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Transl Behav Med. 2013 June; 3(2): 218–225.
Published online 2013 February 28. doi:  10.1007/s13142-013-0203-6
PMCID: PMC3717981

Nutrition knowledge of low-income parents of obese children


Minority and low-income children are overrepresented among obese US children. Lack of basic nutrition knowledge among parents may contribute to this disparity. The purpose of this study is to measure nutrition knowledge of parents of Medicaid-insured obese children using a simple low-literacy tool. Parents, recruited from pediatric clinics, demonstrated their nutrition knowledge by placing food stickers into cells on a printed grid with food groups displayed in columns and three nutrition categories displayed in rows. In general, parents (n = 135; 74.8 % black; 79.2 % income of ≤$25,000/year) correctly identified food groups (median = 90.5 % correct). Nutritional categories were more commonly misidentified (median = 67 % correct), with parents mostly believing foods were healthier than they were. Multivariable linear regression revealed black race (p = 0.02), no college education (p = 0.02) and income of <$15,000 (p = 0.03) independently predicted misidentification of nutritional categories. Parents’ understanding of food’s nutritional value is variable. Black race, less education, and very low income are associated with poorer nutrition knowledge.

KEYWORDS: Pediatric obesity, Nutrition knowledge, Assessment, Health disparity


Obesity in childhood now affects 17 % of American children [1] and is associated with life-threatening and costly health consequences [2] that start in childhood [3] and persist into adulthood [4]. Minority [1] and poor [5] children are at higher risk for becoming obese than their white peers and peers living at a higher socioeconomic status (SES). For the general population, family-based interventions targeting reductions in high-calorie, low nutrient foods and increases in healthy food intake, along with activity changes, are effective in reducing child weight [6]. However, few interventions have been tailored specifically for minority and low-income groups who are at highest risk for childhood obesity [7, 8].

Nutrition knowledge in pediatric obesity treatment

Kumanyika and colleagues noted that disadvantaged and minority children live in more obesogenic, fast food-filled, low-safety environments than do their white and higher SES peers [5]. Parental nutrition knowledge may also play a role in the development of child obesity since parents must have a good working knowledge of basic nutritional concepts in order to improve food shopping, preparation, and delivery for their obese children. To support a randomized controlled trial testing a parent-focused pediatric obesity intervention for Medicaid-insured children ages two to eleven, we searched for an instrument to assess parental nutrition knowledge. We found none appropriate for use with a sample of low-income and possibly low literacy parents/adults.

The most well-established measure of adults’ nutrition knowledge is the 149-item General Nutrition Knowledge Questionnaire for Adults from the Diet and Health Knowledge Survey, conducted telephonically by the U.S. Department of Agriculture (USDA) from 1994 through 1996 with 5,649 US adults [9]. The questionnaire assessed knowledge of recommended daily servings of food groups and understanding of the relative nutrient content of various foods in a forced choice format (e.g., “Which has more fat: hot dogs or ham?”). Results showed that lower income and black adults were less accurate in categorizing common foods that contain fat and in understanding the connection between cholesterol and fat in foods. The USDA survey questions may not be appropriate for use in an intervention trial with low SES adults, however, because it presumes adequate knowledge of standard food group nomenclature and of specific nutrients and terms (e.g., fats, cholesterol, saturated/polyunsaturated, fiber, etc.). The other best-known nutrition survey, the National Health and Nutrition Examination Survey [10], assesses a broad spectrum of eating behaviors but not nutrition knowledge specifically.

Other published reports describe the nutrition knowledge of adults from specialized samples such as medical students and professionals (e.g., [1113]), athletes (e.g., [14, 15]), grocery store consumers (e.g., [16]), and other general adult samples (e.g., [17, 18]). Most of these reports used either the USDA survey questions (e.g., [19]), a similar measure developed in Europe [20] (e.g., [18]), or investigator-developed measures specific to the population studied (e.g., [13, 21, 22]).

