Briefing Book for Prof Tutor Only**
British Food Journal Soft drinks for lunch? Self-control, intentions and social influences Elisabeth Lind Melbye, Merete Hagen Helland,
Article information: To cite this document: Elisabeth Lind Melbye, Merete Hagen Helland, (2018) "Soft drinks for lunch? Self-control, intentions and social influences", British Food Journal, Vol. 120 Issue: 8, pp.1735-1748, https://doi.org/10.1108/ BFJ-11-2017-0605 Permanent link to this document: https://doi.org/10.1108/BFJ-11-2017-0605
Downloaded on: 01 November 2018, At: 06:28 (PT) References: this document contains references to 69 other documents. To copy this document: permissions@emeraldinsight.com The fulltext of this document has been downloaded 96 times since 2018*
Users who downloaded this article also downloaded: (2018),"Exploring the visual appeal of food guide graphics: A compositional analysis of dinner plate models", British Food Journal, Vol. 120 Iss 8 pp. 1682-1695 <a href="https://doi.org/10.1108/ BFJ-02-2018-0112">https://doi.org/10.1108/BFJ-02-2018-0112</a> (2018),"Alternative food networks: sustainable business models for anti-consumption food cultures", British Food Journal, Vol. 120 Iss 8 pp. 1776-1791 <a href="https://doi.org/10.1108/ BFJ-12-2017-0731">https://doi.org/10.1108/BFJ-12-2017-0731</a>
Access to this document was granted through an Emerald subscription provided by emerald- srm:464943 []
For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
*Related content and download information correct at time of download.
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Soft drinks for lunch? Self-control, intentions and
social influences Elisabeth Lind Melbye
Norwegian School of Hotel Management, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway, and
Merete Hagen Helland Department of Education and Sports Science, Faculty of Arts and Education,
University of Stavanger, Stavanger, Norway
Abstract Purpose – The purpose of this paper is to explore associations between food-related self-control, intentions, descriptive peer norms, parents’ healthy eating guidance and adolescents’ consumption of sugar-sweetened beverages (SSB) in a school lunch setting. An additional aim was to evaluate the psychometric properties of the measure used to assess food-related self-control in order to reveal potential multi-dimensionality. Design/methodology/approach – A web-based survey was conducted among 694 Norwegian high school students. Multiple logistic regression was used to explore associations between the independent variables and SSB consumption. Psychometric evaluation of the self-control measure included factor analysis and internal consistency reliability. Findings – Factor analysis resulted in two food-related self-control dimensions: resistance and avoidance. Multiple logistic regression showed that intentions was the strongest predictor of SSB consumption in the sample. Avoidance and descriptive peer norms appeared as weaker predictors. Research limitations/implications – Based on the findings, the authors suggest that future studies may consider developing guiding principles on how to create health-promoting eating intentions in adolescents, how to deal with peer norms related to foods and beverages and how to avoid tempting stimuli in the environment. Such strategies may be helpful when structural changes in the environment are not feasible in the near future. Originality/value – An original aspect of the present study is that it includes a psychometric analysis of a supposedly one-dimensional self-control measure. Further, it adds to the knowledge about variables associated with adolescent SSB consumption in a school lunch context. Keywords Intentions, Adolescents, Social influence, Soft drinks, Self-control Paper type Research paper
Introduction A low intake of foods and beverages with added sugar is recommended by national and international health authorities (Nordic Council of Ministers, 2014; WHO, 2003). In Norway, which is the setting of the present study, a recent decline in the consumption of sugar-sweetened beverages (SSB) has been reported. Still, Norwegian adolescents have higher intakes than recommended (Fismen et al., 2016). According to a national survey among high schools (n ¼ 447) conducted by the Norwegian Directorate of Health (2013), the share of high school students drinking SSB with lunch on a daily basis is estimated to be more than 20 per cent. The majority (92 per cent) of the surveyed schools reported that they have a school cafeteria or other facilities were students can buy various foods and beverages, and that approximately 60 per cent of these facilities offer sugar-sweetened soft drinks, while 80 per cent offer milkshake/chocolate milk and iced coffee (of which most are sweetened with sugar) on a daily basis. Furthermore, students at most schools (96 per cent) have easy access to local shops and restaurants offering a wide variety of unhealthy foods and beverages. The survey also revealed that schools find it challenging to offer foods and beverages that are in accordance with national nutritional advice when having to compete with neighbouring facilities. With this easy access to SSB, both at school and in the school
British Food Journal Vol. 120 No. 8, 2018
pp. 1735-1748 © Emerald Publishing Limited
0007-070X DOI 10.1108/BFJ-11-2017-0605
Received 3 November 2017 Revised 24 April 2018
6 June 2018 Accepted 6 June 2018
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0007-070X.htm
1735
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
neighbourhood, it seems important to reveal other key influences on adolescents’ SSB consumption during school time.
Several studies have explored correlates of soft drink consumption in adolescence, and environmental factors such as availability and accessibility have consistently been identified as important predictors (De Coen et al., 2012; Gebremariam et al., 2016; Verloigne et al., 2012). Also family rules, food-related lifestyle, parenting styles and parenting practices have been found to be associated with SSB consumption in adolescents (Gebremariam et al., 2016; van der Horst et al., 2007; Verzeletti et al., 2009). Although parents and the home environment appear as important predictors of adolescents’ soft drink intake, their influence has to compete with the influence of friends, peers at school and media (Chan et al., 2012). Furthermore, eating behaviours seem (like many other types of behaviour) to be linked to the construction and expression of identity both at a personal and a social level (Bisogni et al., 2002; Vartanian et al., 2007), and particularly during adolescence, they may fulfil a function of self-expression (Guidetti and Cavazza, 2008). Apart from extrinsic (social) factors like parent and peer influences, also intrinsic (personal) factors such as the levels of self-control and impulsivity have been shown to correlate with adolescents’ consumption of SSB (Melbye et al., 2016) and other unhealthy foods (Honkanen et al., 2012). Since extrinsic and intrinsic factors are hypothesised to act in conjunction to influence human behaviour (Fox and Calkins, 2003; Ryan and Deci, 2000), they should not be seen in isolation from each other. Thus, it appears relevant to lean on elements from well-established socio-cognitive models such as Bandura’s (1977) social learning theory (SLT) and Ajzen’s (1991) theory of planned behaviour (TPB), as well as aspects of self-control theories, when exploring correlates of adolescents’ SSB consumption. While the socio-cognitive theories have their primary focus on extrinsic factors such as the context of social interactions, experiences and outside influences, self-control theories also include intrinsic factors such as temperament and inhibitory control (Vohs and Baumeister, 2016).
