Ten-Year Trends in Children’s Fruit and Vegetable Knowledge, Intent and Behavior
Sutliffe JT1*, Carnot MJ2, Palmer. SE3, Elliott E4
1 Associate Clinical Professor, Health Sciences, Northern Arizona University, USA.
2 Associate Professor, Counseling, Psychology & Social Work, Chadron State College, USA.
3 Associate Dean, College of Health & Human Services, Northern Arizona University, USA.
4 Ware Distinguished Professor, College of Physical Activity and Sport Sciences, West Virginia University, USA.
Jay T. Sutliffe PhD, RD,
Associate Clinical Professor, Health Sciences,
Northern Arizona University,
PO Box 15095, Flagstaff, AZ 86011-5095, USA.
Received: June 26, 2015; Accepted: July 12, 2015; Published: July 14, 2015
Citation: Sutliffe JT, Carnot MJ, Palmer. SE, Elliott E (2015) Ten-Year Trends in Children’s Fruit and Vegetable Knowledge, Intent and Behavior. Int J Food Sci Nutr Diet. S3:001, 1-7. doi: dx.doi.org/10.19070/2326-3350-SI03001
Copyright: Sutliffe JT© 2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
This is a report of ten-year trends in US fifth-grade student knowledge of fruit and vegetable (FV) recommendations, behavioral intent, consumption, and relationships between FV knowledge, behavioral intent and behavior. The study was conducted from 2001-2011 through the Healthy Hearts for Kids (HH4K) online instructional program. A total of 17,559 students from 1,048 schools in 49 US states participated. ANOVA and Pearson product-moment correlations were calculated for all variables. Significant changes over the 10-years were found for knowledge of FV recommendations, fruit consumption, intention to consume FV immediately and in one year, and to choose fruits rather than candy tomorrow and in one year. Effect size was small for all variables. No significant changes in vegetable consumption were found. Generally, data revealed significant correlations between behavior and intent items, but not knowledge. Despite ongoing efforts to inform children about FV consumption, there has been minimal change in fifth-grade children’s overall FV knowledge, behavior and intent, revealing a need for more than just knowledge based education and interventions. It is critical that effectiveness, sustainability and scalability be considered with future interventions.
3.Materials and Methods
3.1 Study Population
4.1 Data Analysis
4.2 Change in Health knowledge
4.3 Change in Health Behaviors: Fruit and Vegetable Consumption
4.4 Changes in Behavioral Intent
4.5 Relationship between FV Knowledge, Behavior and Intent
4.6 Health Knowledge
4.7 Health Behaviors (Self-Reported Consumption)
5.1 Behavioral Intent
5.2 Relationships between Knowledge, Intent and Behavior
5.3 Implications for Research and Practice
6.Acknowledgement and Declarations
Children; Fruit and Vegetable Knowledge; Consumption; Behavioral Intent.
The need to improve the overall dietary habits of children has received extensive attention in recent years, due to a significant increase in the incidence of childhood obesity over the past three decades. The percentage of children aged 6–11 years in the United States who were obese increased from 7% in 1980 to nearly 20% in 2008 according to the National Health and Nutrition Examination Survey (NHANES) . Children and adolescents who are obese are much more likely to be obese as adults and are at an increased risk for chronic diseases [2, 3].
A primary indicator of dietary quality is fruit and vegetable (FV) consumption. Researchers have found that children need to increase the consumption of FV, whole grains, and legumes and decrease their intake of saturated fat, sodium, and extra calories [4, 5]. Current consumption patterns are not meeting the Dietary Guidelines for Americans’recommendations, especially in regards to FV intake [6, 7].
Several initiatives have been developed to increase FV consumption to meet the recommendations of the Dietary Guidelines [8-10]. Knowledge about recommended FV consumption having a positive correlation with actual FV consumption has been mixed [11-14]. Per capita intakes of vegetables decreased from 1989 to 2010 for 2-18 year olds. Fruit consumption decreased from 1989 to 1991 and through the 2003 to 2006 periods and subsequently increased in 2009-2010 .
