Accepted for publication cite as: Nago E, Lachat C, Dossa R, Kolsteren P (2011) Association of
out-of-home eating with anthropometric changes: a systematic review of prospective
studies. Critical Reviews in Food Science and Nutrition doi 10.1080/10408398.2011.627095
Association of out-of-home eating with anthropometric changes: a systematic review of
prospective studies
Eunice S Nago1,2, Carl K Lachat2,3, Romain AM Dossa1 and Patrick W Kolsteren2,3,*
1
Department of Nutrition and Food Sciences, Faculty of Agricultural Sciences, University of
Abomey-Calavi, Cotonou, Benin
2
Department of Food Safety and Food Quality, Faculty of Bioscience Engineering, Ghent
University, Ghent, Belgium
3
Nutrition and Child Health Unit, Department of Public Health, Prince Leopold Institute of
Tropical Medicine, Antwerp, Belgium
Keywords: Obesity, Life Style, Diet, Out-of-home eating, Prospective Studies
Running title: Eating out and anthropometric changes
Acknowledgements
CKL and PWK were responsible for the development of the search syntax and of the
screening methodology. CKL carried out the search. ESN and CKL did the screening and
PWK resolved conflicts. RAD assisted in the interpretation of the data. ESN drafted the paper
and all authors critically reviewed the manuscript.
Corresponding author
Patrick W Kolsteren, Nutrition and Child Health Unit, Department of Public Health, Prince
Leopold Institute of Tropical Medicine, Nationalestraat 155, Antwerp, Belgium.
Tel: 00 32 3 247 63 89 / Fax: 00 32 3 247 65 43 / Email: pkolsteren@itg.be
Conflict of interest statement
1
None of the authors declares a conflict of interest.
2
Abbreviations
USA: The United States of America
US: The United States
BMI: Body mass index
EPIC-PANACEA: The European Prospective Investigation Into Cancer-Physical activity,
Nutrition, Alcohol, Cessation of smoking, eating Out of Home and Obesity study
CARDIA: The Coronary Artery Risk Development in young Adults study
NR: Not reported
WHA: Australian Longitudinal Study on Women’s Health
SUN: Seguimiento Universidad de Navarra (University of Navarro Follow-up)
SEASONS: Seasonal Variation of Blood Cholesterol Study
Add Health: National Longitudinal Study of Adolescent Health.
WHO: World Health Organization
OH: Out-of-home
WC: waist circumference
IA: Interview-administered
SA: Self-administered
EI: Energy intake
PA: Physical activity
3
Abstract
In the present review, the association of out-of-home eating with anthropometric
changes was examined. Peer reviewed studies in 8 databases were searched for and 15
prospective studies were finally included in the review. The quality of the data was assessed
by considering the risk of bias in sample selection, data collection methods and the
appropriateness of the statistical tests used. From this, 7 studies, which used relatively large
samples or had a follow-up period longer than 10 years, were retained for further analysis. It
was concluded that eating out-of-home foods frequently increases the risk of becoming
overweight or obese, bodyweight, waist circumference and women’s BMI. Increase in weight
and waist circumference was greater when eating at fast-food outlets than at restaurants.
4
Introduction
Obesity constitutes one of the most serious public health problems nowadays (1). It is
a major burden to public health due to its adverse health consequences in the short and the
long term. The most significant health-related consequences are cardiovascular diseases,
hypertension, diabetes, cancer and osteoporosis (2). Obesity affects various populations, of all
age groups and both sexes, in high-income as well as low-and-middle income countries. A
fundamental cause of obesity, in addition to the increasingly sedentary lifestyles of today
populations, is the change in dietary habits.
An important change in food habits is the substantial increase of out-of-home eating
all over the world. For instance, three nationwide surveys in the USA showed an increase in
the daily energy contribution of out-of-home foods in people older than 2 years from 23 to
36% between 1977 and 1996 (3). In 1994, 56% of the US population ate outside their home at
least once a day (4). In a recent study in Benin, foods prepared out-of-home have already been
shown to provide 45% of the daily energy intake in a sample of urban adolescents (5).
Away-from-home foods are perceived as contributing to the obesity pandemic because
of their high energy-density, low nutrient density and large portions sizes (6;7). It has been
shown previously that weight gain is induced by an imbalance between energy intake and
energy expenditure and is driven by the consumption of foods with a high energy-density and
of increased portion sizes (8-10). Several interventions addressing various sources of out-ofhome foods, such as worksites (11;12), schools (13;14)and vending machines (15;16), have
been developed with the purpose of improving the nutritional quality of the food offer or
manage weight status. These initiatives have encountered a moderate success.
To our knowledge the strength of the evidence of the association of out-of-home
eating with an increased risk of obesity has not been assessed up to now. It is important to
review the available literature on this topic in order to design relevant obesity prevention
programs directed towards people who consume food outside their home. There has been a
recent review but restricted to fast-foods only (17). The present systematic review aimed at
assessing the association of out-of-home eating with anthropometric changes in various
population groups, in particular the risk of becoming overweight or obese and increases in
bodyweight, BMI, BMI z-score and waist circumference. We report the importance and the
nutritional characteristics of out-of-home eating in another review (Lachat C, Nago E,
Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P, unpublished data).
5
Methods
Inclusion and exclusion criteria
Original studies reporting longitudinally at least an anthropometric measure and,
longitudinally or not, the energy contribution or the frequency of out-of-home eating, were
retained, including interventions targeting dietary modification or weight management. Any
definition of out-of-home eating was considered (for instance the definitions using the place
of preparation or of consumption of foods) as well as studies which used a single source of
out-of-home foods e.g. fast foods or school foods. The review targeted free living humans
who were healthy at baseline, without specific dietary requirements, from both sexes, from
any age, race or ethnicity and any country. Therefore, studies reporting only on overweight or
obese subjects at baseline, pregnant women, or elderly in nursing homes, prisoners and
patients were excluded. Papers reporting on food safety and qualitative papers, such as
editorials and comments, were not considered. The references of the papers retained for data
extraction were also screened to see whether additional papers emerged. The inclusion and
exclusion criteria that were used in the screening process are listed in Table 1.