Based on the discussion above, the following variables were included in the current study: food-related self-control, intentions to consume SSB with school lunch, perceived descriptive peer norms related to SSB consumption with school lunch and perceived parental healthy eating guidance (HEG). Each of these variables is further described below.
Food-related self-control Individual differences in self-control have been associated with dietary behaviours in youth, whereby individuals with high levels of self-control exhibit healthier eating habits than those with lower levels of self-control (Junger and van Kampen, 2010; Luszczynska et al., 2013; Stok et al., 2015). Generally, self-control is described as the ability to resist temptations and impulses. However, the ability to resist temptations and impulses seems to depend on an individual’s overall self-controlling capacity, which has been shown to fluctuate across time (Baumeister et al., 1994). According to Baumeister et al.’s (2007) strength model of self-control, the fluctuation of self-controlling capacity may be caused by depletion of willpower (defined as a type of strength or energy), which is regarded as a limited resource. With other words: self-control appears vulnerable to deterioration over time from repeated efforts, like a muscle that gets tired. Thus, according to this model, if people rely on their willpower alone, they are likely to fail repeatedly because some temptations will inevitably turn up when one’s level of resistance is low (i.e. the muscle is tired). Interestingly, recent research suggests that self-control is also linked to avoiding temptations (Ent et al., 2015). Avoiding temptations may prevent instances of self-control failure caused by depleted willpower. Hence, avoidance appears as an important element of effective self-control. Yet, widely used measures of self-control such as Tangney et al. (2004) 36-item self-control scale, and the corresponding 13-item brief self-control scale (BSCS), do not explicitly distinguish between resistance and avoidance dimensions. Rather, they tend to include items reflecting
1736
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
both resistance and avoidance (and other possible self-control dimensions) in calculating one total self-control score, thus presuming a unidimensional construct. According to Maloney et al. (2012), there is a general lack of well-validated measures of self-control, and despite the widespread use of Tangney et al.’s BSCS, questions about its unidimensionality and validity still remain. Maloney et al. (2012) have addressed these concerns, and their study is one of very few examining the dimensionality of the BSCS. Results from their analyses failed to support a one-factor structure, indicating that self-control is a construct with various facets. Thus, it seems important to further explore the dimensionality of this and other self-control measures.
Intentions According to traditional socio-cognitive models, such as Ajzen’s (1991) TPB, intention is the immediate precursor of behaviour. The TPB proposes that intentions result from the joint impact of attitudes, subjective norms and perceived behavioural control. According to Gollwitzer (1996), however, intentions are the end result of the deliberations of wishes and desires in the pre-decisional phase of decision making. Thus, wishes and desires are seen as important precursors of intention. There are several possible motivations underlying adolescents’ intentions to consume SSB with school lunch. Among them are hedonic motives and impression-management motives such as appearing autonomous (i.e. self-directed or independent) (Stok et al., 2010), “fitting in” or being popular among peers (De la Haye et al., 2010).
Descriptive peer norms For adolescents, the consumption of specific food products may help them create and present a desired identity and to express friendship (Stead et al., 2011). As a result, individual snack and soft drink consumption is shown to be higher when friends and peers have a high consumption (Wouters et al., 2010). Significant peer effects have also been found for the frequency of eating at fast food restaurants, suggesting that an individual is more likely to engage in these behaviours if his or her friends do (Ali et al., 2011). Further, a review by Salvy et al. (2012) reports that peers’ (actual) intake of snacks has been found to be a predictor of youths’ snack consumption. Interestingly, perceived peer behaviour (i.e. descriptive norms) is also shown to be important, and in some cases even more strongly associated with adolescents’ intake than the actual behaviour of the members of the peer group (Perkins et al., 2010). According to Lally et al. (2011), adolescents have a tendency to overestimate their peers’ intake of snacks and soft drinks and underestimate their consumption of fruits and vegetables. Furthermore, adolescents often feel a significant social pressure from their peers, and presenting a positive social profile is important to them (Stevenson et al., 2007). Accordingly, influence from peers seems as an important element for understanding adolescents’ SSB consumption.
Parents’ HEG A variety of food-related parenting practices are used to influence children and adolescents’ diet (Birch and Davidson, 2001; Blissett, 2011; Patrick and Nicklas, 2005). Previous studies have indicated that overly strict practices may have adverse effects on eating behaviours by increasing the consumption of unhealthy foods. For younger children, it is suggested that this may be the result of an increased appeal of the restricted foods that lead to over-consumption in situations where restrictions are removed (Birch et al., 2003; Fisher and Birch, 1999). For adolescents, it may represent an opposition to parental control, indicating an increased need for autonomy and self-presentation (Melbye et al., 2016; Parkin and Kuczynski, 2012). However, parenting practices involving positively framed eating
1737
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
guidance are shown to be associated with increased consumption of healthy food items and decreased consumption of unhealthy ones (Melbye and Hansen, 2015).
The aim of the present research was to explore the impact of relevant intrinsic and extrinsic factors on adolescents’ consumption of SSB with school lunch. More specifically, and based on the preceding literature review, we aimed to reveal the unique contribution of adolescents’ food-related self-control, behavioural intentions, perceived parent HEG and perceived descriptive peer norms in explaining the variance in SSB consumption in a school lunch context. An additional aim was to evaluate the psychometric properties of the self-control measure applied in the current study in order to reveal potential multi-dimensionality (i.e. resistance and avoidance dimensions).
