Appetite 81 (2014) 277–283

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Research report

Children’s knowledge of packaged and fast food brands and their BMI. Why the relationship matters for policy makers ☆ T. Bettina Cornwell a,*, Anna R. McAlister b, Nancy Polmear-Swendris c a

University of Oregon, 1208 University of Oregon, Lundquist College of Business, Eugene, OR 97403, USA Michigan State University, 404 Wilson Road, Room 327, Communication Arts and Sciences Building, Michigan State University, East Lansing, MI 48824, USA c Ann Arbor Public Schools Preschool and Family Center, 2775 Boardwalk, Ann Arbor, MI 48104, USA b

A R T I C L E

I N F O

Article history: Received 12 February 2014 Received in revised form 19 May 2014 Accepted 13 June 2014 Available online 24 June 2014 Keywords: Food preference Consumption patterns Obesity Children Advertising Marketing

A B S T R A C T

Studies regarding the advancing challenges of obesity in many countries are beginning to converge on the importance of early food exposure and consumption patterns. Across two studies (Study 1, 34 boys, 35 girls; Study 2, 40 boys, 35 girls, ages 3–6), child knowledge of brands offering products high in sugar, salt and fat was shown to be a significant predictor of child BMI, even after controlling for their age and gender and when also considering the extent of their TV viewing. Additionally, two different collage measures of brand knowledge (utilized across the two studies) performed similarly, suggesting that this measure may be serving as a surrogate indicator of an overall pattern of product exposure and consumption. Policy implications are discussed. © 2014 Elsevier Ltd. All rights reserved.

Introduction Longitudinal research on the incidence of childhood obesity in the United States has found that children who are obese in their early teens were typically overweight or obese in kindergarten (Cunningham, Kramer, & Narayan, 2014). Using a representative prospective cohort of 7738 children, the work of Cunningham et al. (2014) showed that, while controlling for a host of individual and socioeconomic variables, overweight five-year-olds were four times as likely as healthy-weight children to become obese by the conclusion of the nine-year study. Analysis of the 2011–2012 National Health and Nutrition Examination Survey (Ogden, Carroll, Kit, & Flegal, 2014) found one third of adults and 17% of youth in the United States are obese. Such research on the trajectory and prevalence obesity has demonstrated the enormity of the problem and helped to focus attention on younger children, however, it does not explain the reasons behind the empirical regularity of the findings. In searching for the starting points of obesity, and in particular, childhood obesity, Cornwell and McAlister (2011) have argued that

☆

Acknowledgements: Financial support from the University of Michigan (OVPR grant) to the first author is gratefully acknowledged. Also we thank the reviewers for many helpful comments. * Corresponding author. E-mail address: [email protected] (T.B. Cornwell). http://dx.doi.org/10.1016/j.appet.2014.06.017 0195-6663/© 2014 Elsevier Ltd. All rights reserved.

understanding how a child’s taste palate is developed via exposure to calorie-dense, nutrient-poor foods may contribute to an understanding of how early food consumption patterns influence weight. In two studies of American preschool children, Cornwell and McAlister (2011) found that children with detailed mental representations of fast food and soda brands had a preference for sugar, salt, and fat in their diets. At about the same time, Kim and Lee (2009) reported a study of 12- to 13-year-old Korean children, which included both salt taste acuity and preference. This study showed that preference for soup with a higher salt content was associated with preference for pizza or hamburgers and frequent consumption of pork cutlets or hamburgers. These authors concluded that frequent consumption of certain fast foods might be associated with preference for salt. With these studies as examples, and the weight of sheer logic, there appears to be a link between children’s consumption of packaged and fast foods (i.e., foods high in sugar, salt, and fat) and physiological, pharmacological, psychological, and social outcomes. In fact, an early model of the mechanisms of food preference and liking described exactly these relationships (Mela, 1995). Recent thinking has pushed this logic even further. For example, Allen (2012) has argued that as children grow up, they form a “theory of food” that is derived from implicitly acquired knowledge and habits associated with food and eating. Allen (2012) built the case for a theory of food as a neurocognitive adaptation via two analogies, one was the namesake concept of

