Frequency of eating home cooked meals and potential benefits for diet and health: Cross-sectional analysis of a population-based cohort study

Abstract

Background:
Reported associations between preparing and eating home cooked food, and both diet and health, are inconsistent. Most previous research has focused on preparing, rather than eating, home cooked food; used small, non-population based samples; and studied markers of nutrient intake, rather than overall diet quality or health. We aimed to assess whether frequency of consuming home cooked meals was cross-sectionally associated with diet quality and cardio-metabolic health.

Methods:
We used baseline data from a United Kingdom population-based cohort study of adults aged 29 to 64 years (n = 11,396). Participants self-reported frequency of consuming home cooked main meals. Diet quality was assessed using the Mediterranean Diet Score, Dietary Approaches to Stop Hypertension (DASH) score, fruit and vegetable intake calculated from a 130-item food frequency questionnaire, and plasma vitamin C. Markers of cardio-metabolic health were researcher-measured body mass index (BMI), percentage body fat, haemoglobin A1c (HbA1c), cholesterol and hypertension. Differences across the three exposure categories were assessed using linear regression (diet variables) and logistic regression (health variables).

Results:
Eating home cooked meals more frequently was associated with greater adherence to DASH and Mediterranean diets, greater fruit and vegetable intakes and higher plasma vitamin C, in adjusted models. Those eating home cooked meals more than five times, compared with less than three times per week, consumed 62.3 g more fruit (99% CI 43.2 to 81.5) and 97.8 g more vegetables (99% CI 84.4 to 111.2) daily. More frequent consumption of home cooked meals was associated with greater likelihood of having normal range BMI and normal percentage body fat. Associations with HbA1c, cholesterol and hypertension were not significant in adjusted models. Those consuming home cooked meals more than five times, compared with less than three times per week, were 28% less likely to have overweight BMI (99% CI 8 to 43%), and 24% less likely to have excess percentage body fat (99% CI 5 to 40%).

Conclusions:
In a large population-based cohort study, eating home cooked meals more frequently was associated with better dietary quality and lower adiposity. Further prospective research is required to identify whether consumption of home cooked meals has causal effects on diet and health.

Full text

R E S E A R C H Open Access
Frequency of eating home cooked meals
and potential benefits for diet and health:
cross-sectional analysis of a population-
based cohort study
Susanna Mills
1*
, Heather Brown
1
, Wendy Wrieden
2
, Martin White
1,3
and Jean Adams
3
Abstract
Background: Reported associations between preparing and eating home cooked food, and both diet and health,
are inconsistent. Most previous research has focused on preparing, rather than eating, home cooked food; used
small, non-population based samples; and studied markers of nutrient intake, rather than overall diet quality or
health. We aimed to assess whether frequency of consuming home cooked meals was cross-sectionally associated
with diet quality and cardio-metabolic health.
Methods: We used baseline data from a United Kingdom population-based cohort study of adults aged 29 to
64 years (n= 11,396). Participants self-reported frequency of consuming home cooked main meals. Diet quality was
assessed using the Mediterranean Diet Score, Dietary Approaches to Stop Hypertension (DASH) score, fruit and
vegetable intake calculated from a 130-item food frequency questionnaire, and plasma vitamin C. Markers of cardio-
metabolic health were researcher-measured body mass index (BMI), percentage body fat, haemoglobin A
1c
(HbA
1c
),
cholesterol and hypertension. Differences across the three exposure categories were assessed using linear
regression (diet variables) and logistic regression (health variables).
Results: Eating home cooked meals more frequently was associated with greater adherence to DASH and
Mediterranean diets, greater fruit and vegetable intakes and higher plasma vitamin C, in adjusted models. Those eating
home cooked meals more than five times, compared with less than three times per week, consumed 62.3 g more fruit
(99% CI 43.2 to 81.5) and 97.8 g more vegetables (99% CI 84.4 to 111.2) daily. More frequent consumption of home
cooked meals was associated with greater likelihood of having normal range BMI and normal percentage body fat.