Materials and methods Participants and procedures The first draft of the survey questionnaire was pre-tested by 15 second grade high school students from different schools in Rogaland. These students were recruited from the authors’ network (i.e. colleagues’ and friends’ children) and did not take part in the main survey. The pre-test was conducted in two steps. Initially, four students were asked to answer the questions orally, and to discuss all questions with an interviewer (one of the authors). Revisions were made according to their comments. Next, the questionnaire was pre-tested for clarity and length among 11 (other) students. The students filled in the questionnaire at home and sent it back to the researchers with comments. Based on these comments, a few minor revisions were made before the final draft was ready. The Norwegian Centre for Research Data, which is the Norwegian data protection officer for all Norwegian universities and several hospitals and research institutes, was notified about the study and approval was given before data collection started.
All high schools (n ¼ 25) in Rogaland County, Norway, were invited to participate in the web-based survey. Four schools situated in different parts of the county agreed to participate (two schools close to the city centre and two rural schools). The response rates among eligible first and second grade students at these schools were 14, 36, 68 and 72 per cent, giving 694 completed questionnaires. The school administration gave consent for their students’ participation in the survey, and participants were recruited through an invitation on the school website, with a link to the survey. Students were given information about the study, that participation was voluntary and anonymous and that completing the survey questionnaire implied their consent to participate. The survey was conducted using the EyeQuestion software version 3.15.3 (Logic8 BV, Wageningen, The Netherlands). The two schools with the highest response rates allowed the students to answer the survey during class (which was highly recommended by the investigators). If the survey was not finished in one session, it was possible for the respondent to return to the link and finish it at a later time. However, it was only possible to take it once. Students who missed answering some questions (n ¼ 142) were excluded from data analyses. The population of interest for this study was late adolescents (i.e. 16–19 years old), thus respondents younger than 16 and older than 19 years were excluded (n ¼ 20). The mean age in our sample was 17.1 years (SD ¼ 0.8), 67 per cent of the respondents were female and 94 per cent were of Norwegian or other Nordic origin.
Measures Consumption of SSB with school lunch was measured in days per week. Participants were asked to report their usual intake: “How many days per week do you normally consume SSB as part of your school lunch?” (SSB was defined as sugar-sweetened drinks such as carbonated soft drinks, lemonade, iced tea, sweet coffee drinks, energy drinks, milkshake/ chocolate flavoured milk, etc.). Diet drinks (i.e. drinks that do not contain sugar) were not
1738
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
included in this measure. Since all Norwegian schools are closed during weekends, response alternatives ranged from 0 (never) to 5 days (every school day) per week. The variable was coded as follows: never ¼ 0, seldom¼ 0.5, one day per week¼ 1, two days per week¼ 2, three days per week¼ 3, four days per week¼ 4, five days per week¼ 5. SSB consumption with school lunch was later dichotomized into two categories to separate between students who do not (never or seldom) drink and students who drink SSB with school lunch: never/seldom ( ¼ 1) and 1 to 5 days per week ( ¼ 2).
Food-related self-control was measured by five items taken from Tangney et al.’s (2004) BSCS and made related to food by Honkanen et al. (2012): “I have a hard time breaking bad food habits”, “I wish I had more self-discipline when it comes to unhealthy food”, “Sometimes I can’t stop myself from eating unhealthy food, even if I know it’s wrong”, “I avoid food that is not good for me” and “People would say that I have good self-discipline when it comes to unhealthy food”. The first three items were reverse-coded so that higher numbers indicate stronger self-control.
Contemporary measurement research has suggested that for doubly concrete constructs, i.e., constructs that consist of concrete singular matters and concrete aspects related to these matters (here: SSB consumption with school lunch and intentions and descriptive peer norms related to SSB consumption with school lunch), tailor-made single-item measures may be advantageous (Ang and Eisend, 2017; Bergkvist, 2015; Bergkvist and Rossiter, 2007). Thus, adolescents’ intentions and (perceived) descriptive peer norms related to SSB consumption with school lunch were measured by one tailor-made item each: “I wish to drink SSB with my school lunch every day” and “My friends often drink SSB as part of their school lunch”.
Adolescents perceptions of their parents’ HEG were measured by an adapted version of Haszard et al.’s (2013) seven-item HEG scale, which is one of five subscales resulting from factor analysis on the Comprehensive Feeding Practices Questionnaire originally developed by Musher-Eizenman and Holub (2007). This scale has previously been adapted to a Norwegian context in Bjelland et al.’s (2014) research on family processes and dietary habits among Norwegian 13–15 years old and their parents, and further adapted to reflect adolescents’ perceptions of parents’ HEG in the present study. The HEG items read: “Most of the food my parents keep in the house is healthy”, “My parents encourage me to try new foods”, “My parents discuss with me why it is important to eat healthy foods”, “My parents tell me that healthy food tastes good”, “My parents discuss with me the nutritional value of foods”, “ My parents encourage me to eat a variety of foods”, “My parents model healthy eating for me by eating healthy foods themselves”, “My parents try to show enthusiasm when eating healthy foods” and “My parents show me how much they enjoy eating healthy foods”. All items measuring self-control, intentions, descriptive peer norms and HEG were scored on a five-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree).
Participants were also asked to report their age in years, their gender (1 ¼ female, 2 ¼ male), their ethnicity (1 ¼ Norwegian/Nordic origin, 2 ¼ all other origins) and the highest completed level of education for their mothers and fathers (1 ¼ elementary school, 2 ¼ high school, 3 ¼ up to 4 years of college/university, 4 ¼ more than 4 years of college/university, 5 ¼ did not complete any education, 6 ¼ do not know). Parental education was dichotomized into two categories: “high school or less” ( ¼ 1) and “college/university education” ( ¼ 2).
Statistical analyses Statistical analysis started with psychometric evaluation of the self-control measure to reveal potential multi-dimensionality. Next, descriptive analyses and bivariate correlations were run. Finally, logistic regression analysis was run to explore associations between the independent variables and SSB consumption with school lunch.