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“theory of mind” and the other was by analogy to language acquisition. Theory of mind (Premack & Woodruff, 1978) is concerned with how intentions are understood, and has become an important cognitive theory that describes the human ability to attribute mental states to oneself and to others. As a useful analogy, Allen pointed out that theory of mind has a developmental trajectory with an important early childhood emphasis, as does his theory of food; that theory of mind is a complex cognitive ability that is not observable but allows the prediction of behavior; and he concluded that theory of food is “an internal, cognitive representation of our diets in our minds” (p. 125). It was, however, the analogy drawn between Allen’s theory of food and language acquisition that was the most compelling. As explained by Allen (2012), “normative diets are analogous to first languages in that they are acquired without overt teaching; they are also difficult to change or modify once a critical period in development is passed” (p. 123). Allen’s thinking was that one’s theory of food “becomes enmeshed in the cognitive makeup of an individual” (Allen, 2012, p. 126) and changing this first language of food is as hard as replacing one’s mother tongue and this is one reason that dieting fails. Between the grand cognitive meta-theory of theory of food and the large-scale findings of childhood overweight as a predictor of overweight and obesity later in life (Cunningham et al., 2014), lies the need for pragmatic research that links early exposure and learning to child overweight and obesity. Thus, of concern in the current research, is the link between packaged and fast foods and a child’s body mass index (BMI, here measured as BMI z-scores). Past research, to be discussed shortly, has attempted to draw a link between BMI and various marketing indicators such as brand logo recognition and television viewing. While use of these measures has disclosed correlations, one criticism that has often arisen was that these measures of branded food exposure were overly simple and only loosely associated with biological outcomes. With this in mind, the potential usefulness of measuring brand knowledge is discussed, after reviewing research adopting other methods.

year-olds – logo recognition varied with children’s BMI scores. Overweight children more frequently identified foods purchased in selfservice or carryout restaurants. In addition, these children were less skilled at recognizing the logos from healthy fruit and vegetable foods (e.g., Chiquita and Green Giant). Of interest to the current research was the nature of the brand logo recognition test, which was originally developed by Fischer et al. (1991) to examine the recognition of tobacco brands. This test asked children to match a logo to a product category and was originally argued to indicate that an advertisement has been both seen and remembered. This logo recognition test captured awareness of brands and further, some brand knowledge in that children must have known the product category for the logo item proffered to be counted as correctly identified. The two empirical studies just reviewed suggested that the logo recognition measure captures some aspects of market exposure and that this exposure was linked to BMI. The value of the measure is in part that it was a child measure rather than a parent report measure. It could be said that, in this type of study, logo recognition was a surrogate indicator of a host of things including social factors that allowed exposure to the logo, food selection and consumption permitting experience, and likely the development of preference that reinforced repeat consumption behavior, that in turn influenced BMI. The shortcoming of the logo alone as a surrogate indicator of seeing and remembering an advertisement is that it does not link to behavioral aspects of brand selection and consumption. In contrast, McAlister and Cornwell’s (2010) brand knowledge collage task captures a more comprehensive understanding of a young child’s brand experience. Because their measure requires the child to distinguish between brands on aspects of product presentation (e.g., recognition of products in their packaging) as well as marketing (e.g., recognition of characters associated with the brand, but not necessarily communicating the brand’s logo), it is a more comprehensive measure of their familiarity and experience with a brand. The brand knowledge collage task has not, however, been examined for any potential link to child BMI in prior studies.

Brand logo recognition

Television viewing and BMI

Research conducted in India has considered brand logo recognition as a measure of exposure to marketing activities (Ueda et al., 2012). Using Fischer, Schwartz, Richards, Goldstein, and Rojas’s (1991) logo matching game, the researchers had three- to 13-year-old children match each of 18 logos to their corresponding product category. The 18 logos represented brands from the product categories of sweet and savory snacks, drinks, and fast food. The researchers found that logo recognition was associated with higher BMI scores and nutritional knowledge but not with unhealthy food preferences or purchase requests by children. Hence they concluded that the correlation between BMI and logo recognition implied that marketing exposure and brand awareness might be part of lifestyle linked to the risk of becoming overweight. It should be noted that this study included both domestic and international brands. Important to interpretation of the findings is the fact that the stimuli included some multinational food brands (e.g., McDonald’s and Domino’s) that were not present in the test community and also not advertised in the area. Thus, the somewhat low recognition for these brands (30% and 35% respectively) was likely due to out of market travel or communications exposures or possibly to guessing. The impact of including recognition measures for brands not in the market arguably contributed noise to the data. In fact, it seems likely that a stronger relationship between brand logo recognition and BMI might have been found if the stimuli had included only locally available brands. Also utilizing Fischer et al.’s (1991) logo recognition measure, Arredondo, Castaneda, Elder, Slymen, and Dozier (2009) found that – among a predominantly Hispanic child population of four- to eight-