Literature Search
Studies were identified by searching through 8 electronic databases: Medline, CAB
Abstracts, the Cochrane Library, ISI Web of Knowledge, Embase, Agricola, Ingenta and
Bioline international. All contents were first assessed, without date and language restriction,
from July 7th to July 10th, 2010. There was an update from March 10th to March 18th2011.
The search syntax was elaborated in Medline and adapted to the other databases.
Combinations of the following terms were searched for in titles or abstracts: out-of-home,
eating out, street food, junk food, outside the home, away from home, takeaway, cafeteria,
food dispenser, catering, fast food, canteen, restaurant, worksite, food, diet, food habits,
eating, nutrition assessment and feeding behavior.
All the 7319 papers found were merged into a single database (Reference manager
version 9, The Thomson Corporation, NY) and doubles were deleted. A researcher (CL)
screened the titles of the remaining papers. The next steps were the reading of abstracts and of
full papers by two co-authors (CKL and ESN) independently. In case of doubt at any stage,
the paper concerned was kept in the review database. A discussion followed to solve the
6
divergence between the two independent appraisals and when necessary, the expertise of a
third co-author (PWK) was requested. A flow chart of the screening is represented in
Figure1.
Analysis of the papers
Information, such as authors and year of publication, countries, participants to the
study and sample size, was extracted to present each study included in this review. The
baseline characteristics of the subjects, the exposure (quantity, energy intake or frequency of
out-of-home eating) and the outcome measures (risk of becoming overweight or obese,
changes in bodyweight, BMI, BMI z-score and waist circumference) were also retrieved.
Results are presented in terms of change or absence of change in the outcome measure over
time, as an effect of a longitudinal change in out-of-home eating or of the consumption or outof-home foods at baseline.
The methodological quality of the studies was assessed by considering the risk of bias
in the sample selection (representativeness and participation rate), the design (controlled trial
or not), the data collection methods (validity and reliability), the appropriateness of the
statistical tests and whether they accounted for potential confounders or misreporting of
dietary intake.
Results
Description of the articles of the review
Of the 7319 papers, 128 were retained for full text reading. Thirteen papers were kept
after full reading and from the screening of their references, 2 additional studies were
retrieved. Hence, 15 papers were included in this review (Table 2).Eight studies (18-25) used
the place of consumption to define out-of-home eating. Most studies focused on fast-foods
(18;19;23;24;26-30). The other type of out-of-home foods that were assessed by the studies
were restaurant foods (18-20;22) quick-service and coffee shop foods (31), takeaway foods
(30;32) and workplace foods (22).
All the papers have been published in the last 13 years. Among the 15 papers, 10
reported studies conducted in the USA (18-21;23;24;27-29;31).One paper (22) analyzed data
from the European Prospective Investigation Into Cancer-Physical activity, Nutrition,
Alcohol, Cessation of smoking, eating Out of Home and Obesity (EPIC-PANACEA) (33;34)
which is carried out in 10 European countries. One study in Australia (32), one in the UK
7
(30), one in Spain (25) and one in Canada (26) were also included in this review. Three papers
(18;19;24) analyzed data from the Coronary Artery Risk Development in young Adults
(CARDIA) study (35;36). Two other studies (27;28) reported data from the only controlled
trial included in the review, the Pound of Prevention Study (37).
Subjects were in a large age-range, going from 8 to 75 years and mostly of both sexes.
Twelve of the papers reported on adults above 18 years of age (18-22;24-29;32). Eight studies
sampled at baseline children and adolescents (23;30;31)and/or young adults (18-20;23;24;32).
The sample size at baseline varied from 68 subjects (20) to 36994 subjects (22), with eight
studies that can be considered as large studies (18;19;22-25;30;32). The follow-up period
reported in the papers was variable and ranged from 12 weeks (20;26) to 15 years (24;30).
Four studies followed subjects for more than 10 years (19;24;30;31).
Association of eating out-of-home with anthropometric changes
Risk of overweight or obesity
Eating more often at a place out-of-home, in general, was associated with an increase
in the risk of becoming overweight or obese (Table 3).In the paper by Ma et al. (21), the
frequency of eating each meal (breakfast, lunch or dinner) out-of-home was calculated by
dividing the number of each meal out-of-home by the total number of days on which the meal
was consumed. Subjects were then separated into quartiles based on this frequency.
Compared to the subjects in the first quartile of breakfast frequency or of dinner frequency
away-from-home, the other subjects had about 2 to 3 times more risk of becoming obese. At
the opposite, being in the 2nd, 3rd or 4th quartile of lunch frequency out-of-home was
associated with a 30-60% lower risk for obesity, compared to being in the first quartile. BesRastrollo et al. (25)followed a larger sample (14106 versus 641 subjects) for a longer period
(average follow-up of 4 years versus 1 year) and found a 22 to 33% higher risk of overweight
or obesity when eating out at least once a week, compared to not eating out.
Change in bodyweight
More frequent fast-food consumption increased bodyweight (Table 4). The increase in
bodyweight ranged from 0.15 kg over 13 years for an additional eating occasion of fast-food
per week (19) to 1.68 kg over 3 years per each additional meal per week at a fast-food
establishment (27). Li et al. (29) reported an increase in weight with 1-2 visits/week to fast-
8
foods outlets during 1 year. Bes-Rastrollo et al. (25) found a 12 to 36% higher risk of gaining
2 kg or more per year when eating at fast-food outlets once or more per week.
The increase in weight associated with restaurant frequency was smaller than for fastfood frequency (19;22). In the study by Naska et al. (22), there was no significant association
between annual weight change and the energy intake of restaurant eaters or the energy intake
from restaurant foods.
