R E S E A R C H Open Access
Meal planning is associated with food
variety, diet quality and body weight status
in a large sample of French adults
, Caroline Méjean
, Vani Aroumougame
, Gladys Ibanez
, Benjamin Allès
, Serge Hercberg
and Sandrine Péneau
Background: Meal planning could be a potential tool to offset time scarcity and therefore encourage home meal
preparation, which has been linked with an improved diet quality. However, to date, meal planning has received
little attention in the scientific literature. The aim of our cross-sectional study was to investigate the association
between meal planning and diet quality, including adherence to nutritional guidelines and food variety, as well as
Methods: Meal planning, i.e. planning ahead the foods that will be eaten for the next few days, was assessed in
40,554 participants of the web-based observational NutriNet-Santé study. Dietary measurements included intakes of
energy, nutrients, food groups, and adherence to the French nutritional guidelines (mPNNS-GS) estimated through
repeated 24-h dietary records. A food variety score was also calculated using Food Frequency Questionnaire.
Weight and height were self-reported. Association between meal planning and dietary intakes were assessed using
ANCOVAs, while associations with quartiles of mPNNS-GS scores, quartiles of food variety score and weight status
categories (overweight, obesity) were evaluated using logistic regression models.
Results: A total of 57% of the participants declared to plan meals at least occasionally. Meal planners were more
likely to have a higher mPNNS-GS (OR quartile 4 vs. 1 = 1.13, 95% CI: [1.07–1.20]), higher overall food variety (OR
quartile 4 vs. 1 = 1.25, 95% CI: [1.18–1.32]). In women, meal planning was associated with lower odds of being
overweight (OR = 0.92 [0.87–0.98]) and obese (OR = 0.79 [0.73–0.86]). In men, the association was significant for
obesity only (OR = 0.81 [0.69–0.94]).
Conclusions: Meal planning was associated with a healthier diet and less obesity. Although no causality can
be inferred from the reported associations, these data suggest that meal planning could potentially be
relevant for obesity prevention.
Keywords: Meal planning, Diet quality, Food variety, Overweight, Cross-sectional study
In industrialized countries, eating habits and cooking prac-
cooking has decreased: in the United States, it has been re-
duced from 1:63 hour per day in 1965–1966 to 58 min in
2006–2007 . Additionally, the source of food consumed
has changed: people consume less food prepared at home,
whereas foods prepared away from home represent an in-
creasing part of the diet [2–4].
In light of this observation, a number of studies
have evaluated the potential impact of food prepared
away from home on dietary quality, as well as weight
status. These studies highlighted that the consump-
tion of food prepared away from home is associated
with a lower quality diet [5–8] and a higher body
mass index [9–11], whereas benefits have been attrib-
uted to home-prepared food [2, 12–14]. More
* Correspondence: email@example.com
Equipe de Recherche en Epidémiologie Nutritionnelle, Centre de Recherche
en Epidémiologie et Statistiques, Université Paris 13, Inserm (U1153), Inra
(U1125), Cnam, COMUE Sorbonne Paris Cité, Bobigny, France
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ducrot et al. International Journal of Behavioral Nutrition
and Physical Activity (2017) 14:12
frequent home food preparation has been associated
with better adherence to dietary objectives ,
higher intakes of fruits, vegetables [13, 14], fiber, fol-
ate and vitamin A, while lower intakes of fat in young
people . Therefore, home meal preparation has
been increasingly promoted as a strategy for improv-
ing dietary quality and preventing obesity [12–15].
In designing strategies to promote home cooking, it
is important to understand the patterns and correlates
of home meal practices. Many studies have investi-
gated the reasons why people cook less. Time scarcity
and cooking skills were identified as common barriers
to prepare home meals [6, 11, 12, 16, 17]. Previous
research emphasized that individuals with lower cook-
ing skills were more likely to consume away from
home food such as ready meals or take-out meals
from fast food or restaurants [11, 18]. In response to
these difficulties, a number of studies have evaluated
the opportunity to improve cooking skills in order to
promote healthy dietary patterns [19–21]. To face
time pressure, a series of qualitative studies
highlighted that parents resort to food choice coping
strategies, such as meal simplification, taking out, or
meal planning [16, 17, 22–26] despite their potential
impact on diet quality. Among these strategies, time
management skills  and in particular meal plan-
ning [28, 29], which consists in deciding ahead the
foods that will be eaten in the next few days, has
been previously suggested as a solution to balance
competing time demands and reduce barriers to
healthy dietary practices. In the literature, very few
studies have investigated meal planning practices and
they often focused on adequate diet for diabetic sub-
jects [30–32]. Studies performed on general popula-
tions showed that meal planning was positively
associated with frequencies of home food preparation
 and family meal , as well as the presence of
fruits for dinner . To our knowledge, only one
study in the literature has evaluated the potential link
between meal planning and food consumption. It fo-
cused on fruit and vegetables specifically, and showed
that planning meal ahead was associated with higher
fruit and vegetable intakes . However, the latter
presented weakness in the dietary intake assessment
method since it consisted only of questions on the
number of servings eaten per day. Additionally, meal
planning was evaluated, among various practices, as a
tool to maintain weight among successful weight
losers [36, 37] but no data exists on the potential re-
lationship with weight status in the general popula-
tion. In the present study, we hypothesize that meal
planning might encourage home meal preparation,
and therefore have beneficial effects on dietary quality
and consequently on weight status. Thus, we first
described meal planning practices among a large sam-
ple of individuals. Then, we investigated the relation-
ships between meal planning and diet quality, based
on adherence to nutritional guidelines, energy, macro-
nutrients and food group intakes, as well as food var-
iety. Finally, we evaluated the association between
meal planning and weight status.