There are few reports of nutrition knowledge in minority or low-income English-speaking samples in the USA. Resnicow and colleagues [23] asked one knowledge question in a 19-item study-specific assessment of fruit and vegetable consumption in an African American sample. A study of primarily Hispanic (race unspecified) and white non-Hispanic low-income and low-literacy adults [24] used a 14-item knowledge questionnaire specific to concepts in an intervention program; results showed relatively low knowledge at baseline for intervention and control groups (57 and 56 % correct, respectively). Results by race were not reported.

There are several published reports assessing nutrition knowledge of parents; these generally involved samples not similar to ours and/or measures that did not give a broad picture of parents’ knowledge. For example, Ray and colleagues [25] measured the nutrition knowledge of Finnish parents using only two questions asking about the number of daily servings of fruit and vegetables recommended by the World Health Organization. Other investigators asked parents questions specific to feeding practices with infants or very young children (e.g., [26]) or in non-English speaking samples (e.g., [27, 28]). Six studies were identified that measured nutrition knowledge in English-speaking low-income mothers. Four used complicated words and terms (e.g., “phytochemicals” and “folic acid”) [29], unspecified investigator-developed questions [30, 31], or focus-group methodology [32]. Two used the same investigator-developed 25-item questionnaire with an unspecified number of questions testing knowledge of vitamins, minerals, macronutrients, and the Food Guide Pyramid [33, 34]. Among non-Hispanic participants in the latter study, white low-income mothers scored significantly higher at baseline than black mothers; knowledge results were not reported by race in the former report.

In summary, little is known about the nutrition knowledge of low SES adults or parents of US children who are obese. Existing measures either presume a basic understanding of traditional food groups and/or basic nutritional concepts, an assumption that has not been tested and that may not be warranted amongst adults in families at highest risk for obesity. The aim of this paper is to describe the level of knowledge of very basic nutritional concepts demonstrated in a sample of low-income adults who are primary caregivers of an obese child, using a novel assessment method.


Participants and setting

Participants were parents (93 %) or other adult primary caregiving relatives (7 %; all called “parents” in this paper) of Medicaid-insured obese children. Participants were recruited from a large urban academic hospital-based pediatric primary care clinic and were enrolled with their obese child (body mass index (BMI) ≥95th percentile for age and gender) ages 2 to 11 years in a randomized clinical pediatric obesity trial targeting parents. The University of Pittsburgh Institutional Review Board approved the study that was carried out in 2010 in compliance with HIPAA guidelines. All participants provided written informed consent. One study participant, a recent immigrant, was excluded from the assessment because he did not recognize many foods queried in the assessment.

Study design and measures

Nutrition knowledge was assessed at baseline with a novel instrument, the Nutrition Knowledge Grid-Basic (NKG-Basic), developed by our research group, that assesses participants’ ability to accurately identify the food group and relative nutritional status of common foods. The NKG-Basic requires little reading ability and is presented visually as a grid formed by columns that display eight food groups and rows that represent three nutritional categories. Stickers portraying 23 common foods and beverages are available to be placed by participants in the grid cell that most accurately describes each food’s group and category (see Fig. 1).

Fig 1
aColumn labels in brackets are for readability in the journal article only; actual NKG-Basic grid is 11 × 14 in., and labels are easily visible. b Sticker sizes relative to grid are enlarged for this figure

