Understanding the influence of the snack definition on the associationbetween snacking and obesity: a review

DARIO GREGORI1, FRANCESCA FOLTRAN2, MARCO GHIDINA3, & PAOLA BERCHIALLA4

1Laboratory of Epidemiological Methods and Biostatistics, Department of Environmental Medicine and Public Health,

University of Padova, Italy, 2Department of Environmental Medicine and Public Health, University of Padova, Italy,3ZETA Research Ltd, Trieste, Italy, and 4Department of Public Health and Microbiology, University of Torino, Italy

AbstractThe aim of the present study is to understand how different definitions of snacking influence the estimated probability of obesityin the presence of concurrent risk factors. Factors influencing obesity were evaluated by reviewing the relevant literature througha PUBMED search. Six different modalities to define snack consumption were identified. A Bayesian network model in whichnodes represent the variables that the retrieved studies indicate as affecting the probability of obesity was implemented and usedto estimate the individual risk of developing obesity taking into account the concurrent effect of the considered risk factors. For asubject with a given profile of factors, the probability of obesity varies according to the chosen definition of snacking, up tomaximum of 70%. The variability of the probability of obesity attributable to the chosen definition of snacking is very high andmay threaten any conclusion about the effect of snacking, which may be related to the specific definitions adopted in the study.

Keywords: Children, snacks, obesity, nutritional epidemiology

Introduction

Research on the determinants of obesity is fraught with

controversial issues,mostly because it iswell recognized

as being a multi-factorial condition, resulting from an

imbalance between energy intake and expenditure.

In fact, genetic, cultural, socioeconomic, behavioral

and situational factors all play a role in eating and

weight control (Bray and Champagne 2005).

A number of studies examined the relationship

between obesity and diet in children, and, among

dietary patterns, snacks received great attention:

indeed, commercially available snack foods are, on

average, considerably more energetically dense than

most foods in the diet. Thus, snacks act with respect to

the average of the overall diet as major contributors to

calorie and saturated fat intake (Whybrow et al. 2007).

However, even if the percentage of energy from dietary

fat is widely believed to be an important determinant

of body fat accumulation, the existence of causal

relationships between single nutrients or foods and

obesity is controversial: prospective cohort studies

have frequently failed in finding a correlation, while

also factors other than dietary choices, including the

speed of ingestion and physical activity, seem to

influence obesity (Jordan et al. 1981; Hu et al. 1997;

Erkkila et al. 2008). Nevertheless, synthesizing the

existing knowledge about this topic is a difficult task,

given the enormous heterogeneity in scientific litera-

ture in describing and categorizing food dietary intake.

Particularly, snack is a generic word used to identify a

huge range of different foods, and the definition of the

snacking activity itself seems to be extremely variable

among studies (Gregori and Maffeis 2007). Indeed, at

least two strict concurrent definitions plus some

hybrids are present in the epidemiological research.

The first definition is based on food categories

(Wurtman et al. 1993) and consists of a taxonomy of

food, where snacks are identified by their quality and

composition. An alternative definition is based on time

criteria. In the time-based definition of snacking,

usually, foods consumed between 8:00 and 10:00 am,

ISSN 0963-7486 print/ISSN 1465-3478 online q 2011 Informa UK, Ltd.

DOI: 10.3109/09637486.2010.530597

Correspondence: Prof. Dario Gregori, Department of EnvironmentalMedicine and Public Health, Via Loredan 18, 35151 Padova, Italy. Tel: 3949 8275384. Fax: 39 2 700445089. E-mail: dario.gregori@unipd.it

International Journal of Food Sciences and Nutrition,

May 2011; 62(3): 270275

between 12:00 and 2:00 pm and between 6:00 and

8:00 pm are considered meals. Every food item

consumed between meals is thus considered a snack

(Toornvliet et al. 1996). Hybrid definitions are also

presented in the literature, like the complex Food

Based Classification of Eating Episodes.

Although some authors (Gregori and Maffeis 2007)

pointed out that the usage of such definitions is not

always clearly stated in the papers, making the overall

understanding of the effect of snacking troublesome,

the actual influence of the adoption in a research study

of one or the other definition has never been estimated.

Indeed, empirical studies use only one specific

definition of snacking in their studies, so comparative

data are not available for the competing definitions.