Lien et al  found that behavioral intent to consume FV had no significant effects on attitudes and subjective norms on behavior. Anderson et al  found behavioral intention was correlated with increased fruit consumption but not vegetable consumptionfor primary school children. Several researchers have found that when intent was measured as willingness and motivation by adults to consume FV, it was the strongest predictor of FV intake [18, 19].
Web-based health literacy programs are one of many types of interventions designed to promote a healthy life style among youths [20-22]. Many resources have been directed to increasing FV consumption in the past ten-years, but the impact on dietary health literacy is unclear.
Healthy Hearts for Kids (HH4K) was developed in 2001 as one of the first web-based instructional modules for schools to increase health knowledge and improve health attitudes and behaviors. HH4K has been used by approximately 17,500 fifth-grade children from 49 US states between 2001 and 2011. HH4K includes a survey of FV knowledge, behavior and behavioral intent prior to students completing online lessons. This data can be used to investigate trends in health literacy. In this study, we report 10- year trends in fifth-grade student knowledge of FV recommendations, FV behavioral intentand FV consumption. We also report the relationships between FV knowledge, behavioral intent and actual behavior.
Materials and Methods
This study presents an analysis of secondary data collected through HH4K. Teachers registered to use HH4K online and were prompted to enter school and student information. From 2001-2010, student information entered included gender, ethnicity and age except in 2001, 2006, 2008 and 2009, Ethnicity and age were not reported in 2010. A total of 17,559 children, primarily 5th graders aged 10-11, from 1048 schools and 49 states were enrolled to use HH4K. The survey was administered to enrolled students when they logged on to HH4K the first time. Data was collected with each new cohort of students on a yearly basis from 2001 – 2011. Data from 2006 was lost due to a corrupt data file. Gender, ethnicity and age of student participants as reported by teachers are displayed in Table 1.
As use of HH4K expanded to more US states and more schools registered to use the instructional module with their students, the schools continued to be predominately rural, with over half classified as rural, an average of about 30% designated urban, and less than 10% were inner-city schools. Table 2 provides a snapshot of school demographics over three years during data collection.
The Institutional Review Board (IRB) at Northern Arizona University approved this analysis of secondary data.
When students first logged on to HH4K, they were asked to complete a 23-item pre-survey that included questions addressing behavior, behavioral intent and knowledge for nutrition, tobacco and physical activity. The survey was derived from the Youth Risk Behavior Surveillance System (YRBSS) [23, 24] and validated instruments used in the Sport, Play and Active Recreation for Kids (SPARK) .
Two items required subjects to indicate how often in the past they ate FV (e.g. During the past 7 days, how many times did you eat vegetables). Answer choices included “I did not eat vegetables during the past 7 days,” 1-3 times, 4-6 times, and 1, 2, 3 or 4 or more times per day”. These responses were assigned numerical values from 1–7.
Behavioral intent items required subjects to indicate how likely they were to eat FV tomorrow, and in one-year. Additionally, subjects were asked how likely they would choose fruit rather than candy or cookies tomorrow and one year from now. Answer choices were based on a five-point Likert-type scale (I definitely will not, probably will not, about a 50/50 chance, probably will and definitely will). Higher scores indicate a greater intention for that particular behavior.
The FV knowledge test item was developed from HH4K instructional objectives and was part of a knowledge pre-test administered before each respective health literacy . A total of 29 knowledge items were included, but this analysis is focused on an item that required subjects to identify the number of FV servings that were recommended each day. This item was scored as either incorrect (0) or correct (1).
One-way ANOVAs with year as the factor (9 levels) was used to examine changes across time in FV consumption knowledge, behavior intent and behavior. Bonferroni post hoc procedures were used to identify significant differences. The Pearson product-moment correlation was used to examine the relationship between knowledge about healthy consumption, intended consumption and actual behavior. SYSTAT software version 13.1 was used for all analysis (Systat Software, Inc. San Jose, CA USA).