Instrument development

The instrument was developed using food groups from the Food Guide Pyramid [35] and the National Heart Lung and Blood Institute’s WeCan! categorization of foods into Go (high nutrition/low caloric content), Slow (good nutrition/moderate caloric content), and Whoa (low nutrition/high caloric content) ( nutritional categories. Columns display traditional food groups in six columns plus two additional columns, added because of our interest in assessing parents’ understanding of healthier vs. less healthy beverages and sweets/snacks. The additional snack and beverage groups are relevant because overconsumption of high calorie-low nutrition beverages is implicated in the development and maintenance of pediatric obesity, particularly in our study population (e.g., [36]), and because parental understanding of energy-dense vs. nutrient-dense snacks is a key component in decreasing overall caloric intake in most pediatric obesity interventions (e.g., [37]). Rows on the NKG-Basic grid display nutritional categories (Go, Slow, and Whoa). Food stickers were developed in consultation with a registered dietitian who has significant experience preparing nutrition education programs and materials for low-income groups in our region. The stickers represent a broad spectrum of healthy and not-so-healthy foods typically consumed by the residents of our geographical area who are primarily white (66.5 %) or black (25.8 %) and non-Hispanic (97.7 %) [38].

The column and row format of the NKG-Basic was employed to allow for separate assessment of participants’ knowledge of each food’s group and of its nutritional category (e.g., participants can place a sticker in the correct column, but the incorrect row, indicating they know the food’s group, but do not understand its nutritional category) for each food presented. All food groups and nutritional categories are represented among the 23 foods.

The instrument is scored by assigning a binary accuracy score (1 = correct; 0 = incorrect) for the food group and, separately, for the nutritional category of each food sticker (excluding the broccoli and potato chip stickers that were used as examples). Six foods are scored as correct if placed in either of two food group columns (100 % orange juice is correct if identified as a fruit or beverage; skim, 2 % and whole milk are correct as both milk/milk like foods and beverages; doughnut as both a bread/cereal and sweets/snack; and tomato as both a fruit and vegetable). This scoring method resulted in a total of 27 possible correct food group placements (five vegetables, five fruits, two breads/cereals, three dairy, four meats and other proteins, one fat, two sweets/snacks, five beverages), and 21 correct nutrition category placements (eight Go, six Slow, seven Whoa foods).

Accuracy of food group and nutritional category identification was determined by dividing the number of correctly identified foods by the total number of foods in each group or category. For example, for food groups, because there were five vegetables, possible accuracy values were 0, 20, 40, 60, 80, and 100 % correct, whereas because there was only one fat, possible accuracy values were 0 and 100 %.

NKG-Basic administration

A trained research staff person began each assessment by orienting the participant to the instrument. Displaying the grid and stickers, the staff person said, “We want to understand how much families know about healthy and not-so-healthy foods.” The staff person explained the NKG-Basic column (food groups) and row (nutritional categories) headings and gave a brief description of what makes a food Go, Slow or Whoa (amount of calories compared with the amount of nutrition provided by the food; see row labels on Fig. 1). The participant was then asked to “place each food sticker in the right box that tells its food group and whether it is a Go, Slow, or Whoa food.” Then, as an example, the researcher demonstrated how to place the broccoli sticker by talking the participant through the process of deciding the food group and nutritional category of broccoli and, finally, saying, “That’s right. You put the broccoli sticker under vegetables in the Go row,” and asking the participant to affix the sticker in the correct cell. The participant then completed an example by her/himself with the potato chips sticker and received correction and assistance if needed. If the participant had difficulty placing the chip sticker accurately, the procedure was completed a third time with the water sticker; this occurred only once in 135 assessments.

When the participant understood the use of the grid, the researcher instructed him/her to place the remaining stickers in the correct “boxes.” Foods that might be ambiguous from their pictures because of food preparation factors were clarified as the participant worked (e.g.: “That’s baked ham”; “Assume the corn-on-the-cob is plain with no butter on it”; “Ignore the bun and mustard; just focus on the hot dog”; “Those are hard-boiled eggs.”). No feedback about performance was provided to participants as they completed the task.

Other measures

Participants completed a self-report, self-administered demographic questionnaire on which they checked off their employment status, household income, and race from multiple choice lists of categories; ethnicity was recorded separately as Hispanic or Non-Hispanic. Age was determined from reported date of birth. Weight and height of parent participants and their participating child were measured on a digital scale and research-quality portable stadiometer, respectively, by trained research staff in the research office that was housed within the pediatric clinic. BMI was calculated using the formula: weight in kilograms/(height in meters)2 [39].