The role that television plays in childhood dietary patterns has been disputed. In a widely cited study of preschool children’s television viewing, Dennison, Erb, and Jenkins (2002) found that almost 40% of the approximately three thousand preschoolers in their study had a TV set in their bedroom and were more likely to be overweight. While discussions of television viewing have been supplanted by discussions of “screen viewing time” (Anderson and Whitaker, 2010), the central thinking has continued to orient to time spent viewing screens as a contributor to child weight gain through inactivity and mindless eating, in addition to weight gain associated with the likely exposure to branded food communications. Zimmerman and Bell (2010) examined the relationship between the content of children’s television viewing (commercial, noncommercial) and their BMI. The researchers used panel data collected in 1997 and 2002 and concluded that it was not the sedentary activity of watching televised programming that was the link to obesity but rather the nature of the food and beverage products (i.e., those of low nutritional quality) that were promoted via commercial television ads. Their research also ruled out the possibility that eating while watching television was a contributor to elevated BMI reports among children. Likewise, Jago, Baranowski, Baranowski, Thompson, and Greaves (2005) investigated BMI among a tri-ethnic (37% Anglo-American, 37% African American, 26% Hispanic) sample of preschool children in the US and concluded that sedentary behavior while viewing television was not a significant contributor to changes in BMI over the three-year period of the study. TV viewing was, however, found

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to be a positive correlate. TV viewing may have influenced BMI via the exposure to branded foods that it facilitated, however, children’s overall diets were not significantly related to BMI increases over time. Hence, consistent with Ueda et al. (2012), Jago and colleagues’ findings seemed to suggest that television viewing may have facilitated marketing exposure and brand awareness that became part of an overall lifestyle associated with obesity.

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watching more television (Boyland et al., 2011). Thus, for the current research, the expectation is that children with greater knowledge of packaged and fast food brands would have higher BMI scores than those children with lesser knowledge of these food brands. Naturally, given the discussion above, it is also important to consider the role of television and physical activity. Study purpose and research questions

Physical activity and BMI Not surprisingly, studies of preschoolers aged three to five have found that child weight and child physical activity were correlated and that children who were not engaging in physical activity were at risk for weight gain (Jago et al., 2005; Trost, Sirard, Dowda, Pfeiffer, & Pate, 2003). On the other hand, research has shown an in-school physical activity intervention (three 30-minute sessions a week over 24 weeks) with this age group was not effective in reducing BMI (Reily et al., 2006). A review of the correlates of preschool children’s physical activity behaviors found that age and BMI were consistently shown to have no relationship with a child’s physical activity (Hinkley, Crawford, Salmon, Okely, & Hesketh, 2008). Parental physical activity and time spent outdoors did influence child physical activity, as did gender (boys were found to be more active than girls). Importantly, this review covering 24 articles and 39 variables sought to determine the influencers of activity for children in this age group and not the results of the activity on children in this age group, nonetheless, the results suggested that there was variation in activity level across BMI ranges. While some researchers have reported that physical activity could alleviate obesity issues, Jago et al. (2005) concluded that this was truer of children exiting the preschool years (i.e., six- and seven-year-olds). Brand knowledge and BMI The term “brand knowledge” is used in the present research to refer to a mental network of stored information about a brand, which is different from the more basic measure of brand logo recognition. Why is it useful to examine children’s deeper knowledge of packaged and fast food brands as a predictor of BMI? Firstly, a measure of brand knowledge offers the same benefit as the previously discussed logo recognition measure in that it elicits information directly from a child rather than relying on parent or guardian reports. The measure of brand knowledge, however, has an advantage over brand logo recognition by being more comprehensive in the type of information represented. In particular, it allows the researchers to capture the child’s understanding of “brand differentiation” or the distinction between two competing brands. This more sophisticated understanding of the marketplace must stem from some combination of learning, experience, and understanding. Simple exposure to branded items might have built logo recognition but brand knowledge requires both awareness and discrimination. A host of other research has examined the link between marketing and child food behaviors. Early research linked child exposure to advertising with product preference (Borzekowski & Robinson, 2001), but further research has linked marketing exposures to experience. Robinson, Borzekowski, Matheson, and Kraemer (2007) found that products packaged with a heavily marketed brand are found by preschoolers to be tastier than those same foods in plain packaging. Importantly, moderator analysis showed that this effect was more pronounced when children had more television sets in their home and when they ate the branded product more frequently. In other research, food commercials have been shown to increase child consumption (Halford, Gillespie, Brown, Pontin, & Dovey, 2004), and over-consumption (Halford, Boyland, Hughes, Oliveira, & Dovey, 2007). As well, commercials support a preference for energy-dense foods that is more pronounced for those