Change in BMI and BMI z-score
Results on the link between fast-food consumption and change in BMI were
conflicting. Two studies (26;28) found no difference in BMI with increasing fast-food use
(Table 5). The first study (26)concluded that change in fast-food consumption over 12weeks
was not correlated with BMI change and the second one showed that over a one-year period,
eating at fast-food outlets did not change BMI(28).On the other hand, Duffey et al. (18)
concluded that a higher fast-food consumption was associated with a greater change in BMI
over 3 years. The increase in BMI was 0.20 kg/m2 for each increase in fast-food frequency of
1 time per week and was higher when the interaction of fast-food and restaurant was
examined.
There was no association between BMI change and restaurant frequency alone (18)
but eating takeaway foods increased BMI in women (32). Compared to women who never or
rarely ate takeaway foods, those who ate these once a week were 15% less likely to maintain
their BMI within a 5%range after 4 years (Table 5).
Eating fast-foods, takeaway or quick-service foods twice a week or more at baseline
increased the BMI z-score (30;31) whereas a 5-year change in fast-food consumption did not
predict BMI z-score at follow-up, controlling for baseline BMI z-score (23), Table 5.
Change in waist circumference
Findings about waist circumference were also conflicting (Table 6). Li et al. (29)
reported more than 1 cm increase in waist circumference with 1 or 2 visits per week to fastfood establishments during one year but Bédard et al. showed that fast-food consumption had
no effect on waist circumference over a 12-week period (26).
Quality appraisal of the findings
The studies reviewed used appropriate methods for data collection but most did not
pre-validate the tool used to measure out-of-home eating. Two studies conducted 24-hour
9
dietary recalls (21;22) and one paper used 7-day dietary records (31) to estimate the
importance of eating out. The remaining papers used frequency questionnaires. Ten papers
(18;19;21-24;26-29) reported that the dietary assessment was carried out by an interviewer
and in most papers (18-21;23;24;26-30), the anthropometric variables were measured and not
self-reported. In all studies, except three (22;26;32), the assessment of weight status was done
with a standard method or a method which validity and reliability had been established
previously. However, for the evaluation of the quantity, energy intake or frequency of awayfrom-home foods, only 3 studies (22;25;26) used pre-validated tools.
The studies also used appropriate statistical methods and, except for the study by
Bédard et al. (26), adjusted for potential confounders when analyzing the association between
eating out and weight status.
However, none of the papers had a study design that could infer causality. Only 2
papers (27;28) described a controlled trial but gave no details on the randomization process.
Furthermore, as the primary objective of this intervention was weight gain prevention and was
not related to out-of-home eating, these studies did not compare the association of out-ofhome eating and weight status between treatments and controls. One paper (26) reported a
cohort study and the remaining papers were about interrupted time series.
Furthermore, most of the studies were to some extent subjected to selection bias.
Seven studies used a small or a convenience sample (20;21;26-29;31). Four other studies
(18;19;22;24;25) reported on large samples but were restricted to 4 urban areas in the USA, in
the framework of the CARDIA study (18;19;24) or University graduates in Spain (25).
Furthermore, 3 out of the 4 studies had an average participation rate (<80%). Only the study
by Bes-Rastrollo et al. (25) had a high participation rate (95%).The remaining studies
(22;23;30;32) had an average participation rate but the report by Naska et al. (22) used a large
sample from 9 European countries and the studies by Niemeier et al. (23), Viner and Cole
(30) and Ball et al. (32) covered large national samples.
Analyzing the risks of bias did not allow a differentiation among the studies. Thus,
sample size and follow-up duration were used as criteria to select the best quality studies.
Seven papers were subsequently retained among which 3 studies that used large national
samples (23;30;32), the EPIC-PANACEA study by Naska et al. (22), 2 papers with a followup period longer than 10 years (19;24) and the study by Bes-Rastrollo et al. (25) which
followed a large sample of University graduates.
10
Among these papers, 1 reported on the risk of overweight and obesity (25), 1 on waist
circumference (19), 2 on BMI (25;32), 2 on BMI z-score (23;30) and 4 on bodyweight
(19;22;24;25). It was concluded that eating frequently at a place out-of-home increases the
risk of becoming overweight or obese. The increase was 33% when eating out at least twice a
week, compared to not eating out (25).Eating away from home frequently also increases
bodyweight but the weight gain was much higher when eating at fast-food outlets than at
restaurants (19;22;24). The weight gain was estimated to 129g per year when eating fastfoods at least twice a week (25).
A further finding is that eating at fast-food establishments or eating takeaway foods
frequently was associated with an increase in BMI (25;32). Particularly, women who
consumed takeaway foods once a week were 15% less likely to maintain their BMI within a
5% range after 4 years, compared to those who never or rarely ate them (32). Findings with
regard to BMI z-score relate to the change from adolescence to adulthood and were variable.
Change in the frequency of fast-food visits from adolescence to young adulthood did not
change BMI z-score at young adulthood, controlling for BMI in adolescence (23) whereas
eating fast-foods or takeaway meals twice a week or more in adolescence increased BMI zscore by 0.14 to 0.21 unit between 16 and 30 years (30;31).
Finally, eating at fast-food establishments was associated with a greater increase in
waist circumference over time than eating at restaurant. Each additional visit to fast-food
outlets per week increased waist circumference by 0.12 cm over 13 years and for restaurants,
it was 0.08cm (19).
Discussion
The aim of this systematic review was to assess the link between out-of-home eating
and anthropometric changes. After a quality appraisal of the 15 prospective studies included,
7 studies, which used relatively large samples (22;23;25;30;32) or had a follow-up period
longer than 10 years (19;24), were retained for further conclusions. From these papers, it was
concluded that eating frequently at a place out-of-home increases the risk of becoming
overweight or obese and also bodyweight. Weight gain however, was found to be much
higher when eating at fast-food outlets than at restaurants. Eating at fast-food establishments
was associated with a greater increase in waist circumference over time than eating at
restaurants. The mechanism that leads to weight gain and obesity is an imbalance between
energy intake and energy expenditure and is driven by the consumption of foods with a high
energy-density and of great portion sizes (8;9). A decrease in physical activity, and by
11
consequence a reduction in energy expenditure, is also conducive to obesity (38). Fast-foods
are known to have a high energy density (39;40) and a high fat content and led to a high
energy intake (Lachat C, Nago E, Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P,
unpublished data) (41). As shown previously, the latter were reported as having a high fat
content (Lachat C, Nago E, Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P,
unpublished data) (41). The review by Rosenheck confirmed that more fast-food consumption
is associated with increased caloric intake leading to weight gain (17). Our review adds to this
that this association also holds for restaurant foods.
