Canadian Journal of Public Health

, Volume 99, Issue 6, pp 494–498 | Cite as

A Simple Method to Assess Fruit and Vegetable Intake among Obese and Non-obese Individuals

  • Gaston GodinEmail author
  • Ariane Bélanger-Gravel
  • Ann-Marie Paradis
  • Marie-Claude Vohl
  • Louis Pérusse



Fruit and vegetable (F&V) consumption is generally associated with the prevention of major chronic diseases. For monitoring purposes, public health researchers require short but reliable and valid questionnaires to assess F&V consumption. The aim of the present study was to validate a brief one-page self-administered fruit and vegetable questionnaire (FV-Q) for obese and non-obese populations.


The validation study was conducted from 2004 to 2006, among a sample of 350 obese and non-obese French-speaking participants. The six-item FV-Q was designed to measure F&V consumption over a seven-day period. It was validated against an interviewer-administered Food Frequency Questionnaire (FFQ) by means of correlation analysis and computing of epidemiologic indices. The analyses were performed separately for obese and non-obese individuals in order to account for potential different reporting patterns and the absence of such validation in obese populations. All the analyses were performed during 2007.


For obese and non-obese participants, the Pearson correlation coefficients between the FV-Q and FFQ were, respectively, r = 0.66 (p<0.0001) and r = 0.65 (p<0.0001) for the mean daily intake. Values for sensitivity and specificity were 88.5% and 63.6% for obese individuals and 80.0% and 65.6% for non-obese individuals, respectively. Positive predictive values were moderate in both groups, whereas negative predictive values were very good. Overall, results were very similar for obese and non-obese individuals.


This brief F&V questionnaire can be used to identify people requiring nutritional counseling. Moreover, it can be used for both obese and non-obese populations.


Fruit vegetables questionnaires validation studies 



La consommation de fruits et de légumes est généralement associée à la prévention des grandes maladies chroniques. Dans un but de surveillance, les chercheurs en santé publique ont besoin de questionnaires courts mais fiables pour déterminer cette consommation. Nous avons voulu valider le QFL, un questionnaire auto-administré d’une seule page permettant de mesurer la consommation de fruits et de légumes dans des populations obèses et non obèses.


Notre étude de validation a été conduite entre 2004 et 2006 auprès de 350 participants obèses et non obèses de langue française. Le questionnaire en six points mesure la consommation de fruits et de légumes sur une période de sept jours. Il a été validé par rapport au FFQ (un questionnaire sur la fréquence de consommation des produits alimentaires administré par entrevue) au moyen d’une analyse de corrélation et d’indices épidémiologiques. Pour pallier d’éventuelles différences dans la façon de répondre au questionnaire et permettre son usage auprès d’une population obèse, les analyses ont été effectuées séparément pour les sujets obèses et non obèses. Toutes les analyses ont été effectuées pendant l’année 2007.


En ce qui concerne la consommation quotidienne moyenne, les coefficients de Pearson entre le QFL et le FFQ étaient de r = 0,66 (p<0,0001) pour les participants obèses et de r = 0,65 (p<0,0001) pour les non-obèses. Les valeurs de sensibilité et de spécificité étaient, respectivement, de 88,5 % et 63,6 % pour les sujets obèses et de 80,0 % et 65,6 % pour les non-obèses. Les valeurs prédictives positives étaient modérées dans les deux groupes, tandis que les valeurs prédictives négatives étaient très bonnes. En général, les résultats étaient similaires chez les obèses et les non-obèses.


Ce bref questionnaire mesurant la consommation de fruits et de légumes peut être utilisé pour identifier les personnes qui nécessitent de l’assistance nutritionnelle. De plus, il peut être utilisé autant auprès de populations obèses que non obèses.