This research seeks to contribute to an evolving pool of evidence regarding the role of marketing in childhood obesity. The research focuses particularly on preschool children since they are considered to be a vulnerable population worthy of special research attention. This vulnerability stems from both the obesity trajectory established in childhood but also through their inability to make food selection and consumption decisions on their own right. Though the Centers for Disease Control and Prevention (2013) recently announced a decline in obesity rates among low-income preschool children across 19 states in the US, obesity rates for this group remain alarming and are stagnant in some states and have recently increased in three. Moreover, certain consumer advocacy groups are concerned that the companies that invest most heavily in child-directed marketing will continue to work harder to excite children with their offerings of highly processed foods and beverages. For example, a recent Robert Wood Johnson Foundation report concluded that companies including McDonald’s and Burger King violated their promise of self-regulation. These companies had formerly vowed to focus their advertising on actual food items rather than toy giveaways, but instead featured free toy giveaways in 69% of their television advertising and focused on movie tie-ins 55% of the time (Bernhardt et al., 2013). Given the concerns over food purveyors’ persistent targeting of young children, the present research investigates the interrelationships between preschool children’s exposure to commercial television, their knowledge of popular packaged food and beverage brands, their physical activity, and BMI. Specifically, when controlling for age and gender effects, the following research questions are asked: RQ1: Does exposure to commercial television significantly influence preschool children’s BMI scores? RQ2: Does knowledge of packaged food and beverage brands significantly influence preschool children’s BMI scores? RQ3: Does the amount of daily physical activity counteract the effect of brand knowledge or commercial television exposure on preschool children’s BMI scores? These questions are addressed systematically in each of two studies. The rationale for this is that it is the general reliance on fast food and prepackaged foods (that are high in sugar, salt, and fat and low in nutrients), rather than a set of particular foods, which establish unhealthy dietary patterns. For this reason, the sets of brands are varied across the two studies. Given the similarity in the studies, a single discussion section is used. Study 1 method Participants Participants were 69 children (34 boys, 35 girls) ages three years ten months to five years four months (M = 4 years 9 months, SD = 4 months), as well as one parent of each child. While race was not collected directly from participants, the composition of the preschool was diverse (28% Caucasian, 26% African American, 15% Multiethnic, 10% Hispanic, 8% Asian, 7% other, 5% Arab American, and less than one percent American Indian). Following IRB approval for the

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study, the participants were recruited by sending an information letter and consent form home with all children enrolled at a large Midwestern Head Start preschool. Responses were obtained from seven fathers and 62 mothers aged between 20 and 47 (M = 33 years, SD = 7 years). Researchers worked only with children whose parent/s provided written consent. Parent survey Each parent completed a brief paper survey. Parents were asked to provide their age and gender, as well as the age and gender of their participating child. In an open-ended section, parents were asked to report how many hours per week their child typically spent watching commercial television. The same question was asked about non-commercial screen time (e.g., watching DVDs that did not contain ad breaks). Data pertaining to non-commercial television viewing were not used in subsequent analyses. The question was merely included to encourage parents to make the distinction between commercial and non-commercial screen time so that they would report accurately on the former. Finally, parents were asked how many days per week their child typically engaged in 30 minutes or more of physical activity (such as running, swimming or playing on playground equipment, sliding or climbing). Given that all children in this study participated in physical activity at preschool, little variation was anticipated in in-school activity levels even if the nature and level of play differed from child to child. Thus, a parent report of physical activity was included. Brand knowledge collage task The task developed by McAlister and Cornwell (2010) required children to sort picture cards to create collages reflecting their knowledge about brands. McAlister and Cornwell’s (2010) original task involved four popular fast food and soda brands: McDonald’s, Burger King, Coca-Cola, and Pepsi. Based on brand marketing conceptualizations (Keller, 1993), successful task performance required that a child correctly identified items belonging to a brand (thus capturing brand awareness through a recognition measure), distinguished one brand’s offerings from those of a competitor (thus capturing unique associations that communicate brand differentiation), and disregarded irrelevant distracter items (e.g., swimming goggles did not belong with either fast-food or soda brand). For each of the brands, color pictures were presented on 3 × 3.5 in. cards. Six pictures were used for each brand: three products, two sales/purchase venues, and one “other” card depicting either a trade character or an advertising/merchandise item. During task administration, each brand pair (fast-food pair and soda pair) was assessed on a separate trial. For each trial, the researcher laid out three pages in front of the child. The child was presented with 18 randomly sorted stimulus cards (six per brand, plus six distracter cards that depicted irrelevant products and venues). Each trial was administered by the researcher “sticking the logo of one brand on the first page and the logo of the competing brand on the second page, and saying: ‘We’re going to make some collages. I want you to show me how to make these pictures. This one is the [Coke] picture, so you should put all the [Coke] ones here. This one is the [Pepsi] picture, so put all the [Pepsi] ones here. This one is for any that don’t belong (demonstrating with additional distracter card)’” (McAlister & Cornwell, 2010, p. 212). In addition to using McAlister and Cornwell’s (2010) fast food and soda brand trials, this study also employed a trial for chip brands (Fritos, Doritos) and a trial for cereal brands (Lucky Charms, Trix). These additional trials using different product categories were included to increase the generalizability of results and to facilitate testing of reliability of the measure. The order of presentation of trials was counterbalanced across participants. At the end of each

