The purpose of this study was to investigate objective performance on a nutrition label comprehension task and the influence of numeracy and food-related involvement on this performance level. A pilot study (n = 45) was run to prepare the scales in French. For the main study (n = 101), participants provided demographic in- formation and answered the nutrition label survey, the short numeracy scale and two different food-related involvement scales (i.e. the food involvement scale and the nutrition involvement scale). Both studies were conducted online, and consent was obtained from all participants. Participants answered correctly only two- thirds of the nutrition label task items. Numeracy and food involvement scores were positively correlated with performance on this task. Finally, food involvement interacted with numeracy. Specifically, people scoring low in numeracy performed generally more poorly on the task, but if they had high food involvement scores, their performance increased. This suggests that high food-related motivation may compensate for poor numeracy skills when dealing with nutrition labels.
The prevalence of obesity is continuously increasing worldwide, despite a heightened awareness of the matter. Many factors contribute to growing obesity rates, such as food marketing, the tendency towards sedentary behavior in everyday life, as well as increased consumption of processed foods (French, Story, & Jeffery, 2001; Popkin, 1998; Stuckler, McKee, Ebrahim, & Basu, 2012; Swinburn, Caterson, Seidell, & James, 2004). Overweight and obesity are largely preventable. Making healthier food choices and ensuring regular physical activity are the easiest (i.e., most accessible, available and affordable) ways to prevent overweight and obesity (World Health Organisation, 2016).
Nutrition information labeling on foods is one of the aids that has been implemented to help people make more appropriate judgments about certain aspects of a food, such as its caloric content, portion in- formation (Cowburn & Stockley, 2005) and therefore its healthiness. However, it is unclear how well consumers process nutritional in- formation, and which individual differences may impact on their per- formance in using it. In particular, it is unclear how food-related in- volvement and numeracy may impact consumer performance with nutrition labels.
The present research contributes to this general question by in- vestigating the role of food-related involvement, numeracy and their interactive effects on performance with food-related estimates and comparisons of nutrition labels. Numeracy relates to general capacities in dealing with numeric computations. Food-related involvement is measured with two separate scales, tapping into (i) specific nutritional interest and (ii) more general interest in food, respectively. These various concepts and how we expect them to relate to performance in dealing with nutrition labels information are explained in the introduction.
Regulations for nutrition labeling exist globally in, e.g., the United States, Canada, Australia and Europe. The new European Regulation on food information given to consumers (No 1169/2011) was accepted on December 13, 2014 and has made it mandatory to provide nutrition information as of December 13, 2016. Nutrition labeling is aimed at informing consumers about the nutrient content of a food (e.g., amount of sugar, fat or calories), as well as information on possible allergens, engineered nanomaterials, and origin of some foods (e.g., for certain meats). This information, together with basic knowledge about what constitutes a healthy food choice, is intended to help individuals make informed food purchase decisions.
Previous research has reported associations between food labeling and food choice, although different consumers reported using different types of labels (Barreiro-Hurlé, Gracia, & De-Magistris, 2009). Because most adults encounter food labels while purchasing or preparing their food, the potential impact of these labels for public health may be substantial (French et al., 2001). The latter conclusion is valid, however, only if consumers are able to process this information accurately
(Grunert, Celemín, Storcksdieck genannt Bonsmann, & Wills, 2012).
To date, however, it remains unclear how capable consumers are of using the information found in a nutrition information table, and more specifically how well they perform on tasks requiring use of this information, e.g., when comparing products or computing the caloric content of a food portion. This is because most studies investigating nutrition labeling used subjective measures of understanding, and frequency of use (see Cowburn & Stockley, 2005; and Grunert & Wills, 2007 for two reviews). Self-reports likely overestimate both frequency and performance in using nutrition labels (Grunert & Wills, 2007).
Two studies that used a more objective method (i.e., a verbal pro- tocol analysis) suggested that consumers might simply look at the nu- trition information panel but not process the information any deeper (Cowburn & Stockley, 2005). The first study that used objective mea- sures of food label use and comprehension, examined primary care
patients’ comprehension of food labels, along with the relationship
between comprehension and their literacy and numeracy skills (Rothman et al., 2006). On average, patients in this study answered 69% of the food-label questions correctly, and label comprehension was strongly associated with low-level literacy and numeracy skills. How- ever, this study was carried out amongst a patient sample, and it has not been replicated. The second study that used objective measures of food label use and comprehension (Malam et al., 2009) focused on the front of package labeling (i.e., traffic light labeling, which is not used everywhere) and not on the mandatory nutrition labeling. Therefore, it is important to provide further information on how people perform on such tasks and to uncover which factors may impact performance. The first aim of this study is to investigate how consumers from a non-pa- tient sample perform on a label task.