Associations with HbA
1c
, cholesterol and hypertension were not significant in adjusted models. Those consuming
home cooked meals more than five times, compared with less than three times per week, were 28% less likely to have
overweight BMI (99% CI 8 to 43%), and 24% less likely to have excess percentage body fat (99% CI 5 to 40%).
Conclusions: In a large population-based cohort study, eating home cooked meals more frequently was associated
with better dietary quality and lower adiposity. Further prospective research is required to identify whether
consumption of home cooked meals has causal effects on diet and health.
Keywords: Home cooking, Diet, Cardio-metabolic health
* Correspondence: susanna.mills@newcastle.ac.uk
1
Institute of Health & Society, Newcastle University, Baddiley-Clark Building,
Richardson Road, Newcastle upon Tyne NE2 4AX, UK
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.
Mills et al. International Journal of Behavioral Nutrition
and Physical Activity (2017) 14:109
DOI 10.1186/s12966-017-0567-y
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Background
Theprevalenceofobesityanddiet-relatednon-
communicable diseases (NCDs), such as type II diabetes,
hypertension, and certain cancers, have been increasing
steadily worldwide [1]. These changes have been accompan-
iedbyadecreaseinthetimespentcookingathomeinthe
majority of developed countries [2, 3]. Concern has been
expressed by policy makers, practitioners and researchers in
the field of food and nutrition regarding a perceived decline
in cooking skills, which has been hypothesised to be linked
to the increase in diet-related NCDs [4–6].
Certain studies, primarily cross-sectional in design, have
indicated that a higher frequency of cooking and prepar-
ing food at home may be associated with consuming a
healthier diet [7–9] and benefits to health and longevity
[10–12]. In contrast, other cross-sectional research has
suggested that home food preparation and cooking may
be associated with diets lower in fibre and higher in fat,
saturated fat, sugar, and salt [13, 14] and could potentially
be detrimental to health [15, 16]. Adding to this confu-
sion, the majority of research to date has used cooking
and food preparation practices as an exposure, rather than
the consumption of home cooked food itself. Since eating
food is more proximal to potential diet and health out-
comes, focusing on behaviour upstream may be more
likely to introduce confounding, for example regarding
gender –given that more women than men engage in
food preparation [3], and women tend to have healthier
diets [17]. Of key primary interest therefore is establishing
whether consuming home cooked meals is associated with
benefits to diet and health, and subsequently investigating
who eats home cooked meals, and then who prepares
these meals and why.
To date, research investigating the potential advan-
tages and disadvantages for diet and health of cooking
and preparing food at home has generally focused on
specific dietary indicators, rather than overall diet quality
or health, and assessed measures cross-sectionally or
after a brief follow-up period [18]. Most studies have
been small in size, with associated limited scope to iden-
tify significant associations [8]; limited to a specific geo-
graphical area [19]; and/or restricted to population
subgroups by for example age [10, 20] or ethnicity [11].
Despite the fact that the evidence base for relationships
between cooking and both diet and NCDs is mixed and
inconclusive, the promotion of home cooking forms part
of public health strategies to improve diets and reduce
obesity and diet-related NCDs internationally [21]. Fur-
ther research is therefore crucial, to investigate on a large
scale the potential associations between consumption of
home cooked meals and diet and health outcomes.
Inthisstudyweaimedtoassess whether the consump-
tion frequency of home cooked meals was cross-sectionally
associated with indicators of diet and cardio-metabolic
status. In view of the current evidence base, we hypothe-
sised that eating home cooked meals more frequently
would be associated with markers of a healthier diet and
improved cardio-metabolic health.
Methods
Data source
The Fenland Study is a population-based cohort study in-
vestigating interactions between genetic and lifestyle factors
in determining obesity and diabetes. The study recruited
adults born between 1950 and 1975 from general practice
lists in Cambridgeshire, United Kingdom (UK), between
2005 and 2015 [22]. Participants were invited to attend one
of three clinical sites in Cambridgeshire to take part in a
detailed assessment. A total of 12,434 participants under-
took baseline assessment (approximate response rate 27%),
which involved a range of clinical, biological and anthropo-
metric measurements, and completion of questionnaires.