1739
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Psychometric evaluation of the self-control measure. Psychometric evaluation of the self-control items included factor analysis and calculation of internal consistency reliability (Cronbach’s α). We applied Tabachnick and Fidell’s (2007) recommendations for assessment of the appropriateness of our data for factor analysis: i.e. the presence of correlation coefficients greater than 0.3 in the correlation matrix, a KMO value of 0.6 or greater and a significant Bartlett’s test ( po0.05). Initial factor analysis using the Kaiser criterion (i.e. eigenvalue W1) and a scree plot test followed by factor analyses using different numbers of fixed factors were used to decide the number of factors to retain. Since the items were likely to be significantly correlated (i.e. they were all aiming to tap food-related self-control), oblique rotation was chosen to clarify the data structure (Costello and Osborne, 2005). Factor loadings of 0.4 or higher on allocated scale were used as a criterion for convergent validity, while cross-loadings of less than 0.4 on any other scale were used as a criterion for discriminant validity (Hair et al., 2010). Additionally, substantive evaluation based on the items’ face validity and previous research on the dimensionality of the self-control construct was used to arrive at the final factor solution. Because of a small number of items in our self-control measure, Cronbach’s α was classified as ⩾ 0.6¼ acceptable and ⩾ 0.07 ¼ good (Field, 2013). Before running further analyses, items were added up to produce averaged scores.
Descriptive analyses. Mean scores, standard deviations, skewness and kurtosis values were calculated for all study variables. As suggested by Kline (2005), we chose to apply cut-off values of 3.0 and 8.0 for skewness and kurtosis, respectively. Prior to model analyses, bivariate correlations were run to test for multicollinearity between independent variables. We applied a cut-off value of 0.80 or greater for multicollinearity, as suggested by Haerens et al. (2008).
Model analysis. Multiple logistic regression was used to explore the impact of the measured intrinsic (self-control dimensions and intentions) and extrinsic (perceived descriptive peer norms and parental HEG) factors on the consumption of SSB with school lunch. Since adolescent gender and parental educational level are well-known correlates of children and adolescents’ dietary behaviours, including soft drink consumption (Forshee and Storey, 2003; Vereecken et al., 2005), the regression model included adjustments for these variables.
Results Psychometric evaluation of the self-control measure Inspection of the correlation matrix for the five self-control items revealed the presence of several correlation coefficients of 0.3 and above. The KMO value was 0.69, and Bartlett’s test of sphericity showed statistical significance ( po0.001), supporting the factorability of our data. The Kaiser criterion suggested a two-factor solution, while the scree plot test suggested a three-factor solution. Based on this, we compared forced two- and three-factor solutions to decide how many factors to retain. In our sample, a two-factor solution reflecting the hypothesised subcomponents of self-control, resistance and avoidance, seemed more reasonable than a three-factor solution. The three items loading on the resistance factor made conceptual sense, as they all seemed to reflect the individual’s perception of his/her self-discipline regarding unhealthy eating/unhealthy food consumption (i.e. ability to resist temptations). However, the two items loading on the avoidance factor appeared conceptually different from each other as one item seemed to be a clear cut reflection of the individual’s avoidance of unhealthy foods (I avoid food that is not good for me), while the other item seemed to be a reflection of the individual’s perception of other people’s view on his/her self-discipline regarding unhealthy food (People would say that I have good self-discipline when it comes to unhealthy food). Thus, the latter item was removed, and a final factor solution with three items reflecting
1740
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
resistance and one item reflecting avoidance was reached (see Table I). This two-factor solution explained 79 per cent of the variance in our data, and Cronbach’s α for the resistance dimensions was 0.80 (it was not possible to calculate α for the avoidance dimension after removing one of two items in the final solution). A total (averaged) score for the resistance dimension was calculated, and the resulting new self-control variables named “resistance” and “avoidance” were used as independent variables in the logistic regression model.
Descriptive analyses Mean scores, standard deviations, skewness and kurtosis for the study variables are presented in Table II. Bivariate correlations are presented in Table III. All variables had values within the range of chosen cut-offs for skewness and kurtosis, and no multicollinearities were found for the independent variables in our proposed regression model.
Self-control items Resistance Avoidance
I have a hard time breaking bad food habits (R) 0.79 I wish I had more self-discipline… (R) 0.87 Sometimes I cannot stop myself from eating… (R) 0.86 I avoid food that is not good for me 0.93 Cronbach’s α 0.80 – R2 0.55 0.24 Note: (R) ¼ reversed items
Table I. Results from factor
analysis of the self-control measure,
presenting factor names, factor
loadings, Cronbach’s α and variance
explained (R2) for the final solution
Resist Avoid Intent HEG DPN SSB cons
Resistance (resist) – Avoidance (avoid) −0.28** – Intentions (intent) 0.08* −0.33** – Healthy eating guidance (HEG) 0.04 0.23** −0.11* – Descriptive peer norms (DPN) 0.04 −0.11** 0.24** −0.01 – SSB consumption (SSB cons)a −0.09* −0.30** 0.54** 0.02 0.31** – Notes: This variable was dichotomized before running regression analysis. aMeasured in times per week. *po0.05; **po0.01
Table III. Bivariate correlations
between the study variables
Variable (number of items) Mean (SD) Skewness Kurtosis
Resistance (3)a 3.36 (1.07) −0.43 −0.57 Avoidance (1)a 3.04 (1.14) −0.04 −0.87 Intentions to consume SSB (1)a 1.74 (1.19) 1.53 1.20 Healthy eating guidance (9)a 3.59 (0.86) −0.45 −0.09 Descriptive peer norms, SSB consumption (1)a 3.44 (1.25) −0.44 −0.85 SSB consumption (1)b 1.09 (1.36) 1.45 1.20 Notes: This variable was dichotomized before running regression analysis. aScored on a five-point Likert scale (1 ¼ totally disagree, 2 ¼ partly disagree, 3 ¼ neutral, 4 ¼ partly agree, 5 ¼ totally agree); bmeasured in times per week
Table II. Means, standard deviations (SD),
skewness and kurtosis for the variables
included in logistic regression analysis
1741
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Model analysis The independent variables in our logistic regression model included the two self-control strategies resistance and avoidance, intentions to consume SSB with school lunch, perceived descriptive peer norms related to SSB consumption with school lunch and perceived parental HEG. Adjustments were made for adolescent gender, maternal and paternal educational level. The model was statistically significant, χ2(8) ¼ 172.15, po0.001, explaining 35 per cent (Nagelkerke R2) of the variance in SSB consumption with lunch. As shown in Table IV, three of the independent variables made a unique contribution to the variance explained (listed in order of importance): intentions to consume SSB with school lunch (OR ¼ 2.05, CI ¼ 1.68–2.50), perceived descriptive peer norms related to SSB consumption with school lunch (OR ¼ 1.33, CI ¼ 1.12–1.57) and the self-control avoidance dimension (OR ¼ 0.78, CI ¼ 0.62–0.98). These results indicated that students with higher scores on intentions and perceived descriptive peer norms were more likely to consume SSB with school lunch than students with lower scores on these variables and that students with higher scores on avoidance strategies were less likely to consume SSB with school lunch than students with lower scores on this variable.