The NutriNet-Santé study (http://info.etude-nutrinet-san-
te.fr) is an ongoing web-based prospective observational
cohort study launched in France in May 2009 with a
scheduled follow-up of 10 years. It aims to investigate the
relationship between nutrition and chronic disease risk, as
well as the determinants of dietary behavior and nutri-
tional status. The study was implemented in the French
general population (internet-using adult volunteers, aged
≥18 years). The rationale, design and methodology of the
study have been fully described elsewhere . In brief, to
be included into the study, participants have to complete a
baseline set of self-administered web-based questionnaires
assessing dietary intake, physical activity, anthropometric
characteristics, lifestyle, socioeconomic conditions and
health status. As part of the follow-up, participants are
asked to complete the same set of questionnaires each
year. Moreover, each month, participants are invited by e-
mail to fill in optional questionnaires related to dietary in-
take, determinants of eating behaviors, nutritional and
health status. This study is conducted in accordance with
the Declaration of Helsinki, and all procedures were ap-
proved by the Institutional Review Board of the French In-
stitute for Health and Medical Research (IRB Inserm n°
0000388FWA00005831) and the Commission Nationale
de l’Informatique et des Libertés (CNIL n°908450 and n°
909216). All participants provided informed consent with
an electronic signature. This study is registered in
Meal planning questionnaire
Meal planning practices were assessed via an optional
questionnaire launched in the NutriNet-Santé cohort
study in April 2014.
First, grocery shopping and cooking practices were
evaluated. In particular, participants were asked to indi-
cate whether they were involved in grocery shopping
(every day, several times a week, once a week, less than
once a week) and cooking (every day twice a day, every
day once a day, several times a week but not every day,
once a week, less than once a week, never) in their
household. Then, participants were asked the following
question “Generally, when do you choose the foods you
are going to eat for meal?”(just before meal, during the
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 2 of 12
day, the day before, few days before, one week before,
never). Participants responding “never”were exempted
to complete the rest of the questionnaire.
Participants were also asked whether having to think
about what they have to cook is a constraint for them.
The responses were rated on a 5-point Likert scale ran-
ging from one (strongly disagree) to five (strongly agree).
Participants were then asked whether they planned
meals, even in an irregular manner (yes I do, yes I did
but not anymore, no I never planned meals). The defin-
ition of “planning meals”given to the participants was
“to plan ahead the foods that will be eaten for the next
few days”. Participants who reported planning meal cur-
rently were considered as “meal planners”whereas
others were categorized as “non-meal planners”.
Finally, the questionnaire included questions about
meal planning frequency (several times a week, once a
week, once every two weeks, two to three times a
month, not regularly), duration (a few days, one week,
two weeks or more), period of the week (weekdays,
weekend, weekdays and weekend) and sources of inspir-
ation (personal recipe repertoire, Internet or apps, ingre-
dients available during grocery shopping).
Socio-demographic and economic characteristics
At baseline and annually thereafter, participants in the
NutriNet-Santé study are asked to provide socio-
demographic data, including sex, age (18–30, 30–50, 50–
65, >65 years), educational level (up to secondary, some
college or university degree), monthly income (<1,200 €,
1,200–1,800 €,1,800–2,700 €and >2,700 €per consump-
tion unit), presence of children in the household (yes, no),
history of dieting to lose weight during the past year (yes,
no) and physical activity (low, moderate, high). Monthly
household income is calculated per “consumption unit”
(CU), where one CU is attributed for the first adult in the
household, 0.5 CU for other persons aged 14 or older, and
0.3 CU for children under 14, following national statistics
methodology and guidelines .
Physical activity was assessed using a short form of
the French version of the International Physical Activity
Questionnaire (IPAQ). The weekly energy expenditure
expressed in metabolic equivalent task minutes per
week was estimated, and three scores of physical activ-
ity were constituted [i.e., low (<30 min/day), moderate
(30–59 min/day), and high (≥60 min/day)] according to
the French guidelines for physical activity .
For the present study, we used the closest available data
with respect to the assessment of meal planning practices.
At inclusion and once a year thereafter, participants are
invited to complete three non-consecutive 24-h dietary
records, randomly assigned over a 2-week period (two
weekdays and one weekend day). For the present ana-
lysis, we selected participants who completed at least
three 24-h dietary records since their inclusion in the
cohort study (i.e. completed between May 2009 and
December 2014). Participants reported all foods and
beverages consumed at each eating occasion. They esti-
mated the amounts eaten using validated photographs of
portion sizes , using household measures or by indi-
cating the exact quantity (grams) or volume (milliliters).
