agriculture. Assessed August 26, 2020.
        • Fulgoni III, V.L.
        • Keast D.R.
        • Bailey R.L.
        • et al.
        Foods, fortificants, and supplements: Where do Americans get their nutrients?.
        J Nutr. 2011; 141: 1847-1854
        • Schroder H.
        • Fito M.
        • Estruch R.
        • et al.
        A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women.
        J Nutr. 2011; 141: 1140-1145
        • Verger E.O.
        • Mariotti F.
        • Holmes B.A.
        • et al.
        Evaluation of a diet quality index based on the probability of adequate nutrient intake (PANDiet) using national French and US dietary surveys.
        PLoS One. 2012; 7e42155
        • Masip G.
        • Keski-Rahkonen A.
        • Pietilainen K.H.
        • et al.
        Development of a Food-Based Diet Quality Score from a Short FFQ and Associations with Obesity Measures, Eating Styles and Nutrient Intakes in Finnish Twins.
        Nutrients. 2019; 11: 2561
        • van Lee L.
        • Feskens E.J.
        • Meijboom S.
        • et al.
        Evaluation of a screener to assess diet quality in the Netherlands.
        Br J Nutr. 2016; 115: 517-526
        • Food and Nutrition Technical Assistance Project
        Measuring the Quality of Women's Diets: Consensus on a Global Indicator for Women's Dietary Diversity.
        Food and Nutrition Technical Assistance Project, FHI 360. 2015;
        • Pan A.
        • Sun Q.
        • Bernstein A.M.
        • et al.
        Changes in red meat consumption and subsequent risk of type 2 diabetes mellitus: Three cohorts of US men and women.
        JAMA Intern Med. 2013; 173: 1328-1335
        • Pan A.
        • Sun Q.
        • Bernstein A.M.
        • et al.
        Red meat consumption and mortality: Results from 2 prospective cohort studies.
        Arch Intern Med. 2012; 172: 555-563
        • Farvid M.S.
        • Cho E.
        • Chen W.Y.
        • et al.
        Adolescent meat intake and breast cancer risk.
        Int J Cancer. 2015; 136: 1909-1920
        • Godfray H.C.J.
        • Aveyard P.
        • Garnett T.
        • et al.
        Meat consumption, health, and the environment.
        Science. 2018; 361: eaam5324
        • Food balance sheets
        UN Food and Agriculture Organization. Assessed November 26, 2020.
        • Beto J.A.
        • Metallinos-Katsaras E.
        • Leung C.
        Crowdsourcing: A critical reflection on this new frontier of participant recruiting in nutrition and dietetics research.
        J Acad Nutr Diet. 2020; 120: 193-196
        • Chiuve S.E.
        • Fung T.T.
        • Rimm E.B.
        • et al.
        Alternative dietary indices both strongly predict risk of chronic disease.
        J Nutr. 2012; 142: 1009-1018
        • Warensjo Lemming E.
        • Byberg L.
        • Wolk A.
        • et al.
        A comparison between two healthy diet scores, the modified Mediterranean diet score and the Healthy Nordic Food Index, in relation to all-cause and cause-specific mortality.
        Br J Nutr. 2018; 119: 836-846
        • Olsen A.
        • Egeberg R.
        • Halkjaer J.
        • et al.
        Healthy aspects of the Nordic diet are related to lower total mortality.
        J Nutr. 2011; 141: 639-644
        • Food and Nutrition Technical Assistance Project
        Developing and Validating Simple Indicators of Dietary Quality and Energy Intake of Infants and Young Children in Developing Countries: Summary of Findings From Analysis of 10 Data Sets.
        Food and Nutrition Technical Assistance Project, FHI 360. 2006;
        • Miller V.
        • Webb P.
        • Micha R.
        • et al.
        Defining diet quality: A synthesis of dietary quality metrics and their validity for the double burden of malnutrition.
        Lancet Planet Health. 2020; 4: e352-e370
      5. Ross J, Zaldivar A, Irani L, et al. Who are the Turkers? Worker demographics in Amazon Mechanical Turk. Social Code Rep. 2009-01.


      S. Kronsteiner-Gicevic is an IMMANA (Innovative Methods and Metrics for Agriculture and Nutrition Actions) postdoctoral fellow, The London Centre for Integrative Research on Agriculture and Health, London, UK, and Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.


      Y. Mou is a researcher, Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.


      S. Bromage is postdoctoral fellow, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.


      T. T. Fung is a professor, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, and a professor, Department of Nutrition, Simmons University, Boston, MA.


      W. Willett is a professor, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, and professor, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.