The data collection tools are available online [23].
Study exclusion criteria included previously diagnosed
diabetes, psychosis, terminal illness, pregnancy, and
inability to walk unaided. The Fenland study was
approved by the Health Research Authority National
Research Ethics Service Committee –East of England
Cambridge Central –and performed in accordance with
the Declaration of Helsinki. All participants provided
written informed consent to participate in the study.
Frequency of consumption of home cooked meals
Exposure was derived from an item in the participant ques-
tionnaire: ‘When eating your main meal at home, how
often do you usually eat home cooked meals?’Response
categories were: never or rarely; one to two times per week;
three to five times per week; or more than five times per
week. The first two response categories were collapsed to
yield appropriate numbers for statistical analysis, as previ-
ously [24], giving a three category variable: less than three
times per week, three to five times per week, and more
than five times per week.
Indicators of diet quality
We assessed a range of dietary outcome variables, namely
Mediterranean Diet Score (MDS) [25], Dietary Approaches
to Stop Hypertension (DASH) score [26], plasma vitamin
C, and fruit and vegetable intakes. Participants completed a
130-item, semi-quantitative food frequency questionnaire
(FFQ) for their food intake over the previous year [27],
which has been shown to yield valid and reproducible food
intake assessments, and has been validated previously in
dietary data collection in the European Prospective Investi-
gation into Cancer and Nutrition (EPIC) studies [28]. The
FFQ EPIC Tool for Analysis was used to convert food in-
take frequency to energy, nutrient and food intakes [29].
Total daily intake was provided in grams for carbohydrate,
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 2 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

fibre, fat, saturated fat, sugar, protein, fruit, vegetables and
alcohol. Total daily sodium intake was measured in milli-
grams, and total daily energy intake in kilojoules. Dietary
intake values were winsorized at 1st and 99th percentiles,
by replacing the smallest and largest percentage values in
the distribution with the observations closest to them [30].
This was undertaken to account for their positively skewed
distribution, and the limitations of the FFQ as a tool to col-
lect precise data on dietary intake [31, 32]. Data on dietary
supplements were not collected.
The consumption of a more DASH accordant diet is
associated with positive health indicators and lowered
cardio-metabolic risk [33–35]. The DASH diet assumes
that beneficial impact is derived from the overall diet, ra-
ther than individual foods or nutrients playing important
roles [36]. A DASH score was computed from each par-
ticipant’s dietary intake using the method developed by
Fung et al. [26]. This index includes eight components
(one nutrient and seven food groups) based on eating
guidance from the United States (US) National Heart,
Lung and Blood Institute [37]. Scoring is established
through quintile rankings, on the basis of relative com-
parisons to the rest of the sample, with men and women
classified separately. Participants are allocated a score
from one (lowest quintile) to five (highest quintile) for
energy-adjusted intake of: low-fat dairy products; whole
grains; nuts, seeds and legumes; fruit (includes fruit
juice); and vegetables (excludes potatoes). In contrast,
for intakes of red and processed meat; sodium; and
sugar-sweetened beverages, participants are allocated a
score from one (highest quintile) to five (lowest quintile).
Scores are then combined to give a total DASH score,
ranging from a minimum of eight to a maximum of 40
points. In this study, DASH scores were standardised
using the z-score, to yield a semi-continuous measure of
participants’relative standing.
The Mediterranean diet is generally considered to be
low in consumption of red meats, moderate in con-
sumption of fish, poultry, fermented dairy products and
wine, and high in consumption of fruits, legumes, cereals
and olive oil [38, 39]. Concordance with the Mediterra-
nean diet has been linked with positive health outcomes,
in particular the primary prevention of cardiovascular
disease [40]. A Mediterranean diet score (MDS) was cal-
culated from each participant’s dietary intake using sex-
specific tertiles, according to relative comparisons with
the rest of the sample. Scores of zero, one or two were
allocated for each of nine dietary components, including
legumes; fruit and nuts; vegetables; ratio of monounsatu-
rated and polyunsaturated fatty acids to saturated fatty
acids; fish; meat products; dairy products; cereals; and
alcohol [25]. In order to appraise quality of diet inde-
pendent of quantity, dietary intakes were adjusted to a
2000 kcal/day diet using the residual method. This also
aimed to help reduce measurement errors, since energy
intake is partially associated with over-reporting and
under-reporting of dietary intake [41]. MDS scores were
then standardised using the z-score.