Discussion The present study aimed to explore the associations between food-related self-control strategies, intentions, perceived descriptive peer norms, perceived parental HEG and adolescents’ consumption of SSB with school lunch, and evaluate the psychometric properties of five food-related self-control items in order to reveal potential multi-dimensionality (i.e. resistance and avoidance dimensions).
Psychometric evaluation of the self-control measure Running factor analysis on the five food-related self-control items resulted in two factors named resistance (three items) and avoidance (one item). Both face validity (i.e. the wording of the items) and a low correlation between the two factors (r ¼ 0.21, po0.01) supported this distinction. Furthermore, Cronbach’s α for the resistance factor indicated acceptable to good internal consistency reliability. Thus, our finding is in line with recent research suggesting that self-control is linked not only to the ability to resist temptations, but also to avoidance of temptations (Ent et al., 2015). Accordingly, the influence of these dimensions on dietary behaviours should be considered separately as well as in conjunction with each other. This was taken into account in the subsequent regression analysis of the present study.
Model analysis Results from multiple logistic regression indicated that intentions was the most important correlate of SSB consumption with school lunch, while perceived descriptive peer norms and avoidance appeared as weaker correlates. These findings are further discussed below.
OR CI
Self-control, resistance 1.10 0.89–1.34 Self-control, avoidance 0.77* 0.62–0.98 Intention to consume SSB 2.05* 1.68–2.51 Healthy eating guidance 1.02 0.80–1.29 Descriptive peer norms for SSB consumption 1.33* 1.12–1.58 Notes: Adjustments were made for adolescent gender, maternal and paternal educational level. *po0.05
Table IV. Results from logistic regression analysis presenting odds ratios (OR) and 95% confidence intervals (CI) for potential correlates of drinking SSB with school lunch
1742
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
The finding of a positive association between intentions and SSB consumption during school lunch is in line with previous research on attitude theories postulating that intentions are immediate precursors of behaviour (Ajzen, 1991; Perugini and Bagozzi, 2004).
The finding of a positive association between perceived descriptive peer norms and SSB consumption is also in line with the results of several previous studies assessing peer influence on adolescents’ behaviour related to snacking and SSB consumption (Chung et al., 2017; Gregori et al., 2011; Larson et al., 2017; van Ansem et al., 2015; Wouters et al., 2010). Notably, research by Townsend and Foster (2013) focusing on the school lunch context, revealed that a considerable part of students’ lunch choices could be explained by extrinsic factors such as peer descriptive norms. This may possibly originate from adolescents’ desire to belong to an in-group of peers (i.e. social pressure), and by the fact that adolescents and their peers spend a significant amount of time together at school. Bandura’s (1977) SLT postulates that people learn from observation, imitation and modelling of the behaviours, attitudes and reactions of other people. Spending time together facilitates all of these factors, and may thus contribute to the shaping of dietary preferences through social learning. Accordingly, as suggested by Hoeffler (2012), if an individual feels sufficiently close to a model (e.g. a peer) that is observed to use a particular product, s/he may be inclined to use the same product.
The negative association between avoidance and SSB consumption is self-explanatory and supported by previous research: if one avoids being exposed for tempting foods and beverages, one simply does not have the opportunity to consume them and the frequency of consumption decreases (Fishbach and Shah, 2006). In other words, avoiding temptations means reducing one’s exposure for triggers that may challenge the self-control resistance dimension (Ent et al., 2015). The lack of association between resistance and SSB consumption found in the present study supports the potential powerful effect of using avoidance strategies instead of relying on one’s ability to resist temptations. Thus, forecasting the self-control conflicts that the presence of tempting foods and beverages may elicit, and choosing to eliminate these stimuli from one’s immediate environment by avoiding them, seems like a fruitful, low-effort self-control strategy. According to Fishbach and Shah (2006), this tactic involves the operation of meta-cognitive planning processes that may be essential for consumers’ healthy navigation in a food environment characterised by the ready availability of palatable, high energy/low-nutrient products. In a previous study on adolescents’ food-related self-controlling strategies, Stok et al. (2012) identified a gap between adolescents’ knowledge of what they should do and how they can do it. This study further suggested that one of the possible reasons for this gap is that adolescents do not recognise situations in which they should use appropriate self-controlling strategies to ensure healthy eating. Thus, based on the results from the current study, the development of guiding principles on when to use avoidance strategies and how to do it may be beneficial.
No association was found between perceived parental HEG and adolescent SSB consumption with school lunch. This seems reasonable, considering the context of consumption (i.e. being away from home/being together with peers) and the increased need for autonomy in adolescents compared to younger children. Thus, a possible explanation may be that parental HEG, although positively framed, threatens adolescents’ need for autonomy and thereby acts against its purpose – resulting in no effect on the behaviour in question. As suggested in a recent study on adolescent impulsivity and parental regulation (Melbye et al., 2016), this may reflect a mechanism where adolescents’ resistance is triggered as a response to what is perceived as illegitimate parental supervision. This line of reasoning is supported by a qualitative study by Parkin and Kuczynski (2012) where adolescents were shown to use different strategies to resist parental rules and expectations. Among the strategies used was transgression of parental rules, resulting in an increase of the behaviour parents were attempting to reduce
1743
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
or control. Parkin and Kuczynski (2012) suggested that adolescents’ resistance about parental regulation might indicate both autonomous motives and awareness of their increased power in the parent–child relationship.