A one way ANOVA to look at differences in knowledge about FV consumption across years yielded significant differences across time (F (8, 7805) = 9.118, p < 0.0001, MSE = .225, Eta squared = .009). Bonferroni post hoc tests identified that 2001 – 2002 and 2002 – 2003 were significantly different from all other years except for 2005-2006. 2005-2006 was different than 2008 - 2009 and 2010 – 2011. Mean correct response rate of knowledge items and also mean fruit consumption and vegetable consumption are displayed in Table 2.
A one-way ANOVA to look at differences in weekly fruit consumption over the past 10 years yielded significant differences across school years (F (8, 17548) = 7.579, p <0.001, MSE = 3.608, Eta squared = .003). Bonferroni post hoc tests indicated that 2009 - 2010 and 2010 - 2011 were different from 2001 - 2002, 2003 - 2004, and 2004 - 2005. Results in 2003 - 2004 and 2007 - 2008 were also different (see Table 1). A one way ANOVA to look at differences in weekly vegetable consumption over the past 10 years yielded no significant differences across school years (F (8, 17548) = 1.295, p =0.241, MSE = 3.356, Eta squared = .001) (Table 3).
Table 3. Change Across Time in Knowledge about Fruit and Vegetable Consumption and Fruit and Vegetable Consumption Reports.
Table 3 presents the mean, median and standard deviations for the behavioral intent variables. A one-way ANOVA yielded significant differences across school years in the likelihood of making a fruit snack choice tomorrow (F (8, 17550) = 13.951, p < .0001, MSE = 1.350, Eta squared = .006). Based on Bonferroni post hoc tests, 2010-2011 was different from all other years. 2007-2008 and 2008-2009 were different from all previous years. 2009-2010 and 2010-2011 were also different from each other. Intent to make a fruit snack choice one year from now also changed in the 10 year time period (F (8, 17550) = 19.408, p < .0001, MSE = 1.256, Eta squared = .009). Based on Bonferroni post hoc tests, 2010 – 2011 was different from all other years. 2005 – 2006 was different than 2003 - 2004 and 2004 - 2005.
There were also significant changes in the ten year time period in the likelihood of eating five FV tomorrow, (F (8, 17550) = 16.292, p < .0001, MSE = 1.314, Eta squared = .007). Based on Bonferroni post hoc tests, 2010 - 2011 was different from all other years. 2003 - 2004 and 2004 - 2005 were also different from each other. Similarly there were changes across time in the likelihood of eating five FV one year from now, (F (8, 17550) = 19.408, p < .0001, MSE = 1.256, Eta squared = .009). Based on Bonferroni post hoc tests, 2010 – 2011 was different from all other years. 2005 – 2006 was different than 2003-2004 and 2004- 2005 (Table 4).
Table 4. Change Across Time in Intention to Select Fruit and Intention to Eat Specified Fruit and Vegetable Quality.
Pearson Product moment correlations between knowledge about optimal FV consumption and intended and actual behaviors indicated no significant correlations, with the exception of a correlation between fruit consumption in the past week and knowledge (r = -.029, p < .05). All other intent and behavior variables were significantly correlated (p < .001) (Table 5).
Table 5. Pearson Correlations among Knowledge of Fruit and Vegetable Recommendations, Behavioral Intentions and Reported Behaviors.
There was a statistically significant improvement in knowledge of the recommended number of servings of FV from 2001-2011. The small effect size, however, suggests true change is minimal. Despite no meaningful change, the knowledge scores in this study reveal a higher correct rate than in other investigations [13, 27]. Researchers have suggested that a lack of adequate knowledge is a primary reason for low FV intake [13, 28].
The increase in knowledge has been reported in the U.S. and Australia, indicating that messages being provided through educational means, primarily via social marketing and emphasizing a specific number of servings of FV per day, have been effective .