Plasma vitamin C (μmol/l) provides an objective biomarker
of fruit and vegetable consumption [42] and fruit and vege-
table intake is promoted in dietary guidelines [43, 44]. Fasting
venous blood samples drawn into heparin-containing tubes
and stabilised using metaphosphoric acid (10%) were mea-
sured for plasma vitamin C levels by fluorometric assay within
two months, as undertaken previously [45].
Markers of cardio-metabolic health
We used body mass index (BMI), percentage body fat,
haemoglobin A
1c
(HbA
1c
), cholesterol and hypertension
as indicators of cardio-metabolic health. Elevated total
cholesterol and low levels of high density lipoprotein
cholesterol (HDL) are associated with increased risk of
cardiovascular disease [46], and the derived ratio of total
cholesterol to HDL is used in the QRISK2 model to esti-
mate risk of cardiovascular disease over the next ten
years [47]. HDL and total cholesterol were measured in
mmol/l in fasting venous blood samples, and the ratio of
total cholesterol to HDL calculated for analysis. In line
with UK guidance, a ratio of 4.0 or greater was used to
indicate higher risks to cardio-metabolic health [48].
Excess body fat and raised BMI have been associated with
increased risk of various NCDs [49]. Height and weight
were measured at the clinical sites by trained observers,
with participants wearing light clothing and barefoot.
Height was measured to the nearest 0.1 cm using a wall-
mounted calibrated stadiometer (SECA 240, Birmingham,
UK). Weight was measured to the nearest 0.1 kg with a
calibrated electronic scale (TANITA, BC-418MA, Tokyo,
Japan). BMI was derived as weight (kg) divided by height
(m
2
). Dual-energy X-ray absorptiometry (DEXA; Lunar
prodigy advanced fan beam scanner (GE Healthcare)) was
used to assess body composition, and has been described in
detail elsewhere [22]. A three-compartment model (fat
mass, fat-free mass and bone mineral mass) was used to
estimate percentage total body fat. In line with international
guidance, overweight was defined as BMI 25 kg/m
2
and
above [50] and excess percentage body fat as 25% and over
for males and 38% and over for females [51].
Haemoglobin A
1c
(HbA
1c
) has previously been used to
assess risk of developing type II diabetes [52]. Participants’
HbA
1c
was measured on entry to the study from fasting
venous blood samples, in either mmol/mol or as a percent-
age. A conversion algorithm was used to convert all mea-
surements to mmol/mol, and in accordance with
international guidance [52], a level of 42.00 mmol/mol
(6.0%) or higher was used to indicate increased risk of type
II diabetes.
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 3 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Hypertension is associated with an elevated risk of devel-
oping cardiovascular disease [53]. Using an upper arm cuff
and automated oscillometric device, three sets of diastolic
and systolic blood pressure measurements were performed
on each participant. The first readings were discarded and
the lowest systolic and lowest diastolic readings from the
last two readings were used for assessment. In adherence to
UK guidance [54], readings of at least 90 mmHg diastolic
and 140 mmHg systolic were considered indicative of
hypertension. Participants currently taking hypotensive
medication, or self-reporting a diagnosis of hypertension
from a clinician, were also classified as hypertensive.
Covariates
In view of the current evidence base regarding factors influ-
encing dietary intake [55], a self-administered questionnaire
was used to collect demographic and behavioural variables
including sex, age, smoking status (current/ex-smoker or
never smoker), and first degree family history of relevant
diseases such as type II diabetes. Participants were asked
whether or not they had been employed in the past four
weeks, and those answering yes were identified as currently
working. Participants reporting more than 48 h working in
any one week were identified as working overtime. Socio-
economic status was assessed using age at leaving full time
education, which was divided into three categories: educa-
tion up to age 16 years (compulsory education); over 16
and up to 18 years (post-compulsory school education);
andover18years(highereducation).