trial, the researcher photographed the collages for later scoring. One point was awarded for each correctly placed item. Body mass index (BMI) Age-appropriate, gender-specific measures of BMI were recorded for all participating children. Such BMI scores are termed “BMI z-scores” because of the standardization for age and gender (Must & Anderson, 2006). The preschool’s nurse, who had appropriate training in taking young children’s heights and weights, provided the BMI z-score data to the research team. The nurse obtained each child’s BMI z-score in May, when the child completed other tasks. Each child was weighed on the same scale. Study 1 results Statistical methods As described below, an overall brand knowledge score was calculated after first checking the internal validity of the scale. This analysis was conducted using SPSS reliability analysis. The three research questions were addressed using hierarchical regression analysis, with BMI z-scores as the dependent variable and the predictor variables entered in stepwise fashion (see detail below). This analysis was conducted using the linear regression function in SPSS. Finally, a binary logistic regression analysis was conducted to test the robustness of findings regarding the research questions. The logistic regression analysis was performed in much the same manner as the hierarchical linear regression, except with the dependent variable coded as a 0–1 categorical variable. In this analysis, overweight/ obese was coded as 1 and healthy weight was coded as 0. Hence, the analysis used the predictor variables to predict the likelihood of being overweight. The binary logistic regression analysis was conducted using SPSS. Preliminary data analysis Scores on each of the four collage trials (fast food, soda, chips, cereal) could potentially range from 0 to 18. It should be noted, however, that a zero score would be unlikely since children were expected to have correctly placed some items by chance even if they had minimal knowledge of the brands. In this sample, the range of scores on each trial were as follows: fast food trial scores ranged from 4 to 18 (M = 13.44, SD = 3.54), soda trial scores ranged from 6 to 18 (M = 14.59, SD = 3.58), chip trial scores ranged from 4 to 18 (M = 12.00, SD = 3.38), and cereal trial scores ranged from 4 to 18 (M = 12.00, SD = 3.38). Cronbach’s alpha was calculated to determine whether all four trials could be combined to give an overall scale score for each child’s brand knowledge. The alpha was strong at α = 0.86. Hence, an overall brand knowledge scale score was calculated by averaging across the performance on all four trials for each child. In the current sample, scale scores ranged from 5.5 to 17.5 (M = 13.42, SD = 3.13). Regression predicting BMI z-scores All three research questions were investigated in a regression analysis predicting BMI, using BMI z-scores as the dependent variable. Age and gender were entered at the first step to partial out their influence. On step two, commercial television viewing and brand knowledge were both entered so that they could be examined simultaneously. Finally, physical activity was added on step 3 to determine whether this variable would change any effects that had been observed on step 2. Results of this regression analysis are summarized in Table 1.

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Table 1 Results of Study 1 regression analysis predicting BMI scores from commercial TV viewing, brand knowledge, and physical activity. Step

Variable

B

Std. Error

Beta

t

1

Age Gender Age Gender Brand knowledge Commercial TV viewing Age Gender Brand knowledge Commercial TV viewing Physical activity

−0.01 −0.63 −0.44 −0.51 0.19 −0.02 −0.01 −0.30 0.08 −0.01 −0.71

0.76 0.50 0.76 0.51 0.08 0.06 0.41 0.28 0.05 0.03 0.06

−0.00 −0.16 −0.08 −0.13 0.30 −0.04 −0.00 −0.08 0.13 −0.02 −0.82

−0.02 −1.27 −0.58 −0.99 2.35* −0.31 −0.02 −1.08 1.78 −0.27 −11.96**

2

3

* p < 0.05, ** p < 0.001.