Daily mean food intakes were calculated, weighted for
the type of day of the week. Energy, nutrient and food
group intakes were estimated using the NutriNet-Santé
composition table including more than 2000 foods .
Dietary underreporting was identified on the basis of the
method proposed by Black . We hypothesize that
meal planning encourages food preparation and there-
fore considered food groups that can be used in food
preparation (e.g. eggs). In addition, we considered food
groups that have nutritional interest (e.g. fruits). Thus,
the following food groups were included in the study:
fruits, vegetables, fish (including seafood and processed
seafood), meat (including cooked ham, offal), eggs, milk,
cheese, added fats (including oil, butter, margarine, vin-
aigrette), sugary products (e.g. cake, biscuits, sugars,
honey, jam, chocolate) and starchy foods (including po-
tato, legumes, pasta, rice, other cereals) with a specific
focus on legumes and whole grain starchy foods (includ-
ing whole grain pasta, rice, other cereals).
Adherence to nutritional guidelines was assessed
using the PNNS Guideline Score (PNNS-GS). The 15-
point PNNS-GS is a validated a priori score reflecting
the adherence to the official French nutritional guide-
lines which has been extensively described elsewhere
. Details on computation of this score are in Add-
itional file 1. Briefly, it includes 13 components: eight
refer to food-serving recommendations (fruit and veg-
etables; starchy foods; whole grain products; dairy
products; meat, eggs and fish; fish and seafood; vege-
table fat; water vs. soda), four refer to moderation in
consumption (added fat; salt; sweets; alcohol) and one
component pertains to physical activity [44, 45].
Points are deducted for overconsumption of salt
(>12 g/day), added sugars (>17.5% of energy intake),
or when energy intake exceeds the needed energy
level by more than 5%. Each component cut-off was
that of the threshold defined by the PNNS public
health objectives when available  otherwise they
were established according to the French Recom-
mended Dietary Allowances . For the present ana-
lysis, we consider the mPNNS-GS, a modified version
of the PNNS-GS, which takes into account only the
dietary components, therefore excluding the physical
activity component. Thus, the maximum score was
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 3 of 12
Food variety score
Food variety has been defined as the number of different
food items reported to be eaten over a given reference
period . Considering that seasonality is likely to in-
fluence food variety and that a period of 10 to 15 days
has been recommended to accurately assess food variety
, the food variety was evaluated using a Food Fre-
quency Questionnaire (FFQ).
Sixteen months after baseline, participants were invited
to complete a self-administrated 240-items FFQ to assess
their usual dietary intake over the past year . Partici-
pants were asked to report their consumption frequency
on the basis of how many times they ate the standard por-
tion size proposed (typical household measurements such
as spoon or standard unit such as a yogurt). The fre-
quency of consumption referred to usual consumption
over the past year on an increasing scale including yearly,
monthly, weekly or daily units, as suitable, and partici-
pants were asked to provide only one answer.
The food variety score corresponded to the number of
FFQ items reported to be consumed at least once during
the last year . The maximum score was therefore
240. Fruit and vegetable variety scores were also com-
puted based on the number of different fruits and vege-
tables reported by the participants.
Height and weight were assessed by using an anthropo-
metric questionnaire, which was self-administered on-
line, at baseline and each year thereafter [50, 51]. For
each participant, the closest available data to the meal
planning questionnaire were used for the analysis.
Data were not collected for pregnant women. BMI (in
) was calculated as the ratio of weight to squared
height. Participants were classified as underweight or
normal weight (BMI < 25), overweight (25 ≤BMI < 30)
and obesity (BMI ≥30) according to WHO references
The analysis focused on participants who had completed
the meal planning questionnaire, had declared being in-
volved in meal preparation in their household, and who
had completed at least three 24-h dietary records since
they were included in the study, as well as the FFQ.
Chi-square tests and Student’sttests were used to
compare characteristics of included vs. excluded partici-
pants, as well as meal planners vs. non-meal planners.
Meal planners’practices were also described. Continu-
ous variables are presented as means ± SDs and categor-
ical variables as percentages.
ANCOVAs were performed to investigate the relation-
ship between meal planning and energy, macronutrients
and food groups. However, for some particular food
groups which did not exhibit normal distribution (i.e.
eggs, milk, legumes, and whole grain starchy foods),
mainly due to a high proportion of non-consumers, a bin-
ary variable (consumer/non-consumer) was created and a
logistic regression analysis was performed. Logistic regres-
sion models were also used to assess the associations be-
tween meal planning and quartiles of mPNNS-GS, as well
as quartiles of food variety scores (overall, fruit and vege-
table) and BMI categories. Due to significant interactions
and differences on the associations with meal planning,
analyses on BMI were performed separately by sex.
Meal planning has been described as a cooking skill
. Thus, characteristics that have been shown to influ-
ence cooking practices, dietary intakes or weight status
were considered as confounders in the present analyses.