Physical activity was measured objectively using an inte-
grated movement and heart rate sensor (Actiheart; CamN-
tech, Cambridge, UK) attached to the chest via two
standard ECG electrodes and worn during free-living over
six days [56]. A ramped treadmill protocol test was used to
individually calibrate heart rate, as undertaken previously
[57]. Monitoring data were cleaned for measurement issues
and sensor wear time was specified as at least 48 h, al-
though data were not necessarily spread over a full 24
period. Periods of non-wear were inferred from the combin-
ation of non-physiological heart rate and prolonged periods
of inactivity, which were taken into account to minimise di-
urnal information bias when summarising the intensity
time-series. Data were processed [58] and a branched equa-
tion framework [59] used for modelling to estimate intensity
time series. These were collated over time to yield daily
physical activity energy expenditure (kJ/kg per day).
Statistical analysis
All analyses were on a complete case basis. Thus, partici-
pants with missing data on any of the variables described
were excluded (n= 1038), leaving 11,396 participants (91.7%
total cohort) in the analysis. The outcome variable with the
greatest missingness was vitamin C (missing for 350 partici-
pants) and the covariate with the greatest missingness was
physical activity (missing for 227 participants). Differences
in the characteristics of Fenland study participants included
and excluded from the analytic sample were tested using the
Mann–Whitney test for continuous variables and Pearson
Chi squared test for categorical variables.
Differences in covariates and markers of diet and cardio-
metabolic health across the three frequency categories of
consuming home cooked meals were assessed using de-
scriptive statistics (Kruskal-Wallis test and Pearson Chi
squared test). Separate analyses were then run for each out-
come variable, using linear regression for continuous diet
variables and logistic regression for binary health variables.
Analyses were adjusted for covariates: sex, age, alcohol in-
take, smoking status, age at leaving full-time education,
physical activity, working status, and overtime working,
with supplementary adjustment for family history of dia-
betes for the outcome of HbA
1c
. The analyses for markers
of cardio-metabolic status were additionally adjusted for
dietary variables (MDS, DASH score, plasma vitamin C,
fruit and vegetable intakes) to assess the potential health
benefits of consuming home cooked meals independent of
dietary improvements.
All analyses were conducted using Stata (version 14;
Stata Corp.) and in view of the large number of compari-
sons, 99% confidence intervals were used to determine if
variables were statistically significant (see Additional file
1 for details of the participant sample).
Results
Participant distribution is summarised in Additional file 2.
A slight majority of the included sample was female
(53.3%), with median age 48.9 years. Most participants were
non-smoking (88.2%), with no family history of diabetes
(76.1%), median alcohol intake of 5.47 g/day and physical
activity expenditure of 51.0 kJ/kg/day. Most participants
had left full time education by 18 years of age (62.2%), were
currently in work (82.8%), and did not work overtime
(88.8%). There were significant differences between the
included and excluded participants in terms of sex, age,
smoking status, physical activity expenditure, working
status, and frequency of consuming home cooked meals.