The model was not significant at step 1, when only age and gender were entered, F(61, 2) = 0.84, ns. With the inclusion of commercial television viewing and brand knowledge at step 2, the model was significant, F(2, 59) = 2.90, p < 0.05. Brand knowledge accounted for 8.40% of variance in BMI scores and was significant, t = 2.35, p < 0.05. Commercial television was not significant. Finally, at step three, the inclusion of physical activity made a significant change to the overall model, R2change = 0.63, F(1, 58) = 143.05, p < 0.001. Children who were more frequently engaged in physical activity had significantly lower BMI scores, t = –11.96, p < 0.001. Physical activity accounted for 63.20% of variance in BMI scores in the full model. At this third step, brand knowledge was no longer significant, t = 1.78, p = 0.08. A Sobel test showed that the indirect effect of brand knowledge on BMI was not significantly different from zero when accounting for physical activity as a mediator (z = 1.83, ns). In these findings, brand knowledge predicts BMI scores but is negated when frequent physical activity is subsequently considered. Regression predicting overweight/obesity An additional analysis was conducted to specifically assess the role of each of the predictor variables in determining whether or not a child’s weight placed them in a category of “overweight/ obese.” To conduct this analysis, children whose BMI fell within the percentile range of 5–84 (according to their age and gender) were categorized as “healthy-weight” and those whose BMI was at or above the 85th percentile for their age and gender were categorized as “overweight/obese” (as per Centers for Disease Control and Prevention 2014). Data from four underweight children were excluded. Binary logistic regression was conducted with weight category (healthy-weight vs overweight/obese) as the dependent variable. The predictor variables were entered in the same stepwise fashion as for the hierarchical linear regression outlined above. Overall, a majority of participants (56.9%) were found to be healthy-weight with regard to weight category (n = 37). Logistic regression analysis was employed to predict the probability that a participant would be overweight/obese. At Block 0 (with none of the predictors entered), the model was able to correctly classify 56.9% of cases. Block 1, when only age and gender were entered, was not significant. The addition of commercial television viewing and brand knowledge did not result in a significant change to the overall model in Block 2, however, the odds ratio for brand knowledge was significant. This odds ratio showed that, when holding other variables constant, a child who was familiar with the brands was 1.20 times more likely to be overweight/obese. This effect was, however, eliminated by the entry of physical activity in Block 3. In Block 3, a test of the full model was significant, χ2(5, N = 65) = 35.77, p < 0.001. The model was able to correctly clas-

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Table 2 Results of Study 1 overall model of the logistic regression analysis predicting overweight/obesity from commercial TV viewing, brand knowledge, and physical activity. Variable Block 2 Model Age Gender Brand knowledge Commercial TV viewing Block 3 Model Age Gender Brand knowledge Commercial TV viewing Physical activity

B

Wald χ2

p

Odds Ratio

−0.36 −0.63 0.18 −0.02

0.19 1.23 3.86 0.06

0.66 0.27 0.04 0.80

0.70 0.53 1.20 0.98

0.15 −0.79 0.11 0.00 −0.87

0.02 1.10 0.64 0.00 16.84

0.88 0.30 0.42 0.98 0.00

1.16 0.45 1.12 1.00 0.42

sify 83.1% of cases. Table 2 shows the regression coefficient, Wald test, and odds ratio for each of the predictors in the full model. Physical activity was the only predictor to show a significant effect in the full model. The inverted odds ratio for physical activity indicated that children with lower levels of physical activity were 2.38 times more likely to be overweight/obese.

Study 2 method Participants Participants were 75 children (40 boys, 35 girls) aged three years six months to six years one month (M = 4 years 6 months, SD = 6 months), as well as one parent of each child. The sample was drawn was from the same preschool and was again, diverse (30% Caucasian, 28% African American, 15% Multi-ethnic, 11% Hispanic, 7% Asian, 9% Arab American, and 1% other) and similar procedures were followed as in Study 1. Responses were obtained from five fathers and 70 mothers who were aged between 21 and 55 (M = 32, SD = 7). The parent survey used in this study was identical to the Study 1 parent survey.

Brand knowledge collage task As in Study 1, McAlister and Cornwell’s (2010) brand knowledge collage task was used for this study. Consistent with McAlister and Cornwell (2010), McDonald’s and Burger King were used for the fast-food trial, as well as Coca Cola and Pepsi for the soda brand trials. Similar to Study 1, two trials were added. Again, the goal was to increase the generalizability of the findings by including other brands but to also argue that capturing a portion of one’s profile in terms packaged and fast foods can serve as a surrogate indicator of an overall profile. That is to say, extensive knowledge of a subset of these products was an indicator for a profile of exposure and consumption much the same way a sample of language abilities could tell an educator about a child’s language development. Thus, in this second study, the additional trials were for candy (M&Ms, Jelly Belly) and cereal (Froot Loops, Fruity Pebbles). Scoring and other procedures were as in Study 1.

Body mass index (BMI) As per Study 1, age-appropriate, gender-specific BMI scores (i.e., BMI z-scores) were recorded for all participating children and provided to the research team by the preschool’s nurse. These data were collected in the month of May, when the child completed other tasks.

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Table 3 Results of Study 2 regression analysis predicting BMI scores from commercial TV viewing, brand knowledge, and physical activity.

Table 4 Results of Study 2 model of the logistic regression analysis predicting overweight/ obesity from commercial TV viewing, brand knowledge, and physical activity.