The aim of the present paper is to understand how

the choice of different snack definitions is impacting

the probability of obesity, in the presence of

concurrent risk factors. In order to accomplish this

goal, we systematically reviewed existing evidence and

implemented a Bayesian network (BN). BNs rely on

Bayes theorem, in which probability distributions for

prior beliefs are modified by new information (the

likelihood function) in developing posterior inferences

(Spiegelhalter et al. 1999). An advantage of this

approach is that prior knowledge and beliefs can be

systematically incorporated, allowing one to fully take

into account the complex relationships between risk

factors related with obesity, as emerging from the

scientific literature.

In the following sections, the literature search

strategy aiming to identify all papers investigating the

relationship between snacks and obesity will be

described. Then, a short introduction to BNs will be

presented and the implementation of a BN model

incorporating the information coming from the

retrieved papers will be proposed and discussed.

Materials and methods

Search strategy and variables definition

A PubMed search based on the terms snack* and

obes*, limited to English-language papers published

between 1 January 2003 and 31 December 2007, was

carried out. A total of 228 cross-sectional, cohort and

randomized controlled trial studies were found and

included in the analysis.

In the articles identified through thePubMed search,

the following risk factors were recognized as being

related to obesity: physical activity, residence, television

viewing, PC/PlayStation usage, parental education,

smoking status of the parents, snacking behavior.

For our purposes, all of these variables were defined

in a precise operational way on the basis of the

literature, as extensively illustrated in the Appendix

(see Supplementary material; online version only),

and they were categorized as shown in Table I.

Moreover, as recognized in the literature, snack

consumption could be defined according to two main

criteria: (I) the type of the snack, or (II) the time when

the snack was eaten. In the first case, a snack consumer

could be a subject eating (IA) sweet snack, (IB) savory

snack or (IC) generic snack. According to the second

criterion, the consumer of the snack could be a subject

eating food (IIA) in the morning, (IIB) in the

afternoon or (IIC) between meals. Therefore, overall

six different modalities to define snack consumption

were identified.

Statistical analysis

Bayesian network. A BN is a graphical model

(Jensen 2001) that represents the joint probability

distributions over a set of random variables. It consists

of a graphical structure, probability tables and an

inference algorithm. The graphical structure is a series

of nodes representing variables and connected by

arrows, forming a graph that has no cycles. Arcs

encode the conditional dependence relationships

among variables. The direction of each arc indicates

a possible causal relationship between the nodes it

joins. Each arc implies a state of conditional

dependence; that is, linked nodes directly influence

each other, possibly in the form of a causal

relationship between the joined nodes. The absence

of an arc represents conditional independence. Each

node is a data structure that contains an enumeration

of possible values it can assume (states) and is

associated with a probability table that quantifies the

probability of each state depending on the values of the

incoming nodes.

Basically, there are three ways to build a BN: both

the structure and the probability tables can be

Table I. Definitions of variables, their abbreviations and categories.

Variable (abbreviation) Category

Gender (Gender) Male

Female

Physical activity (PA) Yes

No

Weight status (Obesity) Underweight/normal

Overweight/obese

Residence (Residence) Urban

Suburban

Rural

Television viewing (TV) Yes

No

Computer use (PC) Yes

No

Paternal education (P_edu) Primary

Secondary

University

Maternal education (M_edu) Primary

Secondary

University

Paternal weight status (P_obes) Underweight/normal

Overweight/obese

Maternal weight status (M_obes) Underweight/normal

Overweight/obese

Parental smoking habits (Smoker) Yes

No

Influence of the snack definition in obesity research 271

elicited from experts; they can be learned from data by

means of learning algorithms; or both the structure

and the numerical probabilities can be a mixture of

expert knowledge, measurements and objective fre-

quency data (Pearl 2000). When both the structure

and probabilities are established, the BN is able to

calculate the probability of developing obesity based

upon available information about other conditionally

dependent nodes using an inference algorithm that

relies on the principles of probabilistic reasoning and

Bayes theorem.

Information extraction and BNmodel implementation. We

implemented a BN model in which nodes represent

the variables that the studies identified in the literature

indicate as affecting the probability of obesity;

moreover, we adopted the first strategy to build the

BN model, among those described above: therefore,

structure and probability tables were both elicited

using the evidence coming from the articles yielded

by the PubMed search, considered as experts.