Models were therefore all adjusted for sex [1, 54, 55],
age , educational level, monthly income , presence
of children in the household , history of dieting to
lose weight during the past year , physical activity
, and cooking frequency. Models evaluating the asso-
ciations with mPNNS-GS, macronutrient and food
groups intakes were further adjusted for daily energy in-
take and number of 24-h dietary records completed by
participants. The energy model was only adjusted on the
number of 24-h records while the food variety models
were adjusted on daily energy intake. Missing covariate
data were imputed using multiple imputation method.
Sensitivity analyses were conducted on a subsample of
individuals having responded to at least one of the diet-
ary assessments (i.e. FFQ, dietary records). In addition,
analyses were conducted using another definition of
food variety score (number of FFQ items reported to be
consumed more than once a week) .
All tests of statistical significance were two-sided and
the type I error was set at 5%. Statistical analyses were
performed using SAS software (version 9.3, SAS Institute
Inc, Cary, NC, USA).
Among the 102,703 participants in the NutriNet-Santé
study who received the meal planning questionnaire, a
total of 52,949 participants (i.e. 51.6%) completed it.
Among them, 1,754 were excluded because they de-
clared not being involved in meal preparation in their
household, 3,242 because of inadequate data in dietary
records (less than three 24-h dietary records or underre-
porting) and 7,399 because they did not complete the
FFQ, thus leading to a total of 40,554 participants avail-
able for analyses. Compared with excluded participants,
included subjects were more likely to be women, older,
to have a lower educational level, higher income, to have
children living in the household, to be physically active,
and less likely to have followed a diet to lose weight dur-
ing the past year (all P< 0.0001).
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 4 of 12
Our final sample comprised 78% of women and 22% of
men, with a mean age of 52.2 ± 14.2 years. Among the in-
cluded participants, 57.4% declared to plan their meals at
least occasionally whereas 42.6% did not, among which
17.3% planned in the past and 25.3% never planned meals.
Overall, the same proportions were observed in men
(meal planners: 55.9% vs. non-meal planners: 44.1%) and
women (meal planners: 57.8% vs. non-meal planners:
42.2%), but women were more likely to have planned
meals in the past compared to men (19.0% vs.11.3%).
Table 1 presents the sociodemographic and economic
characteristics of meal planners and non-meal planners,
as well as mean scores for mPNNS-GS, overall food
variety and overweight prevalence. Overall, differences be-
tween the two groups were relatively limited. Compared
with non-meal planners, individuals who plan meals were
slightly more likely to be women, older, to have a higher
educational level, a higher income, to have followed a diet
to lose weight during the past year and to be physically
active (all P<0.05).Theywerealsomorelikelytohave
higher mPNNS-GS and overall food variety scores and to
have a BMI < 25 kg/m
(all P< 0.0001).
Table 2 shows cooking practices in meal planners vs.
non-meal planners, as well as details regarding meal
practices among meal planners. Compared with non-
meal planners, individuals who plan meals cooked more
frequently. The majority of non-meal planners decided
what food to prepare during the day or just before meal
whereas meal planners reported to decide during the
day, the day before or few days before. Finally, thinking
about what food to prepare was less of a constraint for
meal planners than for non-meal planners (all P<
0.0001). Results among meal planners more specifically
showed that the majority of participants planned their
meals at least once a week. A non-negligible part
(14.8%) also reported to plan meals not regularly. Three-
quarters of participants planned meals for a few days,
but less than a week. Meals were mostly planned for
both weekdays and weekend. Most of the participants
planned meals according to personal recipe repertoire or
the ingredients available during grocery shopping.
Intake of energy, nutrients and food groups in meal
planners vs. non-meal planners are presented in Table 3.
Depending on the outcome, the percentage of explained
) in ANOVAs varied from 0.10 to 0.75. Over-
all very small differences in energy, macronutrient and
food group intakes were observed between meal plan-
ners and non-meal planners.
The associations between meal planning and quartiles
of mPNNS-GS, as well as quartiles of food variety score
are presented in Table 4.
Compared with non-meal planners, individuals who
planned their meals were more likely to belong to quar-
tiles 2, 3 and 4 of mPNNS-GS compared with quartile 1,
thus reflecting a higher adherence to nutritional guide-
lines. Similarly, compared with non-meal planners, meal
planners were also more likely to belong to quartiles 2, 3
and 4 of overall food variety, vegetable variety and fruit
variety compared with quartile 1, thus reflecting a higher
variety of the diet. For these models, the association of
predicted probabilities and observed responses indicated
percent concordant of 63.8 and 54.9%, respectively.Add-
itional analysis considering mPNNS-GS and variety
score as continuous variables revealed similar trends:
meal planners exhibited higher mPNNS-GS (7.92 ±
0.008 vs. 7.88 ± 0.009, P= 0.0001) and overall food var-
iety score (113.81 ± 0.16 vs. 112.20 ± 0.19, P< 0.0001)
compared to non-meal planners.
The logistic regression analysis performed between meal
planning and BMI classes is presented in Table 5. In
women, meal planning was associated with lower odds of
being overweight and obese, while in men, meal planning
was associated with lower odds of being obese only. For
this model, the association of predicted probabilities and
observed responses indicated a percent concordant of 70%.