Strengths and limitations One strength of the present study is that it includes a psychometric evaluation of a supposedly one-dimensional self-control measure applied in previous research, followed by the application of the resulting new dimensions in subsequent regression analysis. Further, and according to a recent study by Larson et al. (2017), there is a lack of research simultaneously assessing personal (intrinsic) factors in combination with environmental (extrinsic) factors. Thus, another strength of this study is that it combines well-researched socio-environmental factors (peer and parental influences) with less explored personal factors (intentions and self-control strategies) to predict adolescents’ dietary behaviours. Among the limitations of the study is its cross-sectional design, which hampers causal inferences. Using adolescents’ self-reports on all study variables may also be considered a limitation, as it may increase the risk of common methods bias. Yet another limitation is the different specificity of the measures used. This may have contributed to the difference in strength of the associations found in our regression model. For example, the strong association between intentions and SSB consumption may have been influenced by the high level of specificity of the intentions variable towards the outcome variable (i.e. it was specific for SSB consumption with school lunch), while the weaker associations between the self-control variables and SSB consumption may have been influenced by their low level of specificity towards the outcome variable (i.e. they were more generally related to food consumption and not restricted to SSB consumption with school lunch).
Conclusive remarks and implications Today’s food environment with its ready availability of low-nutrient/high-energy foods and beverages calls not only for structural and social-environmental interventions to confront unhealthy temptations. Also personal tools like self-controlling strategies may be helpful to steer adolescents’ food and beverage consumption in a favourable direction—at least in contexts where structural and social-environmental interventions are not planned in the near future. Thus, based on results from the present study, and in line with previous work by Stok et al. (2012), we suggest that besides teaching adolescents what healthy eating is, teaching them when and how to use various strategies to promote healthy dietary behaviours may be beneficial. In particular, our results suggest that it might be useful to develop guiding principles for adolescents on how to create health-promoting intentions, how to avoid tempting stimuli in the immediate environment and how to deal with (perceived) peer pressure.
References
Ajzen, I. (1991), “The theory of planned behaviour”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
Ali, M.M., Amialchuk, A. and Heiland, F.W. (2011), “Weight-related behavior among adolescents: the role of peer effects”, PLoS One, Vol. 6 No. 6, Journal of Advertising Research, doi: 10.1371/journal. pone.0021179.
Ang, L. and Eisend, M. (2017), “Single versus multiple measurement of attitudes: a meta-analysis of advertising studies validates the single-item measure approach”, doi: 10.2501/jar-2017-001.
Bandura, A. (1977), Social Learning Theory, General Learning Press, New York, NY.
Baumeister, R.F., Heatherton, T.F. and Tice, D.M. (1994), Losing Control: How and Why People Fail at Self-Regulation, Academic press, San Diego, CA.
1744
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Baumeister, R.F., Vohs, K.D. and Tice, D.M. (2007), “The strength model of self-control”, Current directions in psychological science, Vol. 16 No. 6, pp. 351-355.
Bergkvist, L. (2015), “Appropriate use of single-item measures is here to stay”, Marketing Letters, Vol. 26 No. 3, pp. 245-255, doi: 10.1007/s11002-014-9325-y.
Bergkvist, L. and Rossiter, J.R. (2007), “The predictive validity of multiple-item versus single-item measures of the same constructs”, Journal of Marketing Research, Vol. 44 No. 2, pp. 175-184.
Birch, L.L. and Davidson, K.K. (2001), “Family environmental factors influencing the developing behavioural controls of food intake and childhood overweight”, Pediatric Clinics of North America, Vol. 48 No. 4, pp. 893-907.
Birch, L.L., Fisher, J.O. and Davidson, K.K. (2003), “Learning to overeat: maternal use of restrictive practices promotes girls’ eating in the absence of hunger”, The American Journal of Clinical Nutrition, Vol. 78 No. 2, pp. 215-220.
Bisogni, C.A., Connors, M., Devine, C.M. and Sobal, J. (2002), “Who we are and how we eat: a qualitative study of identities in food choice”, Journal of Nutrition Education and Behavior, Vol. 34 No. 3, pp. 128-139.
Bjelland, M., Hausken, S.E.S., Sleddens, E.F.C., Andersen, L.F., Lie, H.C., Finset, A., Maes, L., Melbye, E.L., Glavin, K., Hanssen-Bauer, M. and Lien, N. (2014), “Development of family and dietary habits questionnaires: the assessment of family processes, dietary habits and adolescents’ impulsiveness in Norwegian adolescents and their parents”, International Journal of Behavioral Nutrition and Physical Activity, Vol. 11 No. 1, p. 130, doi: 10.1186/s12966-014-0130-z.
Blissett, J. (2011), “Relationships between parenting style, feeding style and feeding practices and fruit and vegetable intake in early childhood”, Appetite, Vol. 57 No. 3, pp. 826-831.
Chan, D.K., Lonsdale, C. and Fung, H.H. (2012), “Influences of coaches, parents, and peers on the motivational patterns of child and adolescent athletes”, Scandinavian Journal of Medicine & Science in Sports, Vol. 22 No. 4, pp. 558-568, doi: 10.1111/j.1600-0838.2010.01277.x.
Chung, S.J., Ersig, A.L. and McCarthy, A.M. (2017), “The influence of peers on diet and exercise among adolescents: a systematic review”, Journal of Pediatric Nursing, Vol. 36, pp. 44-56.
Costello, A.B. and Osborne, J.W. (2005), “Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis”, Practical Assessment, Research & Evaluation, Vol. 10 No. 7, pp. 1-9.
De Coen, V., Vansteelandt, S., Maes, L., Huybrechts, I., De Bourdeaudhuij, I. and Vereecken, C. (2012), “Parental socioeconomic status and soft drink consumption of the child: the mediating proportion of parenting practices”, Appetite, Vol. 59 No. 1, pp. 76-80.
De la Haye, K., Robins, G., Mohr, P. and Wilson, C. (2010), “Obesity-related behaviors in adolescent friendship networks”, Social Networks, Vol. 32 No. 3, pp. 161-167.
Ent, M.R., Baumeister, R.F. and Tice, D.M. (2015), “Trait self-control and the avoidance of temptation”, Personality and Individual Differences, Vol. 74, pp. 12-15.
Field, A. (2013), Discovering Statistics Using IBM SPSS Statistics, SAGE publications Ltd, London.