Table 1 shows that 6.2% of included participants con-
sumed home cooked meals as their main meal less than
three times per week, 32.4% consumed these three to five
times per week, and 61.5% consumed these more than five
times per week. Participants who ate home cooked meals
more frequently tended to be female, older, non-smokers,
not currently in work, working fewer hours and not work-
ing overtime, older at leaving full time education, with
greater daily alcohol intake. These associations were all sta-
tistically significant at p< 0.01. Participants who consumed
home cooked meals more frequently generally had higher
plasma vitamin C, higher fruit and vegetable intakes, and
higher MDS and DASH score. They were also less likely to
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 4 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Table 1 Characteristics of participants overall and by frequency of consuming home cooked meals
Covariate
a
Consumption of home cooked main meals
Total <3×/week 3-5×/week >5×/week
n= 11,396 (100.00%) n= 704 (6.18%) n= 3688 (32.36%) n= 7004 (61.46%)
Sex Male 5321 389 (7.31) 1914 (35.97) 3018 (56.72)
Female 6075 315 (5.19) 1774 (29.20) 3986 (65.61)
Age (years) Median (IQR) 48.9 (42.7, 54.8) 47.1 (41.7, 53.3) 48.3 (42.2, 53.9) 49.5 (43.1, 55.3)
Alcohol (grams/day) Median (IQR) 5.47 (1.27, 10.72) 3.90 (0.76, 9.56) 5.47 (1.30, 10.56) 5.47 (1.27, 10.88)
Age at leaving full-time education (years) ≤16 4570 351 (7.68) 1709 (37.40) 2510 (54.92)
>16 to ≤18 2521 148 (5.87) 839 (33.28) 1534 (60.85)
>18 4305 205 (4.76) 1140 (26.48) 2960 (68.76)
Smoker No 10,045 569 (5.66) 3133 (31.19) 6343 (63.15)
Yes 1351 135 (9.99) 555 (41.08) 661 (48.93)
Family History of diabetes
b
No 8677 535 (6.17) 2796 (32.22) 5346 (61.61)
Yes 2719 169 (6.22) 892 (32.81) 1658 (60.98)
Physical activity (kJ
c
/kg
d
/day) Median (IQR) 51.00 (37.84, 66.75) 49.64 (35.82, 65.82) 51.57 (38.22, 67.64) 50.89 (37.88, 66.27)
Working in past 4 weeks No 1959 118 (6.02) 563 (28.74) 1278 (65.24)
Yes 9437 586 (6.21) 3125 (33.11) 5726 (60.68)
Working hours Median (IQR) 33.0 (14.0, 40.0) 37.0 (20.0, 43.7) 35.0 (17.5, 41.0) 30.0 (12.0, 40.0)
Overtime work (>48 h/week) No 10,116 592 (5.85) 3243 (32.06) 6281 (62.09)
Yes 1280 112 (8.75) 445 (34.77) 723 (56.48)
Outcome
i
Vitamin C (umol/l
e
) Median (IQR) 69.40
(56.00, 82.00)
63.15
(44.73, 77.38)
66.80
(52.70, 80.10)
71.1
(58.5, 83.4)
Fruit intake (grams/day) Median (IQR) 207.10
(111.61, 329.50)
142.53
(60.08, 264.19)
180.53
(93.10, 293.10)
226.83
(131.16, 353.04)
Vegetable intake (grams/day) Median (IQR) 258.95
(188.89, 348.56)
174.41
(111.92, 257.26)
234.59
(172.55, 310.33)
280.56
(209.53, 375.83)
DASH score
f
Median (IQR) 24 (21, 27) 22 (19, 25) 23 (20, 26) 25 (22, 28)
MDS
g
Median (IQR) 9 (7,11) 7 (6, 10) 8 (6, 10) 10 (7, 11)
Excess body fat
(≥25% men; ≥38% women)
No 4831 246 (5.09) 1399 (28.96) 3186 (65.95)
Yes 6565 458 (6.98) 2289 (34.87) 3818 (58.16)
Overweight BMI
h
(≥25.0) No 4384 211 (4.81) 1290 (29.43) 2883 (65.76)
Yes 7012 493 (7.03) 2398 (34.20) 4121 (58.77)
High cholesterol ratio (≥4.0) No 7234 400 (5.53) 2209 (30.54) 4625 (63.93)
Yes 4162 304 (7.30) 1479 (35.54) 2379 (57.16)
High HbA
1c
i
(≥42.00) No 10,207 608 (5.96) 3265 (31.99) 6334 (62.06)
Yes 1189 96 (8.07) 423 (35.58) 670 (56.35)
Hypertension No 8561 516 (6.03) 2761 (32.25) 5283 (61.72)
Yes 2836 188 (6.63) 927 (32.69) 1721 (60.68)
a
Results shown as number (row percentage). Median (inter-quartile range) shown for: age, alcohol, physical activity, average working hours, vitamin C, fruit intake,
vegetable intake, DASH score, MDS
b
History of diabetes in first degree relative
c
kj = kilojoules
d
kg = kilograms
e
umol/l = micromole/l
f
DASH = Dietary Approaches to Stop Hypertension
g
MDS = Mediterranean Diet Score
h
BMI = body mass index
i
HbA
1c
= Haemoglobin A
1c
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 5 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

have an overweight BMI, excess percentage body fat, high
risk cholesterol ratio, or to be at risk of developing diabetes
according to HbA
1c
level.