Using a large population-based sample of individuals,
this study brought new insights about meal planning
practices and their relationship with dietary quality and
weight status. Meal planning was associated with better
adherence to nutritional guidelines and higher food var-
iety. Furthermore, planning meals was associated with
lower odds of being overweight and obese in women
and of being obese in men.
In our study, despite significant differences regarding
sociodemographic, economic and lifestyle characteristics
due to the large sample size, meal planners and non-meal
planners exhibited very similar profiles. In particular, no
significant difference in meal planning was observed in re-
lation with the presence of children in the household. This
result appears in contrast with previous qualitative studies
suggesting that the presence of children increases the feel-
ing of time scarcity [17, 23–26, 60] and therefore the need
of developing time-saving strategies, such as meal plan-
ning. However, fatigue and time scarcity can also decrease
the likelihood for following meal plans .
To our knowledge, this study is the first to describe
meal planning practices in a general population sample.
Overall, more than one out two participants revealed to
plan their meals at least occasionally. Generally, individ-
uals planned their meals several times a week, for a few
days period including weekdays and weekend, and get
inspiration mostly from their personal recipe repertoire
or ingredients available during grocery shopping. A pre-
vious survey evaluating Canadians’attitudes and habits
with regard to home food preparation highlighted that
about 40% of the participants decide what they will
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 5 of 12
Table 1 Sociodemographic, economic and lifestyle characteristics of meal planners vs. non-meal planners (N= 40,554 - NutriNet-
Non-meal planners Meal planners
N= 17,271 N= 23,283
% or means ± SD % or means ± SD P
Men 22.45 21.09
Women 77.55 78.91
18-30 7.24 6.80
30-50 33.56 33.66
50-65 35.80 35.07
> 65 23.40 24.46
Educational level <0.0001
Up to secondary 34.79 31.84
Some college 30.85 31.23
University degree 34.18 36.79
Missing data 0.18 0.15
Monthly income per household (€/UC
<1,200 11.50 9.17
1,200–1,800 22.22 20.48
1,800–2,700 25.18 25.10
>2,700 26.63 31.09
Missing data 14.48 14.16
Presence of child in the household 0.76
No 26.84 26.97
Yes 73.16 73.03
History of dieting to lose weight during the past year 0.0003
No 68.52 66.79
Yes 29.95 31.36
Missing data 1.53 1.84
Physical activity level <0.0001
High 30.13 32.32
Intermediate 35.52 37.15
Low 21.26 19.32
Missing data 13.09 11.21
7.84 ± 1.35 7.95 ± 1.34 <0.0001
Food variety score
141.43 ± 27.13 144.49 ± 26.01 <0.0001
<25 64.85 68.15
[25–30] 24.27 23.33
≥30 10.88 8.53
Boldface indicates statistical significance
Meal planners are individuals who “plan ahead the foods that will be eaten for the next few days”
On the basis of Student’s t or chi-square tests as appropriate
CU: Household Consumer Units. One CU is attributed for the first adult in the household, 0.5 for other persons aged 14 or older and 0.3 for children under 14
mPNNS-GS: adherence to nutritional guidelines score, based on 24-h dietary records, range 0–13.5
Food variety score: based on the food frequency questionnaire, range 0–240
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 6 of 12
prepare for dinner during the day, 27% the day before
and 33% at least two days before . However, the lat-
ter did not explore the modalities of meal planning.
Based on a large sample of general population, our
data support the notion that planning meal is indeed
associated with a better adherence to nutritional
guidelines and an increased food variety (overall,
fruits and vegetables). However, it should be noted
that only small differences were observed with energy,
macronutrient and food group intakes specifically. Al-
though meal planning has been previously suggested
as a potential tool to improve dietary quality [28, 29],
to our knowledge, no study in the literature has in-
vestigated this related association. Previous authors
highlighted that individuals deciding in advance what
to prepare for dinner were more likely to cook home-
made dishes . Since more frequent food preparation
has been linked with a better diet quality [12–14], this
could potentially explain the healthier diet observed
in meal planners. A few hypotheses can be made on
how meal planning could encourage home food prep-
aration. First, meal planning might address the issue
of not knowing what to prepare for dinner, that has
been previously described as a barrier for home meal
preparation . Second, by planning meals individ-
uals may think about recipes that can be prepared in
a limited period of time and, therefore reduce the
feeling of time scarcity, that may limit home meal
preparation [6, 12, 16] and increase the recourse to
food choice coping strategies such as eating out, de-
livery meals or ready prepared food [17, 23–25]. In
addition, planning meals may reduce the risk of miss-
ing ingredients for home meal preparation which
could also lead to the consumption of food prepared
away from home. Finally, deciding what foods will be
eaten in the next few days could also enable individ-
uals to cook more diversified recipes and to anticipate
grocery shopping for the specific ingredients needed,
thus potentially explaining the increased food variety
observed in meal planners. However, reverse causality
cannot be excluded since individuals interested in
having a healthy diet might be more likely to plan
their meals. In line with this idea, meal-planners in
tional level, to have higher income, to be physically
active and to have a lower BMI, characteristics that
have been related with a better attitude towards
healthy eating . Differences were however limited.