Data analysis

Descriptive statistics appropriate to the distribution of the data were used to characterize the sample and their knowledge of food groups and nutritional status. Kruskal–Wallis one way analysis of variance tested whether categorical participant characteristics (i.e., race, employment status, homemaker status, and household income) were related to nutrition knowledge (i.e., percentage of correctly identified nutritional groups and categories). The Jonckheere–Terpstra trend test tested whether ordinal participant characteristics (i.e., education) were related to percentages of correct responses. Spearman correlation tested for correlations between continuous participant characteristics (i.e., age and BMI) and percentages of correct responses. Multivariable linear regression was used to determine whether age, race, education, employment status, and income were independently related to correct identification of nutritional categories, controlling for gender. Initial analyses with race, education, employment status and income led to collapsing categories when relationships did not differ significantly between all categories. Variables other than gender that were not significant in the model (i.e., p  0.05) were removed by using backward elimination. The characteristics of participants’ children who were enrolled with them in the intervention trial were not included in the multivariate analysis, as they were not hypothesized to be predictive of parental nutrition knowledge.


The majority of the 135 participants in the sample was black (74.8 %), female (97 %), and did not work for pay (60 %). See Table  1 for a full description of the sample. Per the recruitment criteria, all participants had one obese child aged 2 to 11 years old enrolled with them in the randomized controlled intervention trial. The median BMI percentile of participants’ enrolled children was 98.6 % (quartiles, 97.7, 99.4), and the median age was 9 years (quartiles, 6, 10); 82.2 % of the children were black and just over half (53.3 %) were female.

Table 1
Characteristics of participants (N = 135)

Accuracy of food group knowledge

Overall, participants understood food groups well (Table  2). At least 75 % of participants correctly identified 100 % of foods in the grain, fat, vegetable, dairy, and beverage groups. Two thirds (67.4 %) of participants correctly identified 100 % of proteins and an additional 25.9 % correctly identified 75 % of foods in that group. Raisins, which belonged in the fruit group, were identified as “sweets and snacks” by 46 % of participants, accounting for lower scores in these two food groups (94.8 % correctly identified either four or five of five fruits; 83 % correctly identified either two or three of three sweets/snacks). Due to the high accuracy in food group knowledge overall, there was little variation among sample subgroups, making it impractical to test for associations between participant characteristics and food group knowledge.

Table 2
Frequency of correct identification of food groups and nutritional categories

Accuracy of nutritional category knowledge

There was more variation in participants’ knowledge of foods’ nutritional value (Table  2). Go foods were correctly identified more often than Slow or Whoa foods. The most frequent misidentifications in these two latter categories involved believing that foods were healthier than they were. For example, Whoa foods were frequently misidentified as Slow foods (hot dog (56 %), whole milk (31 %), cookie (24 %), fried chicken (20 %), doughnut (16 %), and French fries (10 %)) and Slow foods were frequently misidentified as Go foods (raisins (72 %), eggs (62 %), orange juice (49 %), and 2 % milk (47 %)).

Factors associated with accuracy of nutritional category knowledge

Black adults correctly identified foods’ nutritional status significantly less often (median (quartiles), 66.7 % (57.1 and 76.2 %)) compared with non-black adults (71.4 % (61.9 and 81.0 %)) (p = 0.03). Adults with an income of ≤$15,000 were less likely to identify foods’ nutritional status correctly (61.9 % (57.1 and 76.2 %)), compared with adults with somewhat higher income (71.4 % (57.1 and 76.2 %); p = 0.02). Adults without any college education also correctly identified foods’ nutritional status less often, (61.9 % (57.1 and 71.4 %)) compared with adults with some college education (71.4 % (61.9 and 76.2 %)) or a bachelor’s degree (71.4 % (66.7 and 95.2 %); p < 0.01). Age, employment status, and BMI were not significantly related to correct identification of nutritional categories (all p > 0.05). In multivariable analysis controlling for gender (Table  3), non-black race (p = 0.02), a college education (p = 0.02), and an income greater than $15,000 (p = 0.03) were independently related to correct identification of nutritional categories, although the amount of variance explained by each variable (4–5 %) was minimal.