Takeaway foods are foods purchased in a restaurant and often a fast-food
establishment, to be consumed elsewhere. This review shows that eating takeaway foods
frequently increased BMI in Australian women. This finding is in accordance with the fact
that fast-foods and restaurant foods increase energy intake and led to weight gain and
confirmed longitudinal data from French et al. (27) showing that fast-food use is associated
with higher energy and fat intake and greater body weight in women in the USA.
This review provided no evidence on canteen foods or school foods. Studies in
adolescents showed that change in the frequency of fast-food visits from adolescence to
young adulthood did not predict BMI z-score at young adulthood. Given that adolescents are a
critical age group in which out-of-home eating was a predominant source of energy (41), it is
important to study better the sources and quality of their out-of-home food sources and how
different eating habits are associated with their nutrition status, particularly regarding the risk
of overweight and obesity. Given that previous research have shown cross-sectionally how
eating in canteens can be associated with optimal dietary patterns (42), additional research is
needed to see how canteen foods can be used to prevent and control overweight and obesity in
this age group.
All the studies retrieved for this review were conducted in high-income countries,
particularly the USA. We did not retrieve studies from low- and middle-income countries.
This resulted in conclusions focusing on fast-foods and to a lesser extent on restaurant foods.
It is important to note, however, that street foods are a key source of foods away from home
in LMIC (41). Although they are important sources of traditional foods (43) and have shown
to be valuable ways to promote healthy diets (44;45;45;45;45), various studies show that
street foods undergo important changes towards high energy-dense fatty and sugary foods or
soft drinks (5;46;47).
The present review used a sensitive approach in searching potential articles to be
included and used a syntax with a high sensitivity to identify relevant studies. At the same
12
time, it was not limited to out-of-home in general but also evaluated studies which assessed a
specific source of out-of-home foods. Despite this approach, the number of longitudinal
studies available and of those that could be included in this paper was small. Findings of this
review are potentially biased by the fact that they relate only to out-of-home foods with high
fat and energy contents (fast-foods and restaurant foods).
Findings of this paper justify the need to monitor the nutritional quality of out-ofhome foods and its implication for public health. Further research should look at the
differences in nutrient contents between home and out-of-home foods in general and between
types of out-of-home foods in order to explain changes in consumers’ nutrition status. A
standardization of the definition of out-of-home eating is needed to make the interpretation
and comparison of results from future research easier. The classification of foods according to
their place of preparation, which influences their chemical composition and nutritional
quality, seems more appropriate compared with using the place of consumption.
Research should also go beyond the monitoring of out-of-home eating and its
implication for nutrition status. Various studies that have changed the composition or portion
sizes of foods eaten out-of-home have shown promising effects on dietary intake and weight
status (11;48-50). It is important however, to assess eating out comprehensively. Long-term
intervention studies are needed to determine how to offer consumers healthy out-of-home
foods and examine substitution effects between different sources of out-of-home foods or
with home foods.
This review calls for more research on the consumption of out-of-home foods and its
longitudinal effect on nutrition status. It concludes that eating out-of-home foods frequently
increases the risk of becoming overweight or obese, bodyweight, waist circumference and
women’s BMI. Increase in weight and waist circumference is greater when eating at fast-food
outlets than at restaurants.
13
Table 1. Inclusion and exclusion criteria
Inclusion criteria
Type of study
Prospective study, including intervention study targeting
dietary modification or weight management
Topic
Uses any definition of out-of-home eating, including only
one kind of out-of-home foods
Measures included
-
-
Subjects
-
Reports longitudinally at least an indicator of
weight status (weight, BMI, waist circumference,
waist-and-hip ratio, skinfolds, body composition
indices) as primary or secondary outcome
Reports longitudinally or not at least the percent
energy or the frequency of out-of-home eating as
primary or secondary outcome
Human subjects from all age groups, healthy and
without specific dietary requirements
Infants, children, adolescents and/ or adults, males
and/ or females, of any race or ethnicity and from
any country
Exclusion criteria
-
-
-
Cross-sectional study
review
conference paper
qualitative study
editorials, book or article comments, policy briefs
case study
Not about out-of-home eating
About the cost of or expenditures on out-of-home
foods,
About food safety
About food choices or preferences
Reports no indicator of weight status
No report of the percent energy or the frequency of
out-of-home eating
Reports cross-sectionally an indicator of weight
status
Patients or institutionalized subjects
Subjects with particular nutrient requirements such
as sport people or soldiers, pregnant or lactating
women
Underweight, overweight or obese people only
14
Table 2. Description of the articles of the review
Ref.
Authors and
Countries
Participants
Sample size
publication
Start of data
Follow-up
Source
collection
length
data
of
Type of study
Definition
source of OH
year
(32)
Ball
or
foods
et
al.
Australia
(2002)
18-23years
Women,
n=14779
1996
4 years
WHA
Interrupted
nationally
time
Takeaway food
series
representative
(26)
Bédard et al.
Canada
(2010)
30-65year
n=77
20011
12 weeks
Primary data
Women, urban
Cohort
study,
Fast-food
Intervention
promoting
the
Mediterranean
food pattern
(25)
Bes-Rastrollo
Spain
et al. (2009)
University graduates
n=14106
1999
Mean age of 37years
4.4years
on
SUN
Interrupted
average
time
series
Viner
and
UK
Cole (2006)
16 years, males and
females,
and fast-food
n= 5723
1970
nationally
Naska et al.