Our results showed that women who planned meals
were less likely to be overweight or obese, while in men,
there was an association with obesity only. Since meal
planners have a diet of higher quality, it potentially pre-
vents overweight in these individuals . However, we
cannot exclude reverse causality. People attaching more
importance to food and weight management might be
more likely to plan their meals. In line with this hypoth-
esis, two studies in the literature highlighted that meal
Table 2 Cooking practices and meal planning practices
(N= 40,554 - NutriNet-Santé 2014)
N= 17,271 N= 23,283
Cooking frequency <0.0001
Every day, twice a day or more 28.79 33.60
Every day, once a day 34.70 37.06
Several times a week 27.87 25.30
Once a week or less 7.14 3.54
Never 1.51 0.50
Time of meal choice decision <0.0001
One week before 0.32 7.69
Few days before 6.02 28.54
The day before 21.46 25.63
During the day 41.72 26.27
Just before meal 30.48 11.87
Having to think about what to
cook is a constraint
Strongly agree 9.45 4.33
Agree 29.52 23.62
Neither agree nor disagree 27.40 27.04
Disagree 18.69 23.46
Strongly disagree 14.94 21.54
Meal planning frequency
Several times a week 46.39
Once a week 34.72
Two weeks per month or less 4.07
Not regularly 14.81
Meal planning duration
Two weeks or more 1.18
One week 19.77
A few days 79.05
Meal planning period
Weekdays and weekend 68.15
Sources of inspiration
Personal recipe repertoire 41.15
Internet, apps for meal planning 2.53
Ingredients available during
Boldface indicates statistical significance
On the basis of chi-square tests
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 7 of 12
planning is more frequently used by successful weight
loss maintainers compared to those who did not main-
tain weight losses [36, 37].
In terms of public health, our results bring supportive
insights that promoting meal planning might encourage
the preparation of healthier and more varied home meals.
Previous studies showed that parents would be interested
in learning how to plan meals [28, 60], however, other
findings suggested that meal planning is also perceived as
complex and time consuming . Specific tools might
assist people in managing meal planning but to be
adopted and sustainable over time, it is important to iden-
tify consumers’needs. The present data highlighted that
there are various ways of planning meals. As an example,
we observed that the ingredients available during grocery
shopping are likely to influence meal planning while exist-
ing tools rather propose menus to plan grocery shopping.
Strengths and limitations
A major strength of this study was its large sample size
allowing an evaluation of meal planning practices at a
population level. The wide range of socio-economic and
lifestyle variables collected through the web-based plat-
form enables the control of potential effects of con-
founding factors. In addition, the web-based tool used to
assess 24-h dietary records has shown a good validity in
prior studies [63, 64]. However, because of the influence
of seasonality on food variety, the FFQ was used to
evaluate dietary variety since it allows usual intake esti-
mates over a relatively long period of time .
Table 3 Energy, nutrients and food group intakes in meal planners vs. meal planners (N= 40,554 - NutriNet-Santé 2014)
Non-meal planners Meal planners Non-meal planners Meal planners
N= 17,271 N= 23,283 N= 17,271 N= 23,283
Energy Mean ± SE Mean ± SE PMean ± SE Mean ± SE P
Energy (kcal/d) 1867.11 ± 3.22 1865.01 ± 2.77 0.62 1869.78 ± 2.78 1863.03 ± 2.39 0.068
Nutrients Mean ± SE Mean ± SE PMean ± SE Mean ± SE P
Lipids (g/d) 80.23 ± 0.17 80.15 ± 0.14 0.69 80.21 ± 0.08 80.17 ± 0.07 0.76
Saturated fatty acids (g/d) 32.76 ± 0.08 32.9 ± 0.07 0.17 32.72 ± 0.05 32.93 ± 0.04 0.001
Proteins (g/d) 77.3 ± 0.14 77.69 ± 0.12 0.038 77.22 ± 0.09 77.74 ± 0.08 <0.0001
Carbohydrates (g/d) 193.23 ± 0.39 191.98 ± 0.34 0.015 193.16 ± 0.22 192.04 ± 0.19 0.0001
Sugars (g/d) 90.21 ± 0.22 90.63 ± 0.19 0.14 90.27 ± 0.17 90.58 ± 0.14 0.17
Food groups Mean ± SE Mean ± SE PMean ± SE Mean ± SE P
Fruits (g/d) 195.31 ± 0.97 202.15 ± 0.83 <0.0001 197.43 ± 0.92 200.58 ± 0.79 0.011