Table 3
Participant characteristics independently related to correct identification of nutritional categories, controlling for gender


In this study, we showed that a primarily black sample of low SES parents of obese children was better able to correctly identify traditional food groups than foods’ nutritional status for a set of very common foods. We hypothesize that the broad dissemination of the Food Guide Pyramid and educational information from other sources has produced adequate understanding of food groups, at least for the foods we queried. The foods chosen for our assessment tool were perhaps too easily identifiable by food group, given that we found no variability in results. However, this study was a first attempt to find out what low-income parents of obese children know; the finding that they are very accurate in their basic understanding of food groups is useful knowledge. Our results also showed that low SES parents have poor recognition of common foods’ nutritional value; this result is in line with findings from the USDA 1994–1996 Survey [40]. It is also interesting to note that these adults’ nutrition knowledge was statistically unrelated to their BMI, although it might have been expected that poorer nutrition knowledge would lead to increased weight as a result of poor eating habits. Our finding of no relationship between the two may be a result of the lack of BMI variability in the sample (only 8.4 % were within normal BMI range; 71.0 % were obese).

Also interesting was that although the entire sample was low-income, the very lowest income adults (i.e., annual income of <$15,000) understood nutritional categories even more poorly than their somewhat higher income peers. Our results also demonstrate that black race and lower education within a low-income sample were independently related to misidentification of low nutrition and high caloric content foods. Misidentification was generally in the direction of believing that food items were healthier than they actually are. These findings are consistent with USDA survey results that found that black participants were less able than white participants to correctly identify foods with higher fat content [40].

Given that parents and other caregiving adults in families have a major role in purchasing, preparing, and delivering food to children, it is problematic that understanding of basic nutritional value was poor for the low-income parents in our study. These results may have implications for preventing and intervening in pediatric obesity risk for low-income children. Future studies would be needed to confirm the role of nutrition knowledge on the development or maintenance of obesity in low-income children. Studies could evaluate whether improving parents’ nutrition knowledge results in reduced risk of obesity for minority and low SES children by testing the effect of improvements in parental nutrition knowledge on parental food-delivery behaviors and/or child BMI outcomes.

Limitations of the study

The NKG-Basic was developed with the demographics of our region in mind. Because foods queried on the NKG-Basic do not include foods common to Hispanic, Asian, or other subgroups, the assessment tool would need to be revised for study in those groups. Results presented are descriptive of our primarily black sample and may not be generalizable to all low-income parents. In addition, we did not evaluate whether the NKG-Basic tool was linked to parents’ food-delivery behaviors or to their own or their children’s eating behaviors, so a link between nutrition knowledge as assessed with this tool cannot be considered predictive at this point of pediatric obesity-related factors.

The NKG-Basic, developed to avoid literacy issues may, however, be limited by low numeracy skills, defined as “the ability to reason with numbers and other mathematical concepts” [41]. Individuals with less exposure to math education, a venue in which chart- and table-reading skills are taught, may be less able to interpret the meaning of columns and rows, thus affecting their ability to understand the nature of the assessment task. To lower the risk of this problem, we attempted to ensure that participants could place two example stickers accurately before we proceeded and that did appear to be the case.

Next steps

The Food Guide Pyramid was replaced in June 2011 by MyPlate ( that makes use of some of the traditional food groups in its plate-like display (fruits, grains, vegetables, protein, and dairy) and describes others (fats/oils and sugar/sweets) on linked but secondary website pages. Since the Pyramid is the mechanism by which most parents of young children today learned to understand food groups, we believe these groups are still relevant for assessing adults’ general food knowledge. However, our Nutrition Knowledge Grid is easily translatable to a MyPlate upgrade using a reconfigured grid format.