Fishbach, A. and Shah, J.Y. (2006), “Self-control in action: implicit dispositions toward goals and away from temptations”, Journal of Personality and Social Psychology, Vol. 90 No. 5, pp. 820-832, doi: 10.1037/0022-3514.90.5.820.
Fisher, J.O. and Birch, L.L. (1999), “Restricting access to palatable foods affects children’s behavioural response, food selection, and intake”, The American Journal of Clinical Nutrition, Vol. 69 No. 6, pp. 1264-1272.
Fismen, A.-S., Smith, O.R.F., Torsheim, T., Rasmussen, M., Pedersen Pagh, T., Augustine, L., Ojala, K. and Samdal, O. (2016), “Trends in food habits and their relation to socioeconomic status among Nordic adolescents 2001/2002-2009/2010”, PLoS One, Vol. 11 No. 2, doi: 10.1371/journal.pone.0148541.
Forshee, R.A. and Storey, M.L. (2003), “Total beverage consumption and beverage choices among children and adolescents”, International Journal of Food Sciences and Nutrition, Vol. 54 No. 4, pp. 297-307.
1745
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Fox, N.A. and Calkins, S.D. (2003), “The development of self-control of emotion: intrinsic and extrinsic influences”, Motivation and Emotion, Vol. 27 No. 1, pp. 7-26, doi: 10.1023/a:1023622324898.
Gebremariam, M.K., Henjum, S., Terragni, L. and Torheim, L.E. (2016), “Correlates of fruit, vegetable, soft drink, and snack intake among adolescents: the ESSENS study”, Food & Nutrition Research, Vol. 60 No. 1, p. 32512.
Gollwitzer, P.M. (1996), “Planning and coordinating action”, in Gollwitzer, P. and Bargh, J.A. (Eds), The Psychology of Action: Linking Cognition and Motivation to Behavior, Guilford, New York, NY, pp. 283-312.
Gregori, D., Foltran, F., Ghidina, M., Zobec, F., Ballali, S., Franchin, L. and Berchialla, P. (2011), “The ‘snacking child’ and its social network: some insights from an Italian survey”, Nutrition Journal, Vol. 10 No. 1, p. 132, doi: 10.1186/1475-2891-10-132.
Guidetti, M. and Cavazza, N. (2008), “Structure of the relationship between parents’ and children’s food preferences and avoidances: an explorative study”, Appetite, Vol. 50 No. 1, pp. 83-90.
Haerens, L., Craeynest, M., Deforche, B., Maes, L., Cardon, G. and De Bourdeaudhuij, I. (2008), “The contribution of psychosocial and home environmental factors in explaining eating behaviours in adolescents”, European Journal of Clinical Nutrition, Vol. 62 No. 1, p. 51.
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010), Multivariate Data Analysis: A Global Perspective, 7th ed., Pearson Education, Upper Saddle River, NJ.
Haszard, J.J., Williams, S.M., Dawson, A.M., Skidmore, P.M.L. and Taylor, R.W. (2013), “Factor analysis of the comprehensive feeding practices questionnaire in a large sample of children”, Appetite, Vol. 62, pp. 110-118.
Hoeffler, S. (2012), “Preference exploration and learning: the role of intensiveness and extensiveness of experience”, Journal of Consumer Psychology, Vol. 23, No. 3, pp. 330-340.
Honkanen, P., Olsen, S.O., Verplanken, B. and Tuu, H.H. (2012), “Reflective and impulsive influences on unhealthy snacking: the moderating effects of food related self-control”, Appetite, Vol. 58 No. 2, pp. 616-622.
Junger, M. and van Kampen, M. (2010), “Cognitive ability and self-control in relation to dietary habits, physical activity and bodyweight in adolescents”, International Journal of Behavioral Nutrition and Physical Activity, Vol. 7 No. 22.
Kline, R.B. (2005), Principles and Practice of Structural Equation Modeling, 2nd ed., Guilford Press, New York, pp. 48-51.
Lally, P., Bartle, N. and Wardle, J. (2011), “Social norms and diet in adolescents”, Appetite, Vol. 57 No. 3, pp. 623-627.
Larson, N., Miller, J.M., Eisenberg, M.E., Watts, A.W., Story, M. and Neumark-Sztainer, D. (2017), “Multicontextual correlates of energy-dense, nutrient-poor snack food consumption by adolescents”, Appetite, Vol. 112, pp. 23-34.
Luszczynska, A., de Wit, J.B., de Vet, E., Januszewicz, A., Liszewska, N., Johnson, F., Pratt, M., Gaspar, T., de Mator, M.G. and Stok, F.M. (2013), “At-home environment, out-of-home environment, snacks and sweetened beverages intake in preadolescence, early and mid-adolescence: the interplay between environment and self-regulation”, Journal of Youth and Adolescence, Vol. 42 No. 12, pp. 1873-1883.
Maloney, P.W., Grawitch, M.J. and Barber, L.K. (2012), “The multi-factor structure of the brief self-control scale: discriminant validity of restraint and impulsivity”, Journal of Research in Personality, Vol. 46 No. 1, pp. 111-115.
Melbye, E.L. and Hansen, H. (2015), “Promotion and prevention focused feeding strategies: exploring the effects on healthy and unhealthy child eating”, BioMed Research International, available at: http://www.hindawi.com/journals/bmri/2015/306306/
Melbye, E.L., Bergh, I.H., Hausken, S.E., Sleddens, E.F., Glavin, K., Lien, N. and Bjelland, M. (2016), “Adolescent impulsivity and soft drink consumption: the role of parental regulation”, Appetite, Vol. 96, pp. 432-442.
1746
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
Musher-Eizenman, D. and Holub, S. (2007), “Comprehensive feeding practices questionnaire: validation of a new measure of parental feeding practices”, Journal of Pediatric Psychology, Vol. 32 No. 8, pp. 960-972.
Nordic Council of Ministers (2014), Nordic Nutrition Recommendations 2012: Integrating Nutrition and Physical Activity, Copenhagen, Norden.
Norwegian Directorate of Health (2013), Mat og måltider i videregående skole. En kvantitativ, landsdekkende undersøkelse blant kontaktlærere, skoleledere og ansvarlige for kantine/matbod, Norwegian Directorate of Health, Oslo.