Denmark,
35-74years
(2011)
France,
Men and women
15
years:
from 16 to
representative
(22)
of
consumption
Males and females,
(30)
Place
1970
British
Interrupted
Birth Cohort
series
EPIC-
Interrupted
PANACEA
series
time
Fast-food
and
takeaway
29-30 years
n=36994
1995
1.1
years
to
9.4
time
Place
of
consumption:
Germany,
restaurant
Greece, Italy,
(restaurant
The
cafeteria,
Netherlands,
fast-food) and
Norway,
work
bar,
15
Spain,
(workplace)
Sweden, UK
(19)
Duffey et al.
USA
(2009)
18-30years,Black
n=5115
1985
13
years:
and White, male and
years 7, 10
female, urban
and 20
CARDIA
Interrupted
time
series
Place
of
consumption:
Fast-food
and
restaurant
(29)
Li
et
al.
USA
(2009)
50-75years
n=1221
2006
1 year
Men and women
Portland
Interrupted
Neighborhood
series
time
Fast-food
time
Place
Environment
and
Health
Study
(18)
Duffey et al.
USA
(2007)
18-30years,Black
n=5115
1985
3 years: from
and White, male and
7th
female, urban
year
CARDIA
10th
to
Interrupted
series
of
consumption:
fast-food
and
restaurant
(23)
Niemeier
et
USA
al. (2006)
11-21years,
7-12th
n=14738
1996
5 years
Add Health
graders, males and
females,
Interrupted
time
series
Place
of
consumption:
nationally
fast-food
representative
(24)
Pereira et al.
USA
18-30years
n=5115
1985
(2005)
15
years:
baseline
CARDIA
to
Interrupted
time
series
Levitsky et al.
(2004)
USA
University freshmen
n=68
2000
12 weeks
of
consumption:
15th year
(20)
Place
fast-food
Primary data
Interrupted
series
time
Place
of
consumption:
all-you-can-eat
hall,
pay-cash
16
hall, restaurant
(31)
Thompson et
USA
8-12years, girls
n=196
1990
1 to 10 years
Primary data
Interrupted
al. (2004)
time
series
Quick-service
food,
coffee-
shop food and
restaurant food
(21)
Ma
et
al.
USA
(2003)
(27)
20-70years
n=641
1994
1 year
SEASONS
Interrupted
Men and women
French et al.
USA
(2000)
series
20-45 years
Low-
and
time
n=998
NR
3 years
high-
income women
Pound
of
Weight
Study (POP)
prevention
of
consumption
Controlled trial
prevention
Place
2
Fast-food
gain
intervention
(28)
Jeffery
and
French (1998)
USA
20-45years,
men,
low-income
and
n=1226†
NR
1 year
high-income women
Pound
of
Controlled trial2
prevention
Weight
Study (POP)3
prevention
Fast-food
gain
intervention
1
: This information was retrieved from “Goulet J; Lamarche B; Nadeau G and Lemieux S. Effect of a nutritional intervention promoting the Mediterranean food pattern on
plasma lipids, lipoproteins and body weight in healthy French-Canadian women. Atherosclerosis 2003; 170: 115-124”.
2
: Studies reporting on a controlled trial but without comparing controls and treatments in regard to the association of out-of-home eating and weight status
3
: This information was retrieved from the study by French et al. (2000) also included in this review.
Abbreviation: NR: not reported; WHA: Australian Longitudinal Study on Women’s Health; SUN: Seguimiento Universidad de Navarra (University of Navarro Follow-up);
CARDIA: Coronary Artery Risk Development in young Adults; EPIC-PANACEA: European Prospective Investigation Into Cancer-Physical activity, Nutrition, Alcohol,
Cessation of smoking, eating Out of Home and Obesity; SEASONS: Seasonal Variation of Blood Cholesterol Study; Add Health: National Longitudinal Study of Adolescent
Health.
17
Table 3. Summary of studies assessing the risk of becoming overweight or obese over time when eating out-of-home
Ref
Country
Baseline
Exposure
Follow-up
characteristics
(25) Spain
Outcome
Findings
length
27% ate OH ≥ 2 times/ week
Weekly frequency of away-
2
BMI (kg/m ): 23.1±3.6 (subjects
from-home meals
4.4 years on
average
Incidence
of
Eating out ≥ 1 time/ week associated
1
with a 1.22-1.33 higher risk of becoming
overweight/obesity
not eating out), 23.3±3.7 (eating
overweight/obese compared to not eating
out 1 time/week), 23.6±3.8 (eating
out(p<0.001)
out ≥ 2 times/ week), p<0.001
(21)
USA
29.7% meals OH
Frequency
2
of
breakfasts,
Men: mean BMI 28.6 kg/m , 48%
lunches and dinners eaten
overweight, 27% obese
OH
1 year
to
1st
quartile2,
Average BMI over
Compared
others
1 year
associated with 2.21-2.98 higher risk of
obesity for breakfast frequency, 1.89-
Women: mean BMI 26.6 kg/m2,
2.25 higher risk for dinner frequency, but
33% overweight, 20% obese
30-60% lower risk for lunch frequency
1
: participants with BMI < 25 kg/m at baseline and BMI ≥ 25 kg/m at follow-up, WHO references
2
: the frequency of eating each meal out-of-home was calculated by dividing the number of each meal out-of-home by the total number of days on which the meal was
2
2
consumed. Subjects were then separated into quartiles based on this frequency.
Abbreviation: OH: out-of-home
18
Table 4. Summary of studies evaluating the longitudinal association of bodyweight with out-of-home eating
Ref
Country
Baseline
Exposure
Follow-up
characteristics
(25)
Spain
Outcome
Finding
length
27% ate OH ≥ 2 times/ week
Weekly frequency of
4.4 ±1.7 years
Annual
Weight (kg): 65.5±12.7 (subjects
away-from-home
on average
change
not eating out), 67.2±13.5 (eating
meals
weight
Eating out 1 time/week and ≥2 times/week
associated respectively with 15g and 129g
weight gain per year (p<0.001) compared
out 1 time/week), 69.4±14.0 (eating
to
not
eating
out.