The NKG-Basic instrument may be an informative tool for clinicians including nurses, dietitians, behavioral specialists and primary care physicians in the context of obesity intervention with adults/parents and children in clinical settings. Researchers pursuing clinical trials for pediatric obesity may find the tool useful as well, especially in a revised version that would investigate parents’ understanding of a broader range of foods than was assessed in this study, including “combination foods”, ones that combine foods from more than one food group—such as casseroles, pizza, sandwiches, vegetables cooked in fat, etc. Research with a revised NKG-Basic should evaluate its usefulness as a tool with higher income groups and/or a more general group of parents with obese children, as well as whether scores on the tool are associated with relevant parental food-delivery behaviors and/or child health outcomes. We envision a two-phase research strategy to further refine the usefulness of the NKG-Basic as an assessment tool that could be useful in intervention development. In phase one, we would confirm or deny the results of the current study (that parents understand food groups but not nutritional value) by developing and testing a revised NKG-Basic with a broader range of foods, using foods self-reported in Food Recalls by our current sample of parents to ensure that food stickers portray typical foods for this demographic group. The results of this phase of the study would indicate whether a revised Nutrition Knowledge Grid needs to include both food group columns and nutritional status rows (if phase 1 data show that parents show variability in understanding of both factors) or nutritional status rows only (if study results confirm that most parents understand food groups well). The second phase would involve an intervention study in which food group and/or nutrition status knowledge deficits found in phase 1 would purposefully be remediated as part of a family-based pediatric obesity intervention randomized controlled trial and assessed at baseline and post-intervention using the revised NKG-Basic as an outcome assessment tool.


We thank the following people and organizations for their collaboration and assistance with this study: research interventionists Jess Garrity, MA, and Kelly Powell, BS; research nurses Diana Kearney, RN, CCRC, Tracy Balentine, RN, CCRC, MsCR, Kristy Mackin, BSN, RN, Linette Milkovich, RN, and Marcia Pope, RN, and data manager Darina Protivnak, MSIS. We also thank the physicians, office managers, and staff of the Children's Hospital of UPMC Primary Care Centers (Oakland and Children's at Mercy) and Children's Community Pediatrics-Armstrong Pediatrics (Kittanning) and the University of Pittsburgh CTSI pediatric practice-based research network, Pediatric PittNet, without whose support the project could not have been completed. The project described was supported by the National Institutes of Health through Grant Numbers RC1 MD004564-02, UL1 RR024153, and UL1TR000005.



Practice: Interventions to improve parental food delivery habits for low-income obese children should emphasize a more global understanding of foods’ nutritional value in addition to the more common focus on food groups.

Policy: With the recent dissemination of MyPlate to replace the Food Guide Pyramid, more attention needs to be paid to methods for enhancing adults’ understanding of the relative nutritional value of foods within each food group.

Research: Adult and pediatric obesity prevention and intervention trials for low-income families specifically should assess the presence of nutrition knowledge deficits, include knowledge remediation components in the intervention if applicable, and assess change in knowledge as one part of outcomes measurement for this high-risk population.

Contributor Information

Patricia A. Cluss, Phone: +1-412-6472933, Fax: +1-412-6474252, ude.cmpu@apssulc.

Linda Ewing, Phone: +1-412-6473081, Fax: +1-412-6474252, ude.cmpu@jlgniwe.

Wendy C. King, Phone: +1-412-6241612, Fax: +1-412-6247397, ude.ttip.cde@wgnik.

Evelyn Cohen Reis, Phone: +1-412-6925900, Fax: +1-412-6928516, ude.phc@sier.nyleve.

Judith L. Dodd, Phone: +1-412-3836528, Fax: +1-412-3836636, ude.ttip@ddodj.

Barbara Penner, Phone: +1-412-6473179, Fax: +1-412-6474252, ude.cmpu@cbrennep.


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