The ten-year trends for fruit intake reveal a statistically significant increase in consumption over time. This is consistent with Anderson et al , Pettigrew et al , and Fischer et al . However, the ten-year trends for vegetable consumption remained flat (unchanged). This is also consistent with Anderson et al  and Fischer et al . One concern is that popular choices include fruit juice and fried potatoes, which are considered less nutrient dense than several other potential FV choices .
Despite evidence showing improvement for select people and groups, national averages continue to reveal that most Americans consume on average far less than the recommended amount of FV servings each day [6, 32]. According to the NHANES, the majority of the US population, across all age-groups, does not meet the recommended requirements within any of the nutrientrich food groups, including FV, and are over-consuming solid fats, added sugars and alcoholic beverages (in age 19+) . Behan  reported that feeding patterns initiated as early as 9 to 11 months reflected a trend of not meeting FV consumption requirements as found in the Feeding Infants and Toddlers (FITS) studies.
The trends related to behavioral intent were statistically significant but once again the effect size was minimal. Pettigrew et al  suggested that children’s FV intake is far below their own perceived ideal intakes, which may indicate that children would increase consumption if more FV were available. Children seem to have received and internalized the recommended guidelines but are not necessarily acting on them . Behan  claims “knowing is not doing” as we continue to see discrepancies between our nation’s nutrition knowledge and consumption patterns.
The disconnect between knowledge, behavior, and behavioral intent is supported by this study and needs significant attention. Educational campaigns have “worked” at creating awareness about the specific number of servings, but where are the barriers between knowledge and application?
Although the trends in knowledge improved over time in this study, knowledge did not correlate with FV consumption or with behavioral intent. This is consistent with previous findings that the percentage of Americans who know they should eat five or more servings of FV has increased from 8% to 36% since 1991  despite the consumption rates not corresponding proportionately . This discrepancy between knowledge, intent and behavior was also reported by Pettigrew et al , who found children’s perceptions of ideal intake were much higher than actual reported intake. A similar finding was seen in Australian children , US college students  and adults . Additionally, Spronk et al  found a weak, positive relationship between nutrition knowledge and dietary quality, however, the preponderance of research suggest this may be an anomaly.
In infancy and early childhood, parents and caregivers typically make the purchasing decisions for groceries and food at home, and children have minimal influence. Often between the ages of 7-11 children can develop and utilize strategies for influencing their parents’ food purchasing decisions as well as their own food choices and purchases . Future efforts seeking to improve dietary quality and FV consumption need to consider utilizing a multi-dimensional and layered approach early in the lifecycle of a child. Such approaches need to consider synergistic plans where households are working in unison with schools, communities, workplaces, industry and government with a cohesive message and approach that takes into consideration many of the compounding inter-related factors.
Components that support such a plan must commence early in the home where parents and caregivers can provide food and meals prepared in an environment where the availability of nutrient dense foods (including FV), parental modeling, parental intake, parental encouragement and family rules, have all been shown to positively impact FV intake for children . When the home serves as the primary basis for meals children consume more FV, are more likely to reproduce the behaviors of parents and caregivers and also develop the skills of self-efficacy . Furthermore, the meal planning at home is more conducive for including the approach suggested by Behan  where an FV is included with every food encounter. The home environment also provides an opportunity to teach and model age-appropriate serving sizes which have been shown to be a challenge for some .
Involving children and adolescents in a garden-based nutrition education program, at school or in the community, has been shown to be another reinforcing approach to increase FV consumption [40, 41]. In addition to personal gardening, shopping at local markets, and Community Supported Agriculture (CSA’s) also assist in altering the foodscape in helping the healthy choice becoming the default choice.
Schools play a key role in a comprehensive approach to improving dietary intake. Often schools have wellness and food service policies that put them at the forefront in altering the foodscape children are exposed to [42, 43]. Schools can also improve FV consumption and overall dietary quality through strategies such as scheduling recess prior to lunch , extending lunch to 30-minutesinstead of 20 minutes , and using the offer versus serve method for lunch .