Vegetables (g/d) 303.78 ± 1.11 317.76 ± 0.96 <0.0001 307.68 ± 1.05 314.86 ± 0.91 <0.0001
Fish (g/d) 69.39 ± 0.35 71.24 ± 0.3 <0.0001 70.3 ± 0.33 70.57 ± 0.29 0.56
Meat (g/d) 116.87 ± 0.44 118.7 ± 0.38 0.0016 117.29 ± 0.41 118.4 ± 0.35 0.040
Cheese (g/d) 35.52 ± 0.17 35.8 ± 0.15 0.22 35.53 ± 0.16 35.8 ± 0.13 0.18
Starchy foods (g/d)
227.81 ± 0.83 225.56 ± 0.71 0.040 230.41 ± 0.73 223.63 ± 0.63 <0.0001
Added fats (g/d) 47.17 ± 0.16 48.03 ± 0.14 <0.0001 47.35 ± 0.14 47.89 ± 0.12 0.0032
Sugary products (g/d)
145.71 ± 0.57 146.01 ± 0.49 0.69 146.6 ± 0.48 145.34 ± 0.42 0.050
Ref OR[95% CI] PRef OR[95% CI] P
Eggs 1 1.17 [1.10;1.23] <0.0001 1 1.00 [0.94;1.06] 0.93
Milk 1 1.11 [1.06;1.17] <0.0001 1 0.97 [0.92;1.01] 0.12
Legumes 1 1.09 [1.04;1.13] <0.0001 1 0.96 [0.92;1.00] 0.070
Whole grain starchy foods
1 1.04 [1.00;1.09] 0.062 1 1.04 [0.99;1.09] 0.14
Boldface indicates statistical significance
Pare based on ANCOVA models adjusted for sex, age, educational level, monthly income per household, presence of children in the household, history of
dieting to lose weight during the past year, physical activity, cooking frequency, and number of dietary records
Pare based on ANCOVA models adjusted for sex, age, educational level, monthly income per household, presence of children in the household, history of
dieting to lose weight during the past year, physical activity, cooking frequency, number of dietary records, and daily energy intake
Pare based on logistic regression models adjusted for sex, age, educational level, monthly income per household, presence of children in the household, history
of dieting to lose weight during the past year, physical activity, cooking frequency, number of dietary records, and daily energy intake
Total starchy foods includes potato, legumes, pasta, rice, other cereals, flour and whole grain forms
Sugary products includes foods with high sugar content such as cake, biscuits, sugars, honey, jam, chocolate
Whole grain starchy foods includes whole grain forms of pasta, rice, other cereals, and flour
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 8 of 12
This study was also subject to several limitations. First,
the cross-sectional design of this study prevented any in-
ference of causality. Moreover, since participants were
volunteers in a nutrition focused cohort, they may have
higher health consciousness and interest in nutritional
issues. The fact that we selected only participants who
completed both dietary assessment tools might have
exacerbated this characteristic in our sample. Therefore,
caution is needed when generalizing our results. How-
ever, sensitivity analyses including individuals with at
least one of the dietary assessments (24-h dietary re-
cords or FFQ) revealed similar trends. Besides, the fact
that participants had relatively high knowledge in nutri-
tion could potentially account for the few differences ob-
served in energy and food group intakes, since they may
be able to cook healthful meals without planning meals.
It is also important to consider that food variety score
was based on FFQ data, which has been recorded at dif-
ferent time frames (16 months after the inclusion in the
cohort). Thus, for participants included since a long
time in the cohort study, the estimation may not repre-
sent their current dietary repertoire. In addition, data
were self-reported, thus potentially leading to misreport-
ing due, for example, to desirability bias. Nonetheless,
previous validation studies performed on a subsample of
the NutriNet-Santé study have supported the good valid-
ity of self-reported anthropometric and dietary data [63,
64, 66]. Finally, given that meal planning may be influ-
enced by numerous parameters such as cooking prac-
tices and food availability in the surrounding, it is
possible that some factors mediating the associations ob-
served in the present paper were not taken into account
in the analyses. The potential impact of cooking prac-
tices was however considered by adding cooking fre-
quency as a confounder. Future research should be
conducted to address the issue of how food availability
could potentially influence the relationship observed be-
tween meal planning and diet quality.