Mots clés

fruits légumes questionnaires validation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ford ES, Mokdad AH. Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults. Prev Med 2001;32:33–39.CrossRefPubMedGoogle Scholar
  2. 2.
    He K, Hu FB, Colditz GA, Manson JE, Willett WC, Liu S. Changes in intake of fruits and vegetables in relation to risk of obesity and weight gain among middle-aged women. Int J Obes 2004;28:1569–74.CrossRefGoogle Scholar
  3. 3.
    Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, et al. Fruit and vegetable intake and risk of major chronic disease. J Natl Cancer Inst 2004;21:1577–84.CrossRefGoogle Scholar
  4. 4.
    Joshipura KJ, Ascherio A, Manson JE, Stampfer MJ, Rimm EB, Speizer FE, et al. Fruit and vegetable intake in relation to risk of ischemic stroke. JAMA 1999;282(13):1233–39.CrossRefPubMedGoogle Scholar
  5. 5.
    Steinmetz KA, Potter JD. Vegetables, fruit and cancer prevention: A review. J Am Diet Assoc 1996;96(10):1027–39.CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Health Canada. The Canada’s Food Guide to Healthy Eating, 1997. Available online at: (Accessed January 10, 2007).Google Scholar
  7. 7.
    Garriguet D. Overview of Canadians’ Eating Habits Nutrition: Findings from the Canadian Community Health Survey. Ottawa, 2004.Google Scholar
  8. 8.
    Statistics Canada. Dietary practices, by age group and sex, household population aged 12 and over, 2004. Available online at: (Accessed December 18, 2007).Google Scholar
  9. 9.
    Pérez C. Fruit and vegetable consumption. Health Report. Ottawa, 2002.Google Scholar
  10. 10.
    Tjepkema M. Measured Obesity. Adult Obesity in Canada: Measured Height and Weight Nutrition: Findings from the Canadian Community Health Survey. Ottawa, 2006.Google Scholar
  11. 11.
    Thompson FE, Midthune D, Subar AF, Kahle LL, Schatzkin A, Kipnis V. Performance of a short tool to assess dietary intakes of fruits and vegetables, percentage energy from fat and fibre. Pub Health Nutr 2004;7(8):1097–106.CrossRefGoogle Scholar
  12. 12.
    Bogers RP, Van Assema P, Kester AD, Westerterp KR, Dagnelie PC. Reproducibility, validity, and responsiveness to change of a short questionnaire for measuring fruit and vegetable intake. Am J Epidemiol 2004;159(9):900–9.CrossRefPubMedGoogle Scholar
  13. 13.
    Kim DJ, Holowaty EJ. Brief, validated survey instruments for the measurement of fruit and vegetable intakes in adults: A review. Prev Med 2003;36(4):440–47.CrossRefPubMedGoogle Scholar
  14. 14.
    Traynor MM, Holowaty PH, Reid DJ, Gray-Donald K. Vegetable and fruit food frequency questionnaire serves as a proxy for quantified intake. Can J Public Health 2006;97(4):286–90.PubMedGoogle Scholar
  15. 15.
    Goris AH, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: Selective underreporting of fat intake. Am J Clin Nutr 2000;71(1):130–34.CrossRefPubMedGoogle Scholar
  16. 16.
    Poppitt SD, Swann D, Black AE, Prentice AM. Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int J Obes Relat Metab Disord 1998;22(4):303–11.CrossRefPubMedGoogle Scholar
  17. 17.
    Maurer J, Taren DL, Teixeira PJ, Thomson CA, Lohman TG, Going SB, et al. The psychosocial and behavioral characteristics related to energy misreporting. Nutr Rev 2006;64(2):53–66.CrossRefPubMedGoogle Scholar
  18. 18.
    Health Canada. Canadian Guidelines for Body Weight Classification in Adults. Ottawa, 2003;41.Google Scholar
  19. 19.
    Goulet J, Nadeau G, Lapointe A, Lamarche B, Lemieux S. Validity and reproducibility of an interviewer-administrated food frequency questionnaire for healthy French-Canadian men and women. Nutr J 2004;13:3–13.Google Scholar
  20. 20.
    Saw SM, Ng TP. The design and assessment of questionnaires in clinical research. Singapore Med J 2001;42(3):131–35.PubMedGoogle Scholar
  21. 21.