Step

Variable

B

Std. Error

Beta

t

Variable

1

Age Gender Age Gender Brand knowledge Commercial TV viewing Age Gender Brand knowledge Commercial TV viewing Physical activity

−0.18 −0.67 −0.95 −0.45 0.26 −0.01 −0.72 −0.51 0.20 −0.02 0.21

0.50 0.57 0.54 0.56 0.10 0.05 0.62 0.57 0.12 0.05 0.28

−0.06 −0.19 −0.30 −0.13 0.48 −0.03 −0.23 −0.14 0.38 −0.05 0.15

−0.35 −1.17 −1.75 −0.81 2.75** −0.19 −1.15 −0.89 1.67 −0.29 0.78

Block 2 model Age Gender Brand knowledge Commercial TV viewing Block 3 model Age Gender Brand knowledge Commercial TV viewing Physical activity

2

3

B

Wald χ2

p

Odds ratio

−0.44 −0.59 0.29 −0.07

0.52 1.06 5.85 0.85

0.47 0.30 0.02 0.34

0.65 0.55 1.34 0.93

−0.47 −0.54 0.30 −0.06 −0.13

0.59 0.87 6.05 0.65 0.26

0.44 0.35 0.01 0.42 0.61

0.62 0.58 1.35 0.94 0.88

** p < 0.001.

Study 2 results Preliminary data analysis Scores on each of the four collage trials (fast food, soda, candy, cereal) could range from 0 to 18. In this sample, the range of scores on each trial were as follows: fast food trial scores ranged from 4 to 18 (M = 13.05, SD = 3.94), soda trial, 5 to 18 (M = 13.93, SD = 3.92), candy trial, 5 to 18 (M = 13.59, SD = 3.55), and cereal trial, 4 to 18 (M = 13.02, SD = 3.94). Cronbach’s alpha was calculated to determine whether all four trials could be combined to give an overall scale score for each child’s brand knowledge. The alpha was strong at α = 0.84. Hence, an overall brand knowledge scale score was calculated by averaging across the performance on all four trials for each child. In the current sample, scale scores ranged from 6 to 18 (M = 13.40, SD = 3.16). Regression predicting BMI z-scores As per Study 1, all three research questions were investigated in a regression analysis predicting BMI. The three-step model employed in this study was identical to that used in Study 1 (age and gender entered at step 1, commercial television viewing and brand knowledge entered at step 2, physical activity entered at step 3). Table 3 provides a summary of the regression results. The model was not significant at step 1, when only age and gender were entered, F(38, 2) = 0.80, ns. With the inclusion of commercial television viewing and brand knowledge at step 2, the model was significant, F(2, 36) = 3.96, p < 0.05. Brand knowledge accounted for 16.48% of variance in BMI scores and was significant, t = 2.75, p < 0.01. Commercial television was not significant. Finally, at step three, the inclusion of physical activity made no significant change to the overall model, R2change = 0.01, F(1, 35) = 0.57, ns. In this study, the role played by brand knowledge in influencing child BMI was replicated but the role of physical activity in countering this influence was not. Regression predicting overweight/obesity Consistent with Study 1, a binary logistic regression analysis was conducted to assess the role of each of the predictor variables in determining a child’s likelihood of being overweight/obese. Data from six underweight children were excluded. The predictor variables were entered in the same stepwise fashion as for the hierarchical linear regression outlined above. Overall, a majority of participants (68.1%) were found to be healthy with regard to weight category (n = 47). At Block 0 (with none of the predictors entered), the model was able to correctly classify 68.1% of cases. Block 1 was not significant when only age and gender were entered. In Block 2, the addition of commercial tele-