The displacement of FV by energy-dense, nutrient-poor food and beverages are an area of concern especially as children move into adolescence and young adulthood with the increased desires of autonomy and personal food selection. There is a need to create food environments that normalize the consumption of FV by all age groups. Half of all the advertisements being shown during children’s television shows are for food , thus the importance of using this medium to develop more appropriate social norms for selection of nutrient-dense. There is a discrepancy between marketing expenditures which favors the selection of energydense, nutrient-poor products targeted to young people (2006 industry expenditures of $1.62 billion) and marketing of FV products ($11.4 million). Kraak et al  suggested that governments, agencies and industries should be setting policy on marketing, pricing and availability of FV. The United States has been proactive in developing an action plan that promotes the consumption of FV through the National Fruit & Vegetable Alliance national action plan .
In summary, despite ongoing national, state, and local efforts to better inform and educate children and their parents about FV consumption; there has been minimal change over the last tenyears in 5th-grade children’s overall FV knowledge, behavior and intent. Since our findings mirror those of other countries , it is critical that future efforts involve parents and children in practical educational models such as shopping, recipes and food preparation labs. In addition, effectiveness, sustainability and scalability must also be top priorities among funding agencies.
Acknowledgement and Declarations
None of the authors have a conflict of interest. No outside funding was provided for this project.
- Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM (2010) Prevalence of high body mass index in US children and adolescents, 2007-2008. JAMA 303(3): 242-249.
- Biro FM, Wien M (2010) Childhood obesity and adult morbidities. Am J Clin Nutr 91(5): 1499S-1505S.
- Freedman DS, Zuguo M, Srinivasan SR, Berenson GS, Dietz WH (2007) Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study. J Pediatr 150(1): 12-17.
- Fungwe T, Guenther PM, Juan WY, Hiza H, Lino M (2009) The quality of children’s diets in 2003-04 as measured by the Healthy Eating Index-2005. USDA. Nutrition Insight 43. Online link: www.cnpp.usda.gov/sites/default/files/nutrition_insights_uploads/Insight43.pdf
- Gidding SS, Dennison BA, Birch LL, Daniels SR, Gillman MW et al (2005) Dietary recommendations for children and adolescents: a guide for practitioners: consensus statement from the American Heart Association. Circulation 112(13): 2061-2075.
- Guenther PM, Dodd KW, Reedy J, Krebs-Smith SM (2006) Most Americans eat much less than recommended amounts of fruits and vegetables. J Am Diet Assoc 106(9): 1371-1379.
- U.S. Department of Agriculture [USDA] (2010) U.S. Department of Health and Human Services. Dietary Guidelines for Americans. (7th edtn), Washington, DC, US Government Printing Office; 2010. Online link: www.health.gov/dietaryguidelines/dga2010/dietaryguidelines2010.pdf
- May AL, Kim SA, Sherry B, Blanck HM (2013) Childhood obesity task forces established by state legislatures, 2001-2010. Prev Chronic Dis10: 120-153.
- U.S. Department of Agriculture [USDA]. FY 2014. Budget summary and annual performance plan. www.obpa.usda.gov/budsum/FY14budsum.pdf Accessed March 18, 2014.
- Overweight and obesity. Centers for Disease Control and Prevention Web site. www.cdc.gov/obesity/stateprograms/programGoal.html Published April 27, 2012.Accessed March 18, 2014.
- Reynolds KD, Bishop DB, Chou CP, Xie B, Nebeling L, et al (2004) Contrasting mediating variables in two 5-a-day nutrition intervention programs. Prev Med 39(5): 882-893.
- Edwards JSA, Hartwell HH (2002) Fruit and vegetables – attitudes and knowledge of primary school children. J Hum Nutr Diet 15(5): 365-374.
- Beech B, Rice R, Myers L, Johnson C, Nicklas TA (1999) Knowledge, attitudes, and practices related to fruit and vegetable consumption of high school students. J Adolesc Health 24(4): 244-250.