The probability tables are simple contingency tables

relating the risk factor (e.g. snack consumption) with

the outcome of interest (i.e. obesity), populated by the

numbers extracted from each single paper reviewed.

In the case of concurrent sources of information (as in

cases when more than one paper is providing

information to the given contingency table), all

values were retained in the model and treated as

coming from different experts.

The BN model we implemented allowed the

calculation of the marginal probabilities of obesity

according to the presence of each considered risk

factor. Particularly, the marginal probability to

become obese was computed for every modality used

to define the snack consumption. In order to show how

the concurrent effect of all factors is taken into account

in such a model, the BN was used to estimate the

probability of being obese for some subject profiles,

which are constructed randomly choosing a limited set

of covariates combinations, including gender, physical

activity, television viewing, using a PC, snack

consumption, parental smoke habits, maternal weight

status. Moreover, in order to study the effect of the

different snacking definitions, for every considered

profile we calculate six obesity probabilities, each of

them estimated assuming to describe the subject

snacking activity adopting one of the six modalities

previously identified.

The NETICA modeling environment (Norsys

Software Corporation 2006) was used for both the

BN implementation and the inference task.

Results

The BN based on the data extracted from the 228

papers is presented in Figure 1. All factors are strictly

inter-correlated except for mothers and fathers

weights, which act as independent factors.

The marginal probability of obesity (i.e. averaging

over the observed distribution of the other risk factors)

is presented in Table II. Snacking and television

viewing are the factors that, assuming all of the other

factors are equally distributed among subjects, are the

most relevant, being associated with a two-fold greater

probability of obesity as compared with the other risk

factors.

In Table III, the probability of obesity is estimated

for a set of covariates combinations: in this analysis,

the concurrent effect of all factors is taken into

account, and the effect of the different definitions of

snacking is presented.

Residence

M_edu

PC

TV

P_obes

Snack

Gender

Obesity

M_obese

P_edu

Smoker

PA

Figure 1. Bayesian network structure. Labeled rectangles represent nodes, and arrows represent conditional dependence relationships.

D. Gregori et al.272

Discussion

The increased prevalence of overweight and obesity

particularly among children and adolescents is a severe

public health problem shared by the developed and the

developing world, making strategies to identify risk

factors indispensable. The contribution of snacking

toward overweight in children has gained popularity

over the past decade: particularly, the wide availability

of energy-dense snack foods (Farley et al. 2009) and

the empirical observation that the prevalence of

obesity increased contemporarily with the snacking

habits (Bes-Rastrollo et al. 2009) seem to justify this

growing attention on the issue.

Snack foods tend to be energy dense, including in

the definition items such as sweet bakery goods,

sweets, and chocolate and savory snacks. Thus, the

potential effect of between-meal snacking on obesity

could be driven by an extra energy intake. Moreover,

subjects seem not to compensate energy intake after

snacking, especially if they consume snacks on an

irregular basis. However, the relationship between

snacking and body weight remains controversial

(Bes-Rastrollo et al. 2009), mainly because obesity is

a multifactor phenomenon and the quantification of

the contribution made by individual dietary com-

ponents to this condition could be difficult to estimate.

Numerous determinants of food intake patterns and

consequent weight gain have been described in

scientific literature: they include factors such as

genetics, psychological factors such as disinhibition

and restraint, physical activity levels, home economics

and sociodemographic characteristics (Perez-Cueto

et al. 2009).

Taking into account the concurrent effect of

multiple risk factors is a task even more difficult

when data are extracted from published papers; and

indeed, meta-analytic approaches to multifactorial

Table II. Marginal probabilities of obesity according to the

presence of known risk factors as emerging from the BN model.

Percentage probability of obesity

(95% confidence interval)

Television viewing (Yes) 9.12 (8.759.5)

Paternal weight status

(Overweight)

4.64 (4.394.89)

Maternal weight status

(Overweight)

4.61 (4.334.86)

Residence (Urban) 6.59 (6.296.89)

Physical activity (No) 7.84 (7.518.16)

Paternal education (Primary) 4.61 (4.364.84)

Maternal education (Primary) 3.84 (3.624.07)

Using computer (Yes) 7.15 (6.837.48)

Snack

IA 12.63 (12.1313.31)

IB 12.85 (12.5913.32)

IC 13.13 (12.9413.38)

IIA 13.63 (13.1814.08)

IIB 12.18 (11.6412.73)

IIC 14.03 (13.2614.8)

TableIII.