vision viewing and brand knowledge resulted in a significant change to the model χ2(4, N = 69) = 10.23, p < 0.05. The ability of the model to correctly classify cases increased to 71.0% with the addition of these two predictors. In this second block, the odds ratio for brand knowledge was significant and showed that, when holding other variables constant, a child who was familiar with the brands was 1.34 times more likely to be overweight/obese. In Block 3, a test of the full model was not significant, χ2(5, N = 69) = 10.49, ns. The ability of the model to correctly classify cases was weaker with the addition of physical activity. Table 4 shows the regression coefficient, Wald test, and odds ratio for each of the predictors in the full model, compared to the model at Block 2. Brand knowledge was the only predictor to show a significant effect in the full model. Discussion Across two studies, a child’s brand knowledge was shown to be a significant predictor of their BMI, even after controlling for age and gender and when also considering the extent of their TV viewing. Additionally, the two different collage measures performed similarly, which suggests that this measure may serve as a surrogate indicator of an overall pattern of product exposure and consumption. The success of physical activity to counter the influence of brand knowledge on BMI in the first study, but the failure to replicate this finding in the second study, suggested that exercise was not a robust predictor of child BMI. That is to say that while exercise can be a powerful explanatory variable, the ability of exercise to mitigate other factors that contribute to unwanted weight is questioned. This finding was consistent with prior findings that physical activity may not be sufficient to reduce BMI in children (Hinkley et al., 2008; Reily et al., 2006). Reflection on Allen’s (2012) theory of food, suggested that – just as we can test for the development of language, or the development of theory of mind – we may be on the path to developing a test of one’s theory of food. There has been evidence for some time now to support the notion that food preference develops early (Mennella, Griffin, & Beaucahmp, 2004) and that it is largely determined by repeated exposure. Further, it can also be said with some confidence that dietary selection and consumption of fat (see Mela, 1995 for a review), sugar (Sullivan & Birch, 1990) and salt (Beauchamp, Bertino, & Engelman, 1983; Sullivan & Birch, 1990) tends to lead to increased preferences for these tastes. It is important to understand how early food preferences are set because taste has been argued to be the most important aspect of food choice in school-aged children (Bergstrom, Brembeck, Jonsson, & Shanahan, 2012). Past research did not consider the compendium of exposures that leads to unhealthy dietary patterns and the persistence of these patterns. The current study found that children with a developed knowledge for fast food and packaged food tended to have

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a higher BMI at an early age. These findings suggest that policy to address this problem needs to focus on marketplace product offerings in addition to marketplace communications. In an effort to examine the link between taste and weight, Donaldson, Bennett, Baic, and Melichar (2009) reviewed the literature on the ability to taste different tastes or taste thresholds and body mass. They found the literature to be sparse and contradictory. One of the questions the current study raises but does not answer is the extent to which early exposure sets patterns for many future obese adults thereby lowering the variance in taste thresholds. If this were the case, then desensitization to the taste of sugar, salt and fat might be a first step in the reduction of preference and intake of products containing high levels of taste-makers. Fortunately, research has also found that reduction in regular exposure to fat (Ledikwe et al., 2007) was followed by reduction in preference for dietary fat and reduction of sugar-sweetened drinks is immediately influential on consumption of raw vegetables (Cornwell & McAlister 2013). If the types of foods examined in the collages, which were known to be associated with the development of child taste preference (Cornwell & McAlister, 2011), were limited by parents and caregivers, their reduction might be associated with a reduction in BMI, not per se, from reduced caloric intake but from reduced preference for foods that constitute an unhealthy dietary pattern. It is important that parents and caregivers understand the powerful link between child food consumption patterns and BMI and patterns in adulthood. Community education is needed to clearly explain how patterns of behavior are developed and persist. References Allen, J. S. (2012). ‘Theory of food’ as a neurocognitive adaptation. American Journal of Human Biology, 24, 123–129. Anderson, S. E., & Whitaker, R. C. (2010). Household Routines and Obesity in US Preschool-Aged Children, Pediatrics, 125, 420–428. Arredondo, E., Castaneda, D., Elder, J. P., Slymen, D., & Dozier, D. (2009). Brand name logo recognition of fast food and healthy food among children. Journal of Community Health, 34, 73–78. Beauchamp, G. K., Bertino, M., & Engelman, K. (1983). Modification of salt taste. Annals of Internal Medicine, 98(Pt. 2), 763–769. Bergstrom, K., Brembeck, H., Jonsson, L., & Shanahan, H. (2012). Children and taste. Guiding foodservice. Journal of Foodservice Business Research, 15, 84–100. Bernhardt, A. M., Wilking, C., Adachi-Mejia, A. M., Bergamini, E., Marijnissen, J., & Sargent, J. D. (2013). How television fast food marketing aimed at children compares with adult advertisements. PLoS ONE. Available at: . Borzekowski, D. L. G., & Robinson, T. N. (2001). The 30-second effect. An experiment revealing the impact of television commercials on food preferences of preschoolers. Journal of the American Dietetic Association, 101(1), 42–46. Boyland, E., Harrold, J. A., Kirkham, T. C., Corker, C., Cuddy, J., Evans, D., et al. (2011). Food commercials increase preference for energy-dense foods, particularly in children who watch more television. Pediatrics, 128, e93. doi:10.1542/peds.20101859. Centers for Disease Control and Prevention (2013). Vital signs. Obesity among low-income, preschool-aged children – United States, 2008–2011. Morbidity and Mortality Weekly Report, 62(31), 629–634. Centers for Disease Control and Prevention (2014), Body mass index. Considerations for practitioners, http://www.cdc.gov/obesity/downloads/bmiforpactitioners.pdf. Cornwell, T. B., & McAlister, A. R. (2011). Alternative thinking about starting points of obesity. Development of child taste preference. Appetite, 56, 428–439.

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