Also
associated
out ≥ 2 times/ week), p<0.001
respectively with 1.12 and 1.36 higher risk
of gaining ≥2 kg per year (p=0.001)
(22)
(19)
1
Denmark, Italy,
% EI from restaurant :
EI
France, Sweden,
Men: 3.6-12.4; Women: 3.0-7.2
eaters,
Germany,
UK,
% EI from work2:
restaurant,
Greece,
The
Men: 4.3-15.1; Women: 1.1-11.4
(kg/m2)3:
Netherlands,
BMI
Norway, Spain
Men: 23.4-28.4; Women:22.9-29.0
USA
weight
In men, weak positive but not significant
at
change (weight at
association of annual weight change and:
of
follow-up
minus
1) eating at restaurant with EI close to the
participants eating at
weight on dietary
average of restaurant eaters (β=+0.05,
restaurant
recall day, divided
p=0.368); 2) increase of 500 kcal in EI at
by follow-up time)
restaurant (β=+0.01, p=0.836)
Weight change over
1 time/week more fast-food consumption
13 years
associated with 0.15 ±0.05 kg weight gain over
Change
4
frequency
30.3% overweight, 23.2% obese4
restaurant
EI
%
and
1.1 to 9.4 years
at
work
4
1.9±2.5 times/week at fast-foods
2.3±3.2 times/week at restaurant
of
in
weekly
of
13 years
fast-
Annual
13 years and for restaurant food, this was
food and restaurant
0.09±0.04 kg
food consumption
(29)
(24)
USA
USA
Mean BMI: 29.1±6.5 kg/m2
Weekly frequency of
21% ate at fast-foods ≥ 1 time/ wk
fast-food visits
5
Mean fast-food use :
Weekly
fast-food
Black Men:2.4 times/week
frequency and change
Black Women:1.8 times/week
in weekly fast-food
1 year
15 years
Weight change over
1-2 visits per week to fast-foods associated
1 year
with 0.65 kg weight gain
Weight change over
Increase of 3 times/ week in 15-y fast-food
15 years
weekly frequency increased weight by 1.8
kg (p<0.0001) in White people
19
White Men: 2.4 times/week
frequency
White Women:1.6 times/week
Mean bodyweight6:
Black: 72.4-73.5 kg
White : 69.8-71.5 kg
(20)
USA
Mean BMI: 20.8±2.1 kg/m2
Weekly frequency of
Most subjects had BMI in normal
breakfast, lunch and
2
range (19.8-22.7kg/m )
12 weeks
Weight change over
Eating breakfast and lunch at all-you-can-
12 weeks
eat hall explains 10% of the variance of
dinner at all-you-can-
weight gain. Initial weight adjusted for,
eat hall, cash-op hall
eating lunch at a restaurant explained 5%
and restaurant
of the variance of weight gain and dinner
at a cash-op, 4% and variance explained
improved from 58 to 71%
(27)
USA
Mean weight : 72.8 kg
Weekly frequency of
2
Mean BMI: 27.0±6.0 kg/m
3 years
fast-food meals
Weight change over
Increase of 1 meal/week at fast-food
3 years
increases weight by 1.68 kg over 3 years
36.8% ate fast-food
≥ 2 times/week
1
: Mean contribution to daily energy intake of eating at restaurant, from a country to another
2
: Mean contribution to daily energy intake of eating at work, from a country to another
3:
Mean BMI, from a country to another
4
: Here, baseline values correspond to values measured at the 7 th year
5
: Significant difference between men and women, between Black and White
6
: bodyweight for Black and White people, varying across subgroups of subjects formed on the basis of weekly fast-food frequency
Abbreviation: OH: out-of-home
20
Table 5. Summary of studies assessing the longitudinal association of BMI and BMI z-score with out-of-home eating
Ref
Country
Baseline
Exposure
characteristics
Follow-up
Outcome
Findings
BMI
Compared to women never or rarely eating
length
BMI
(32)
Australia
68% ate takeaways≥1time/week
Frequency of eating takeaway
13.9% overweight and 5.9% obese
foods
4 years
category
(maintainers
versus gainers)
takeaway foods, those eating once a week
1
were 15% less likely to be weight
maintainers
(26)
Canada
(25) Spain
Mean BMI2: 25.4 to 25.8 kg/m2
Change in weight of fast-foods
Mean fast food intake: 51.7 g/ day
consumed over 12 weeks
Cf. table 3
Weekly frequency of away-fromhome meals
12 weeks
BMI
change
Change in fast-food consumption over
over 12 weeks
12weeks not correlated with BMI change
4.4 years on
Annual change
Eating ≥2 times/week associated with
average
in BMI
+0.07 kg/m2 BMI change per year
(p<0.001) compared to not eating out
(18)
USA
1.9±2.4 times/week at fast-foods3
Weekly frequency of fast-food
2.6±3.1 times/week at restaurant3
3
30.6% overweight, 23.3% obese
3 years
Change in BMI
Increased change in fast-food consumption
and restaurant food consumption
from year 7 to
increases 3-year change in BMI but not for
at years 7 and 10, change in the
year 10
restaurant food. Increase of fast-food use
frequencies from year 7 to year 10
of 1time/week increases mean BMI by
0.20 kg/m2.Increase of both fast-food and
restaurant associated with 0.29 kg/m2
increase in BMI
(28)
USA
Fast food (meals/week):
Number of fast-food meals per
Men: 2.2±2.0
week
1 year
1-year change in
No correlation between fast-food eating
BMI
and BMI over 1 year
HIW: 1.5±1.7; LIW: 1.7±1.7
BMI (kg/m2): men: 27.8±4.6
21
HIW: 25.9±4.9; LIW: 27.7±6.9
BMI z-score
(30)
UK
22% ate fast-food ≥ 2 times/week
Baseline weekly frequently of
8.2% obese4
eating at fast-food or take-away
15 years
BMI z-score at
Eating fast-food or takeaway meals twice
follow-up