Several studies have shown that consumers have difficulty using the information that is presented on nutrition labels (e.g., Huizinga et al., 2009; Rothman et al., 2006). The most common reasons for not using nutrition labels include lack of time, size of print, lack of understanding of terms, and concerns about the accuracy of the information (Cowburn & Stockley, 2005). More critical to the present research endeavor, the complexity of nutrition labeling and problems with technical terms, numerical calculations and percentages seem to be correlated with problems regarding understanding nutrition information (Grunert & Wills, 2007). Numeracy is particularly relevant in this regard.
Numeracy encompasses a multitude of skills ranging from basic calculations to understanding measurements and estimations, and even inferring what mathematical concepts need to be used to solve a problem (Golbeck, Ahlers-Schmidt, Paschal, & Dismuke, 2005; Montori & Rothman, 2005). In other words, numeracy embodies one's ability to use and understand numbers in daily life, and so it may play an important role in reading and understanding nutrition labels (Rothman et al., 2006). This is because one must be able to make simple calculations with nutrition labels in order to understand key concepts such as the number of calories per serving or per amount consumed (Grunert & Wills, 2007).
Rothman et al. (2006) were among the first to show that one's understanding of nutrition labels is indeed highly correlated with nu- meracy. Of course, this may be confounded with the influence of educational level, as they report that those participants with lower numeracy scores were older, and had lower educational levels. In the sample of Rothman et al. (2006), the numeracy skills for 63% of their participants were very low: less than 9th-grade level.
In a study by Levy and Fein (1998) it was found that math complexity, little nutrition label use, age, and lower education levels all decreased the accuracy of calculations using nutrition labeling. In this study, participants accurately used nutrition labeling to perform simple calculations, but accuracy decreased as task complexity increased.
Three more recent studies also showed that numeracy facilitates
label use. Firstly, Hess, Visschers, and Siegrist (2012) showed that nu- meracy is positively associated with label use, but this was only as- sessed with a self-report questionnaire. Secondly, Visschers and Siegrist (2010) showed that individuals with low numeracy skills make dif- ferent food choices compared to those with high numeracy skills, de- pending on task complexity. However, these results are based on a choice task between equivalent products with different properties (e.g., non-fat vs. low fat yoghurt). This task does not involve comparing different products (e.g., bread vs. croissants), nor does it require cal- culations related to other food dimensions (e.g., sugar or calorie con- tent). Finally, Miller, Applegate, Beckett, Wilson, and Gibson (2017) also found a correlation between numeracy and nutrition label use: Regardless of age, greater nutrition knowledge and higher numeracy skills were associated with more accuracy in using nutrition labels.
In their recent review, Malloy-Weir and Cooper (2017) concluded
that empirical relationships between, amongst others, numeracy and nutrition label understanding and use have been understudied and are also often limited by the use of self-report data. We aim here to address the latter issue by examining the influence of numeracy on objective rather than self-reported performance in using nutrition labels. Con- sistent with prior work, we hypothesized that low numeracy individuals with low numeracy skills would perform more poorly on food label- related questions.
Besides numeracy, a second factor of interest that may play a key role in performance on food label use is involvement. In the present research, the behavioral form of involvement was considered, using the definition proposed by Stone (1984): “Involvement shall be defined as time and/or intensity of effort expended in the undertaking of beha- viors” (p. 210). Involvement is multidimensional and can be considered
a type of motivation (Broderick & Meuller, 1999; Laurent & Kapferer,
1985; Marshall & Bell, 2004).
The elaboration likelihood model of persuasion (ELM) provides a framework for understanding the influence of involvement on food label processing (Cacioppo & Petty, 1984). According to the ELM, consumers who are highly involved and have the ability to process information base their product evaluations on intrinsic cues (i.e., the nutrition label). Conversely, when consumers have low involvement and/or their ability to process information is limited, their product evaluations are based on extrinsic cues (Walters & Long, 2012). This suggests that individuals who are highly involved in their food choices should base their product evaluation on intrinsic cues such as the nu- trition table, resulting in increased use of nutrition information and increased performance when using nutrition information, which also requires more nutrition knowledge to interpret.
It is important, however, to clearly distinguish between nutritional
knowledge and food-related involvement since their overlap is high and the distinction between them in the literature is occasionally ambig- uous. Below we discuss the differences between these constructs. We first discuss nutrition knowledge and its relation to motivation. We then turn to two types of food-related involvement: food involvement and nutrition involvement.
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