Our results highlighted that individuals planning their
meals were more likely to have a better dietary quality,
including a higher adherence with nutritional guidelines
as well as an increased food variety. Additionally, meal
planning was associated with lower odds of being obese
in men and women and overweight in women only. Al-
though interventional or prospective research should be
conducted in order to infer causality, these data suggest
the potential interest of promoting meal planning to
Table 5 Logistic regression analysis showing the association between meal planning and weight status in men and women (N=
40,554 - NutriNet-Santé 2014)
Men (N= 8,788) Women (N= 31,766)
Univariable Multivariable Univariable Multivariable
OR [95% CI] POR [95% CI] P
OR [95% CI] POR [95% CI] P
BMI<25 1 1 1 1
[25–30] 0.98 [0.89;1.07] 0.60 1.00 [0.90;1.10] 0.93 0.91 [0.86;0.96] 0.0005 0.92 [0.87;0.98] 0.0081
≥30 0.78 [0.68;0.90] 0.0008 0.81 [0.69;0.94] 0.0065 0.74 [0.69;0.80] <0.0001 0.79 [0.73;0.86] <0.0001
The modeled probability was the fact to plan meals
Adjusted for sex, age, educational level, monthly income per household, presence of children in the household, history of dieting to lose weight during the past
year, physical activity, and cooking frequency
Table 4 Multinomial logistic regression analysis showing the
association between meal planning and adherence to nutritional
guideline score (mPNNS-GS) and food variety score (N=40,554-
OR [95% CI] POR [95% CI] P
Adherence to nutritional guidelines (mPNNS-GS score)
Q1 (<6.91) 1 1
Q2 ([6.91–7.83]) 1.10 [1.04;1.17] 0.0006 1.06 [1.00;1.13] 0.039
Q3 ([7.83–8.8]) 1.16 [1.10;1.23] <0.0001 1.10 [1.03;1.16] 0.0022
Q4 (≥8.8) 1.23 [1.16;1.30] <0.0001 1.13 [1.07;1.20] <0.0001
Food variety score
Q1 (<127) 1 1
Q2 ([127–146]) 1.15 [1.09;1.22] <0.0001 1.15 [1.08;1.22] <0.0001
Q3 ([146–162]) 1.28 [1.21;1.36] <0.0001 1.16 [1.10;1.23] <0.0001
Q4 (≥162) 1.34 [1.27;1.42] <0.0001 1.25 [1.18;1.32] <0.0001
Q1 (<20) 1 1
Q2 ([20–23]) 1.22 [1.15;1.29] <0.0001 1.18 [1.11;1.25] <0.0001
Q3 ([23–25]) 1.33 [1.26;1.41] <0.0001 1.24 [1.17;1.32] <0.0001
Q4 (≥25) 1.38 [1.3;1.46] <0.0001 1.21 [1.14;1.28] <0.0001
Q1 (<15) 1 1
Q2 ([15–17]) 1.13 [1.06;1.20] <0.0001 1.07 [1.01;1.13] 0.032
Q3 ([17–19]) 1.21 [1.14;1.28] <0.0001 1.12 [1.06;1.19] 0.0002
Q4 (≥19) 1.23 [1.17;1.31] <0.0001 1.12 [1.06;1.19] <0.0001
Boldface indicates statistical significance
The modeled probability was the fact to plan meals
Adjusted for sex, age, educational level, monthly income per household,
presence of children in the household, history of dieting to lose weight during
the past year, physical activity cooking frequency, and daily energy intake
Ducrot et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:12 Page 9 of 12
improve dietary quality and prevent overweight. Such a
tool could partly address the issue of time scarcity re-
ported by consumers for meal preparation and, might
therefore encourage home cooking. Given the potential
benefits of meal planning identified in this study, it
would be interesting that future research evaluate the
appropriation and the impact of applications designed to
help individuals planning their meals.
Additional file 1: Table S1. French Nutrition and Health Program-
Guideline Score (PNNS-GS) computation. (DOCX 16 kb)
CU: Consumption Units; FFQ: Food Frequency Questionnaire;
mPNNS-GS: Modified Programme National Nutrition Santé-Guideline Score
We thank all scientists, dieticians, technicians, and assistants who help carry
out the NutriNet-Santé study. We especially thank Younes Esseddik, Yasmina
Chelghoum, Mohand Ait Oufella, Paul Flanzy and Thi Hong Van Duong,
computer scientists; Veronique Gourlet, Charlie Menard, Fabien Szabo,
Nathalie Arnault, Laurent Bourhis and Stephen Besseau, statisticians; and the
dieticians. We are grateful to volunteers from the NutriNet-Santé study.
The NutriNet-Santé Study is supported by the French Ministry of Health (DGS), the
French Institute for Public Health Surveillance (InVS), the French National Institute
for Health and Medical Research (INSERM), the French National Institute for
Agricultural Research (INRA), the Medical Research Foundation (FRM), the National
Conservatory for Arts and Crafts (CNAM), the National Institute for Prevention and
Health Education (INPES) and the University of Paris 13. This study is supported by
the National Institute for Prevention and Health Education (INPES).
Availability of data and materials
In France, there is a very strict regulation concerning the protection of personal
data and privacy making difficult the availability of data (even non-nominal data).
PD: conducted the literature review, drafted the manuscript and performed
analyses; CM, VA, GI, BA, EKG, SH and SP: were involved in the interpretation
of results and critically reviewed the manuscript; and SH and SP: were
responsible for the development of the design and the protocol of the
study. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
This study is conducted in accordance with the Declaration of Helsinki, and all
procedures were approved by the Institutional Review Board of the French
Institute for Health and Medical Research (IRB Inserm n°0000388FWA00005831)
and the Commission Nationale de l’Informatique et des Libertés (CNIL n°908450
and n°909216). All participants provided informed consent with an electronic
signature. This study is registered in EudraCT (n°2013-000929-31).
Equipe de Recherche en Epidémiologie Nutritionnelle, Centre de Recherche
en Epidémiologie et Statistiques, Université Paris 13, Inserm (U1153), Inra
(U1125), Cnam, COMUE Sorbonne Paris Cité, Bobigny, France.
de médecine générale, faculté de médecine Pierre et Marie Curie, UPMC
Université Paris 6, 27, rue de Chaligny, 75012 Paris, France.
Santé Publique, Hôpital Avicenne, Bobigny Cedex, France.
Received: 4 July 2016 Accepted: 5 January 2017
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