- Schroeter C, House L, Lorence A (2007) Fruit and vegetable consumption among college students in Arkansas and Florida: Food culture vs. health knowledge. Int Food Agribus Man Rev 10(3): 63-89. Online Link: www.digitalcommons.calpoly.edu/agb_fac/91/
- Slining MM, Mathias KC, Popkin BM (2013) Trends in Food and Beverage Sources among US Children and Adolescents: 1989-2010. J Acad Nutr Diet 113(12): 1683-1694.
- Lien N, Lytle LA, Komro KA (2002) Applying theory of planned behavior to fruit and vegetable consumption of young adolescents. Am J Health Promot 16(4): 189-197.
- Anderson AS, Porteous LEG, Foster E, Higgins C, Stead M, et al (2005) The impact of a school-based nutrition education intervention on dietary intake and cognitive and attitudinal variables relating to fruits and vegetables. Public Health Nutri 8(6): 650-656.
- Blanchard CM, Fisher J, Sparling PB, Shanks TH, Nehl E, et al (2009) Understanding adherence to 5 servings of fruits and vegetables per day: A Theory of planned behavior perspective. J Nutr Educ Behav 41(1): 3-10.
- Godin G, Amireault S, Bélanger-Gravel A, Vohl MC, Pérusse L, et al (2010) Prediction of daily fruit and vegetable consumption among overweight and obese individuals. Appetite 54(3): 480-484.
- ChanLin LJ, Huang RFS, Chan KC (2003) Web-based instruction in learning nutrition. J Instr Psychol 30(1): 12-21.
- Palmer S, Graham G, Elliott E (2005) Effects of a web-based health program on fifthgrade children’s physical activity knowledge, attitudes and behavior. Am J Health Educ 36(2): 86-92.Online link: www.eric.ed.gov/?id=EJ792809
- Health Resources and Services Administration. About health literacy. U.S. Department of Health and Human Services Web site. www.hrsa.gov/publichealth/healthliteracy/healthlitabout.html Published (n.d.).Accessed March 18, 2014.
- Centers for Disease Control and Prevention (2010) Youth Risk Behavior Surveillance – US, 2009, Surveillance Summaries. MMWR 59(No. SS-5). Web link: http://www.cdc.gov/mmwr/pdf/ss/ss5905.pdf
- Centers for Disease Control and Prevention. Adolescent and school health. Website.www.cdc.gov/HealthyYouth/yrbs/index.htm?s_cid=tw_cdc16. Published June 12, 2014. Updated July 9, 2014. Accessed July 27,2014.
- Sallis JF, McKenzie TL, Alcaraz JE, Kolody B, Faucette N, et al (1997) The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, Play and Active Recreation for Kids. Am J Public Health 87(8): 1328-1334.
- Eaton DK, Kann L, Kinchen S, Shanklin S, Ross J, et al (2010) Youth risk behavior surveillance-United States, 2009. MMWR 59: 20.
- Pettigrew S, Pescud M, Donovan RJ (2009) Is children’s knowledge of fruit and vegetable recommendations related to their intakes? Int Rev Public Nonprofit Mark 6(2): 109-118. Online link: research-repository.uwa.edu.au/en/publications/is-childrens-knowledge-of-fruit-and-vegetable-recommendations-related-to-their-intakes%287407f108-2b5c-4eacb8b6-14396fd5f345%29/export.html
- Morgan PJ, Warren JM, Lubans DR, Saunders KL, Quick GI, et al (2010) The impact of nutrition education with and without a school garden on knowledge, vegetable intake and preferences and quality of school life among primary-school students. Public Health Nutr 13(11): 1931-1940.
- Pollard CM, Miller MR, Daly AM, Crouchley KE, Lang AJ, et al (2008) Increasing fruit and vegetable consumption: success of the Western Australian Go for 2&5 campaign. Public Health Nutr 11(3): 314-320.