or more a week at baseline increased BMI
outlets
(23)
USA
Mean age: 15.9y (SEM: 0.11)
2
Change in the number of days on
BMI: 22.9 kg/m (SEM: 0.12)
which fast-foods were consumed
28.7% overweight;10.9% obese
in the last 7 days
z-score by 0.14 to 0.21 unit
5 years
BMI z-score at
Change in fast-food consumption from
follow-up
baseline (adolescence) to follow-up (young
adulthood) did not predict BMI z-score at
follow-up
(31)
USA
Median age: 9 years
4% overweight and 0% obese
71% ate OH
Baseline weekly frequency and
5
1 to 10 years
Change in BMI
Eating at quick-service twice or more a
percent of weekly energy intake
z-score
from
week at baseline associated with greater
of quick-service, coffee-shop and
baseline
to
mean increase in BMI z-score (+0.82 unit)
restaurant foods
follow-up
than eating once a week (+0.20 unit) or not
at all (+0.28 unit)
1
:Maintainers were defined as women whose BMI at follow-up was within 5% of their baseline BMI. Gainers were women whose BMI at follow-up was more than 5%
greater than their baseline BMI
2
: Mean BMI, varying among four subgroups of subjects formed on the basis of medians of fast-food consumption and medians of changes in Mediterranean dietary
(arbitrary) score
3
: For this paper, baseline values are those measured at the 7th year
4:
5
defined as ≥ 95thBMI-for-age percentile of the UK 1990 growth reference
: defined respectively as 85 to 94.9th and ≥ 95thBMI-for- age percentile of the growth reference of the US Center for Disease Control and Prevention (CDC)
Abbreviation: OH: out-of-home; HIW: high-income women; LIW: low-income women; SEM: Standard error of the mean
22
Table 6. Summary of studies evaluating the longitudinal change in waist circumference when eating out
Ref
Country
Baseline
Exposure
Follow-up
characteristics
(26)
(19)
Canada
USA
Outcome
Findings
Change in WC over
Change in fast-food consumption over 12
12 weeks
weeks not correlated with change in WC
Change in WC over
1 time/week more fast-food consumption
13 years
associated with a 0.12 ±0.04 cm increase
length
Mean WC: 81.5 to 84.8 cm
Change in weight of fast-foods
Mean fast food intake: 51.7 g/ day
consumed over 12 weeks
1.9±2.5 times/week at fast-foods1;
Change in weekly frequency of
2.3±3.2 times/week at restaurant
1
1
30.3% overweight, 23.2% obese ;
fast-food
and
restaurant
12 weeks
13 years
food
consumption over 13 years
in WC over 13 years and for restaurant
WC:84.0±14.1 cm1
(29)
1
USA
food, this was 0.08±0.03 cm
2
Mean BMI: 29.1±6.5 kg/m ; 21%
Weekly frequency of fast-food
ate at fast-foods ≥1 time/week
visits
1 year
Change in WC over
1.06 cm increase in WC with 1-2
1year
visits/wk to fast-foods during 1 year
: Here, baseline values correspond to values measured at the 7 th year.
Abbreviation: OH: out-of-home; WC: waist circumference
23
Table 7. Summary of the methodology of the studies
Ref
Sampling
Representativeness
Measurements
Participation
Anthropometrics
Validity
rate
(32)
and
reliability tested
65%
Self-report
Only young women
(n=9657)
weight and height
on food habits,
at
at
baseline
of
No
and
Used
Questionnaire
No
94%
Measurement
sample
(n=72)
weight, height and
university
graduates
Males and females
Multivariate
Adjustment
for
logistic regression
occupation,
student
status, marital status,
parity and new parity
Self-reported
Convenience
Only
Under/over-reporting
and adjustment
baseline
of
NR
Quantitative
Yes
Mixed procedures
1
FFQ, IA, at
for
excluded
WC at baseline, 6
baseline, 6 and
measurements and
No
and 12 wk. Used
12 weeks
Tukey-Kramer
confounders
BMI and WC
(25)
Statistical method
only
BMI
Only urban women
Validity/reliability
of OH measure
National sample
follow-up.
(26)
Food intake
Analysis
repeated
over-reporter
adjustment
for
test
95%
Self-report
Least-squares
Under
(n=13373)
weight at baseline
quantitative
multivariate
reporters excluded
and every 2-year.
FFQ, SA, at
regression,
NR
baseline only
conditional
subjects included
measurement. Used
logistic regression
Adjustment for age,
weight
and
sex, smoking, fiber,
on
and
changes
incidence
overweight
of
height
BMI
and
of
and
obesity with WHO
Yes (for weight)
Semi-
Yes
non
Cox
Tested
proportional
alcohol
hazards analysis
intakes,
and
over-
similar
and
to
energy
education,
following special diet,
PA and baseline BMI
24
references
(30)
National sample
78%
Measurement
of
Standard
Males and females
(n=4461)
height and weight
methods
at 16years and self-
baseline).
report at 30. Used
No
BMI z-score with
report)
(for
Questionnaire
(at
self-
NR
Regression
of
Adjustment for BMI
on food habits,
BMI z-score at
z-score
at
30years on fast-
height at 16 and 30,
food/
sex and social class
baseline
only, SA
takeaway
at
16years,
eating at 16years
UK 1990 growth
references
(22)
Samples from 10
66%
Mainly
European countries
(n=24310)
of
self-report
height
No
and
Single-day 24h
Yes
dietary
Multivariate
Under
mixed-effects
reporters excluded
over-
linear regression
Adjustment for age,
General population
weight on day of
recall, mainly
but only women in
24-h
IA
France,
weight at follow-
status,
Naples (Italy) and
up.