Concurrenteffectsofdifferentrisk

factorsonestimatedprobabilityofobesityaccordingto

somerandomlyconstructed

subject

profiles.

P(O

besity)(%

)

Gender

Physicalactivity

TVviewing

UsingPC

Snack

Smoker

Maternalweightstatus

Snack

IASnack

IBSnack

ICSnack

IIA

Snack

IIB

Snack

IIC

Fem

ale

No

Yes

Yes

Yes

Norm

al

10.48

12.77

12.25

11.38

13.1

17.75

Male

Yes

Yes

Yes

No

Norm

al

4.86

6.65

6.2

6.36

3.72

3.54

Male

No

Yes

No

Yes

Norm

al

15.64

14.27

14.9

13.52

12.29

16.85

Fem

ale

No

Yes

Yes

Yes

Yes

Obese

15.71

14.8

13.91

11.99

15.56

18.29

Male

Yes

Yes

No

Yes

Yes

Norm

al

13.06

12.88

11.46

12

9.64

13.46

Fem

ale

No

Yes

Yes

No

No

Norm

al

4.89

7.45

4.62

4.91

3.36

3.13

Male

No

Yes

No

Yes

Obese

15.06

13.4

12.84

16.35

11.48

15.79

Fem

ale

Yes

Yes

Yes

Yes

Yes

Norm

al

13.3

11.37

12.62

10.98

11.18

12.62

Influence of the snack definition in obesity research 273

studies are rarely conducted, due to such methodo-

logical difficulties.

The approach that we propose is based on BNs and

treats the information extracted from the published

papers as expert opinions, using it to derive

probabilistic scenarios that are eventually used for

providing the estimates. We found 228 articles,

published during a 5-year period and aiming to

describe the relationship between snack consumption

and obesity. Consistent with the literature, we

implemented a BN model giving an overall picture of

the inter-relationships among factors contributing to

obesity in which dietary habits interact with behavioral

and environmental factors.

According to the estimated probability of obesity,

computed for each risk factor, snack consumption and

television viewing seem to make a non-negligible

contribution. These behaviors are usually considered

to have a negative effect per se, the first one increasing

energy intake and the second one decreasing energy

expenditure, but are also believed to act together given

the childrens habits to eat snacks while watching

television. Prospective studies have shown that

television viewing during childhood is associated with

later adult body mass index (Viner and Cole 2005) and

that television viewing time is also associated with the

number of food items requested by children (Marquis

et al. 2005; Chamberlain et al. 2006).

However, despite the existing amount of studies

finalized to evaluate the effect of snack consumption,

alone or in combination with other factors, the

concept of snacking is not unambiguous. Authors

employ this term to identify various consumer profiles

that could be dramatically different with regard to food

eaten (sweet or savory snacks) and the time at which

food is eaten.

We retrieved in published papers almost six different

modalities to describe and define the consumption of

snacks; moreover, we showed that for a subject with a

given profile of the other factors, the probability of

obesity varies according to the chosen definition, with

a relative percentage difference among values of up to

70%. This fact is not surprising given the heterogen-

eity of elements that are commonly encompassed

under the definition of snack.

This finding could be useful in explaining the

enormous uncertainty existing at the present about the

contribution of snacking to obesity. Moreover, we

need to underline the fact that we directly observed

and studied the dramatic effect of differences in risk

factor definitions, but it is also important to underline

that the definition of obesity is also not univocal;

deficiencies in adopting a common and well-estab-

lished definition of obesity have been stressed else-

where (Gregori and Maffeis 2007), and obviously

differences in the definition of obesity could affect the

estimation of the effect of snacking as well as the effects

of all other risk factors.

In conclusion, variability such as that observed in

our analysis, where the probability of obesity can take a

large range of values according to the definition of

snacking adopted, potentially threatens any con-

clusion about the role of snacking in inducing obesity.

In this sense, any effect could be related mainly to the

specific definition adopted in a given study, preventing

any possible generalization of the conceptual categor-

ization of snacking itself.

Declaration of interest: The authors report no

conflicts of interest. The authors alone are responsible

for the content and writing of the paper of interest.

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Influence of the snack definition in obesity research 275

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