    Willett W, Lenart E. Reproducibility and validity of food-frequency questionnaires. In: Willett W (Ed.), Nutritional Epidemiology. New York, NY: Oxford University Press, 1998;101–47.CrossRefGoogle Scholar
  22. 22.
    Lee J, Koh D, Ong CN. Statistical evaluation of agreement between two methods for measuring a quantitative variable. Comput Biol Med 1989;19(1):61–70.CrossRefPubMedGoogle Scholar
  23. 23.
    The interpretation of diagnostic data. In: Sackett D, Haynes R, Guyatt G, Tugwell P (Eds.), Clinical Epidemiology. A Basic Science for Clinical Medicine. Boston/Toronto/London: Little, Brown and Company, 1991;69–152.Google Scholar
  24. 24.
    Maclure M, Willett WC. Misinterpretation and misuse of the kappa statistic. Am J Epidemiol 1987;126(2):161–69.CrossRefPubMedGoogle Scholar
  25. 25.
    Subar AF. Developing dietary assessment tools. J Am Diet Assoc 2004;104(5):769–70.CrossRefPubMedGoogle Scholar
  26. 26.
    Baranowski T, Baranowski J, Doyle C, Wang DT, Smith M, Lin LS, et al. Toward reliable estimation of servings of fruit and vegetables and fat practices from adults’ 7-day food records. J Nutr Educ 1997;29(6):321.CrossRefGoogle Scholar
  27. 27.
    Willett W. Nutritional Epidemiology, 2nd ed. New York: Oxford University Press, 1998;514.CrossRefGoogle Scholar
  28. 28.
    Sudman S, Bradburn NM. Asking Questions. A Practical Guide to Questionnaire Design, 1st ed. Jossey-Bass Inc., 1982;416.Google Scholar
  29. 29.
    Fowler FJ. Survey Research Methods. Beverly Hills, CA: Sage Publications, 1984;159.Google Scholar
  30. 30.
    Holzemer WL, Corless IB, Nokes KM, Turner JG, Brown MA, Powell-Cope GM, et al. Predictors of self-reported adherence in persons living with HIV disease. AIDS Patient Care 1999;13(3):185–97.CrossRefGoogle Scholar
  31. 31.
    Brooks CM, Richards JM, Kohler CL, Seng-Jaws S, Martin B, Winsor RA, et al. Assessing adherence to asthma medication and inhaler regimens: A psychometric analysis of adult self-report scales. Med Care 1994;32(3):298–307.CrossRefPubMedGoogle Scholar
  32. 32.
    Hays RD, DiMatteo MR. Key issues and suggestions for patient compliance assessment: Sources of information, focus of measures, and nature of responses options. J Compliance Health Care 1987;2:37–53.Google Scholar
  33. 33.
    Rand CS. «I took the medicine like you told me doctor»: Self-report of adherence with medical regimens. In: Stone AA, Turkkan JS, Bachrach CA, et al. (Eds.), The Science of Self-report. Implications for Research and Practices. Hillsdale, NJ: Lawrence Erlbaum Associates, 2000;185–213.Google Scholar
  34. 34.
    Svarstad BL, Chewning BA, Sleath BL, Claesson C. The brief medication questionnaire: A tool for screening patient adherence and barriers to adherence. Patient Educ Couns 1999;37(2):113–24.CrossRefPubMedGoogle Scholar
  35. 35.
    Chesney M. The Challenge of Adherence. Bulletin of Experimental Treatments for AIDS. San Francisco AIDS Foundation, 1999. Available online at: (Accessed February 23, 2007).Google Scholar
  36. 36.
    Rifas-Shiman SL, Willett WC, Lobb R, Kotch J, Dart C, Gillman MW. PrimeScreen, a brief dietary screening tool: Reproducibility and comparability with both a longer food frequency questionnaire and biomarkers. Public Health Nutr 2001;4(2):249–54.CrossRefPubMedGoogle Scholar

Copyright information

© The Canadian Public Health Association 2008

Authors and Affiliations

  • Gaston Godin
    • 1
    Email author
  • Ariane Bélanger-Gravel
    • 2
  • Ann-Marie Paradis
    • 3
  • Marie-Claude Vohl
    • 3
    • 4
  • Louis Pérusse
    • 5
  1. 1.Canada Research Chair on Behaviour and Health, Paul Comtois, 4106Laval UniversityQuébecCanada
  2. 2.Research Group on Behaviour and HealthLaval UniversityQuebecCanada
  3. 3.Lipid Research Centre, CHUQ-CHUL PavilionQuebecCanada
  4. 4.Department of Food Science and NutritionLaval UniversityCanada
  5. 5.Division of Kinesiology, Department of Social and Preventive MedicineLaval UniversityCanada

Personalised recommendations