BMI on dietary recall
Utrecht
change
Norway,
(The
recall,
Used
then
education,
weight
smoking
occupation,
day, follow-up time,
Netherlands)
(19)
and
total EI and PA
Males and females
81% (year 7)
Measurement
of
Standard
Questionnaire
Black and White
74%
(year
height, weight and
methods
on
From 4 US urban
10)*;
72%
WC at years 7, 10
habits, at years
areas
(year 20)
and
7, 10 and 20,
status,
IA
television
20.
Used
weight and WC
No
dietary
Fixed-effect
Adjustment for age,
longitudinal
education,
regression
structure,
family
smoking
hours
of
viewing,
total EI and PA
(29)
Convenience
94%
Measurement
of
Standard
Questionnaire
sample
(n=1145)
height, weight and
methods
on
dietary
Males and females,
WC, at baseline and
intake,
urban,
follow-up.
baseline
Used
at
and
No
Multilevel linear
Adjustment
for
regression
neighborhood-level
variables and residentlevel
variables
like
25
Incentives
weight and WC
follow-up, IA
age, sex, education,
smoking, BMI
(18)
Males and females
81% (year 7)
Measurement
of
Standard
Questionnaire
Black and White
79%
height and weight,
methods
on
From 4 US urban
10)*
(year
areas
No
dietary
Multivariate
Adjustment for race,
linear regression
sex, age, study center
at years 7 and10.
habits, at years
and year 7 education,
Used BMI and BMI
7 and 10, IA
income,
change
family
structure,
smoking
status, fast-food and
restaurant
frequency,
PA and EI
(23)
National sample
th
7-12 graders
67%
Mainly
(n=9919)
measurement
Males and females
of
Standard
Questionnaire
methods
on
dietary
height and weight,
habits,
at
baseline
baseline
follow-up.
and
Used
No
Multivariate
For baseline BMI z-
linear regression
score,
at
race/ethnicity,
sex, age, month of
and
follow-up, IA
interview,
maternal
obesity,
parental
BMI z-score with
education,
CDC
behavior, change in
growth
references
sedentary
sedentary
behavior,
PA
(24)
Males and females
74% at year
Measurement
of
Standard
Questionnaire
Black and White
15*
height, weight and
methods
on
dietary
No
Multivariate
Adjustment for sex,
linear regression
age,
study
centre,
From 4 US urban
WC at years 0, 2,
habits, at years
education,
areas
5,7, 10 and 15.
0, 2, 5,7, 10
weight
Used weight change
and 15, IA
alcohol consumption,
and
smoking
television
baseline
height,
status,
viewing,
26
dietary factors and PA
(20)
Convenience
88%
Measurement
of
Standard
Questionnaire
sample
(n=60)
weight at baseline
methods
on food habits
analysis
at
stepwise multiple
Freshmen from one
and
follow-up.
University
Used weight change
NR
follow-up
only
Correlation
regression
Males and females
Adjustment for initial
and
weight
with
maximal
R
improvement
(31)
Convenience
52%
Measurement
of
Standard
7-day dietary
sample
(n=101)
weight and height,
methods
records,
at
baseline
and
Girls only
at
baseline
Incentives
follow-up.
and
Analysis
of
variance adjusted
for
unbalanced
follow-up,
cell
size
BMI z-score with
self-reported
Duncan’s multiple
CDC
but probed by
range test.
Used
growth
references
(21)
No
Adjustment
for
baseline BMI z-score
and
telephone
Convenience
78%
Measurement
of
Standard
24-hour
sample
(n=503)
weight, at baseline
methods
dietary recall
Males and females
and 5 time points
Incentives
Measurement
No
Multivariate
Subjects
logistic regression
tested
excluded
similar
to
on 2 weekdays
subjects included
and 1 weekend
Adjustment for age,
height at baseline
day,
gender, education, EI
BMI averaged over
baseline and 5
all
of
measurement
time
days. Used average
BMI
with
at
and PA
†
points ,
IA.
WHO
references
(27)
Convenience
89%
Measurement
of
Standard
Questionnaire
sample
(n=891)
weight at baseline
methods
on food habits,
No
Multivariate
Adjustment
linear regression
baseline
for
fast-food
27
Women (including
and
low-income)
visit
each
Measurement
annual
of
IA, at baseline
frequency and weight
and
and
each
annual visit
marital
status,
ethnicity, income, age
height at baseline.
and treatment group
Used weight change
(28)
Convenience
86%
Measurement
of
Standard
FFQ, IA, at
sample
(n=1059)
height and weight
methods
baseline
Men and women
at
(including
follow-up.
income)
low-
baseline
and
Used
follow-up
and
No
Multivariate
Adjustment for age,
linear regression
education,
baseline
smoking and BMI and
treatment group
BMI
th
*: 74% at the 10 year represents the participation rate based on the whole cohort whereas 79% at the 10 th year is the participation rate based on the surviving cohort
(excluding dead people); 74% at the 15th year is also based on the surviving cohort.
†
: the frequency of eating each meal out-of-home was calculated by dividing the number of each meal out-of-home by the total number of days on which the meal was
consumed.
Abbreviation: WC: waist circumference; FFQ: food frequency questionnaire; IA: interviewer-administered; SA: self-administered; PA: physical activity; EI: energy intake
28
Articles identified from databases (n=7319):Medline (n=2859), CAB (n=200),
Cochrane (n=388), Web of Knowledge (n=2103), EMBASE (n=640),
AGRICOLA (n=833), INGENTA (n=220), Bioline International (n=76)
Doubles removed(n=1154)
Articles individually screened (n=6165)
Articles not eligible for review based on title
(n=2823)
Articles retained for abstract
screening (n=3342)
Articles excluded after reading of abstracts (n=3214): not on
humans (n=2); reviews (n=13); no dietary assessment (n=1642);
not about out-of-home eating (n=1304), not original studies
(n=215); cannot find paper (n=34); cross-sectional (n=4)
Articles retained for reading of full text (n=128)
Articles excluded after full reading (n=115)
Articles retained after full reading
(n=13)
Papers identified from screening
of references (n= 2)
Articles included in the review (n=15)
Figure 1. Flow chart of the screening
29
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