Parkin, C.M. and Kuczynski, L. (2012), “Adolescent perspectives on rules and resistance within the parent-child relationship”, Journal of Adolescent Research, Vol. 27 No. 5, pp. 632-658.
Patrick, H. and Nicklas, T.A. (2005), “A review of family and social determinants of children’s eating patterns and diet quality”, Journal of the American College of Nutrition, Vol. 24 No. 2, pp. 83-92.
Perkins, J.M., Perkins, H.W. and Craig, D.W. (2010), “Misperceptions of peer norms as a risk factor for sugar-sweetened beverage consumption among secondary school students”, Journal of the American Dietetic Association, Vol. 110 No. 12, pp. 1916-1921.
Perugini, M. and Bagozzi, R.P. (2004), “The distinction between desires and intentions”, European Journal of Social Psychology, Vol. 34 No. 1, pp. 69-84.
Ryan, R.M. and Deci, E.L. (2000), “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being”, American Psychologist, Vol. 55 No. 1, pp. 68.
Salvy, S.-J., De La Haye, K., Bowker, J.C. and Hermans, R.C. (2012), “Influence of peers and friends on children’s and adolescents’ eating and activity behaviors”, Physiology & Behavior, Vol. 106 No. 3, pp. 369-378.
Stead, M., McDermott, L., MacKintosh, A.M. and Adamson, A. (2011), “Why healthy eating is bad for young people’s health: identity, belonging and food”, Social Science & Medicine, Vol. 72 No. 7, pp. 1131-1139.
Stevenson, C., Doherty, G., Barnett, J., Muldoon, O.T. and Trew, K. (2007), “Adolescents’ views of food and eating: identifying barriers to healthy eating”, Journal of Adolescence, Vol. 30 No. 3, pp. 417-434.
Stok, F., de Ridder, D., Adriaanse, M. and de Wit, J. (2010), “Looking cool or attaining self-rule. Differents motives for autonomy and their effects on unehalthy snack purchase”, Appetite, Vol. 54 No. 3, pp. 607-610.
Stok, F., de Vet, E., de Ridder, D. and de Wit, J. (2012), “ ‘I should remember I don’t want to become fat’: adolescents’ views on self-regulatory strategies for healthy eating”, Journal of Adolescence, Vol. 35 No. 1, pp. 67-75.
Stok, F., De Vet, E., Wardle, J., Chu, M.T., De Wit, J. and De Ridder, D. (2015), “Navigating the obesogenic environment: how psychological sensitivity to the food environment and self-regulatory competence are associated with adolescent unhealthy snacking”, Eating Behaviors, Vol. 17, pp. 19-22.
Stok, F.M., de Vet, E., de Ridder, D. and de Wit, J. (2012), “ ‘I should remember I don’t want to become fat’: adolescents’ views on self-regulatory strategies for healthy eating”, Journal of Adolescence, Vol. 35 No. 1, pp. 67-75.
Tabachnick, B.G. and Fidell, L.S. (2007), Using Multivariate Statistics, 5th ed., Pearson Education, Boston, MA.
Tangney, J.P., Baumeister, R.F. and Boone, A.L. (2004), “High self-control predicts good adjustment, less pathology, better grades, and interpersonal success”, Journal of Personality, Vol. 72 No. 2, pp. 271-324, doi: 10.1111/j.0022-3506.2004.00263.x.
Townsend, N. and Foster, C. (2013), “Developing and applying a socio-ecological model to the promotion of healthy eating in the school”, Public Health Nutrition, Vol. 16 No. 6, pp. 1101-1108.
van Ansem, W.J.C., Schrijvers, C.T.M., Rodenburg, G. and van de Mheen, D. (2015), “Children’s snack consumption: role of parents, peers and child snack-purchasing behaviour. Results from the INPACT study”, European Journal of Public Health, Vol. 25 No. 6, pp. 1006-1011, doi: 10.1093/eurpub/ckv098.
1747
Self-control, intentions and
social influences
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )
van der Horst, K., Oenema, A., Ferreira, I., Wendel-Vos, W., Giskes, K., van Lenthe, F. and Brug, J. (2006), “A systematic review of environmental correlates of obesity-related dietary behaviors in youth”, Health Education Research, Vol. 22 No. 2, pp. 203-226.
Vartanian, L., Herman, C. and Polivy, J. (2007), “Consumption stereotypes and impression management: how you are what you eat”, Appetite, Vol. 48 No. 3, pp. 265-277.
Vereecken, C.A., Inchley, J., Subramanian, S., Hublet, A. and Maes, L. (2005), “The relative influence of individual and contextual socio-economic status on consumption of fruit and soft drinks among adolescents in Europe”, The European Journal of Public Health, Vol. 15 No. 3, pp. 224-232.
Verloigne, M., Van Lippevelde, W., Maes, L., Brug, J. and De Bourdeaudhuij, I. (2012), “Family-and school-based correlates of energy balance-related behaviours in 10–12-year-old children: a systematic review within the ENERGY (European energy balance research to prevent excessive weight Gain among Youth) project”, Public health nutrition, Vol. 15 No. 8, pp. 1380-1395.
Verzeletti, C., Maes, L., Santinello, M. and Vereecken, C.A. (2009), “Soft drink consumption in adolescence: associations with food-related lifestyles and family rules in Belgium Flanders and the Veneto region of Italy”, European Journal of Public Health, Vol. 20 No. 3, pp. 312-317.
Vohs, K.D. and Baumeister, R.F. (2016), Handbook of Self-Regulation: Research, Theory, and Applications, Guilford Publications.
WHO (2003), “Diet, nutrition and the prevention of chronic diseases”, Report of a Joint WHO/FAO Expert Consultation (916), Geneva.
Wouters, E.J., Larsen, J.K., Kremers, S.P., Dagnelie, P.C. and Geenen, R. (2010), “Peer influence on snacking behavior in adolescence”, Appetite, Vol. 55 No. 1, pp. 11-17.
Corresponding author Elisabeth Lind Melbye can be contacted at: elisabeth.l.melbye@uis.no
For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com
1748
BFJ 120,8
D ow
nl oa
de d
by T
ex as
A &
M U
ni ve
rs it
y A
t 06
:2 8
01 N
ov em
be r
20 18
( P
T )