- Fischer C, Brug J, Tak NI, Yngve A, te Velde SJ (2011) Differences in fruit and vegetable intake and their determinants among 11-year-old schoolchildren between 2003 and 2009. Int J Behav Nutr Phys Act 8(141): 10-1186.
- Lorson BA, Melgar-Quinonez HR, Taylor CA (2009) Correlates of fruit and vegetable intakes in US children. J Am Diet Assoc 109(3): 474-478.
- Krebs-Smith SM, Guenther PM, Subar AF, Kirkpatrick SI, Dodd KW (2010) Americans do not meet Federal dietary recommendations. J Nutr 140(10): 1832-1838.
- Behan E (2012) Health care providers: What to say to your patients about diet in 30 seconds. Am J Lifestyle Med 6(6): 448-451.
- Schultz J (2003) Food fight: NCI aims for consumer clarity with complaint against dairy group. J Natl Cancer Inst 95(13): 934-935.
- Kipping RP, Howe LD, Jago R, Campbell R, Wells S, et al (2014) Effect of intervention aimed at increasing physical activity, reducing sedentary behaviour, and increasing fruit and vegetable consumption in children: Active for Life Year 5 (AFLY5) school based cluster randomised controlled trial. BMJ 348: 3256.
- Spronk I, Kullen C, Burdon C, O’Connor H (2014) Relationship between nutrition knowledge and dietary intake. Br J Nutr 111(10): 1713-1726.
- Marquis M (2004) Strategies for influencing parental decisions on food purchasing. J Consum Mark 21(2): 134-143. Online link: www.researchgate.net/publication/240258155_Strategies_for_influencing_parental_decisions_on_food_purchasing
- Pearson N, Biddle SJ, GorelyT (2009) Family correlates of fruit and vegetable consumption in children and adolescents: a systematic review. Public Health Nutr 12(2): 267-283.
- Gillman MW, Rifas-Shiman SL, Frazier AL, Rockett HR, Camargo CA Jr, et al (2000) Family dinner and diet quality among older children and adolescents. Arch Fam Med 9(3): 235-240.
- McAleese JD, Rankin LL (2007) Garden-based nutrition education affects fruit and vegetable consumption in sixth-grade adolescents. J Am Diet Assoc 107(4): 662-665.
- Heim S, Stang J, Ireland M (2009) A garden pilot project enhances fruit and vegetable consumption among children. J Am Diet Assoc 109(7): 1220- 1226.
- Hirschman J, Chriqui JF (2013) School food and nutrition policy, monitoring and evaluation in the USA. Public Health Nutr 16(6): 982-988.
- Cullen KW, Watson KB (2009) The impact of the Texas public school nutrition policy on student food selection and sales in Texas. Am J Public Health 99(4): 706-712.
- Getlinger M, Laughlin C, Bell E, Akre C, Arjmandi BH (1996) Food waste is reduced when elementary-school children have recess before lunch. J Am Diet Assoc 96(9): 906-908.
- Bergman E, Buergel N, Enamuthu J, Sanchez A (2000) Time spent by school children to eat lunch. J Am Diet Assoc 100(6): 696-698.
- Goggans MH, Lambert L, Chang Y (2011) Offer versus Serve or Serve Only: Does Service Method Affect Elementary Children's Fruit and Vegetable Consumption? J Child Nutr Manage 35(2). Online link: www.eric.ed.gov/?id=EJ960769
- Kraak VI, Story M, Swinburn BA (2013) Addressing barriers to improve children’s fruit and vegetable intake. Am J Clin Nutr 97: 653-660.
- Creating demand for fruit and veggies. Better Health Foundation Web site. www.pbhfoundation.org/ Published 2008-2014. Accessed July 17, 2014.
- Clarke AM, Ruxton CHS, Hetherington L, O’Neil S, McMillan B (2009) School intervention to improve preferences for fruit and vegetables. J Nutr Food Sci 39(2): 118-127. www.dx.doi.org/10.1108/00346650910943226