Multivariate associations between the frequency of con-
suming home cooked meals and indicators of diet quality
and cardio-metabolic status are shown in Table 2. In all
cases, consuming home cooked meals more frequently was
significantly associated with indicators of a healthier diet, as
measured by higher MDS, DASH score, plasma vitamin C,
and fruit and vegetable intakes. These associations were
present for both consuming home cooked meals three to
five times per week, and more than five times per week,
compared with the reference of less than three times per
week, and remained robust to adjustment for sociodemo-
graphic and behavioural covariates. In particular, those who
consumed home cooked meals more than five times per
week consumed 62.3 g more fruit (99% CI 43.2 to 81.5) and
97.8 g more vegetables (99% CI 84.4 to 111.2) daily than
those who consumed home cooked meals less than three
times per week. This equates to more than three-quarters
of a portion of fruit, and almost one and a quarter portions
of vegetables, or approximately two extra portions of fruit
and vegetables per day.
In terms of cardio-metabolic status, consuming home
cooked meals more than five times per week compared
with the reference of less than three times per week was
significantly associated with all markers except hyperten-
sion in the unadjusted models. After adjustment for socio-
demographic and behavioural covariates (model 1), the
association between consuming home cooked meals more
than five times per week and high risk cholesterol ratio was
extinguished. After further adjustment for dietary variables
Table 2 Associations between frequency of consuming home cooked meals and markers of diet and cardio-metabolic status
Outcomes Home cooked
meals
a
Unadjusted
value (99% CI
b
)
Adjusted value,
model 1
c
(99% CI
b
)
Adjusted value,
model 2
d
(99% CI
b
)
Regression coefficients for dietary indicators
DASH score
e
3-5×/week 0.23 (0.13, 0.34) 0.18 (0.08, 0.28) NA
>5×/week 0.61 (0.51, 0.71) 0.44 (0.35, 0.54) NA
MDS
f
3-5×/week 0.27 (0.17, 0.38) 0.23 (0.13, 0.33) NA
>5×/week 0.64 (0.54, 0.74) 0.52 (0.42, 0.61) NA
Vitamin C (μmol/l
g
) 3-5×/week 4.50 (2.28, 6.73) 3.29 (1.18, 5.39) NA
>5×/week 8.95 (6.81, 11.09) 5.35 (3.31, 7.39) NA
Fruit intake (grams/day) 3-5×/week 32.29 (12.14, 52.44) 27.17 (7.43, 46.92) NA
>5×/week 79.06 (59.69, 98.43) 62.33 (43.19, 81.46) NA
Vegetable intake (grams/day) 3-5×/week 54.22 (40.06, 68.39) 50.54 (36.61, 64.29) NA
>5×/week 107.43 (93.81, 121.05) 97.83 (84.42, 111.24) NA
Odds ratios for markers of cardio-metabolic status
Cholesterol binary (high vs low risk) 3-5×/week 0.88 (0.71, 1.09) 0.96 (0.76, 1.21) 0.99 (0.78, 1.25)
>5×/week 0.68 (0.55, 0.83) 0.87 (0.70, 1.09) 0.93 (0.74, 1.17)
BMI
h
binary (over- vs normal weight) 3-5×/week 0.80 (0.63, 1.00) 0.81 (0.64, 1.02) 0.82 (0.65, 1.05)
>5×/week 0.61 (0.49, 0.76) 0.70 (0.55, 0.88) 0.72 (0.57, 0.92)
Body fat binary (excess vs normal) 3-5×/week 0.88 (0.70, 1.10) 0.89 (0.71, 1.13) 0.92 (0.73, 1.17)
>5×/week 0.64 (0.52, 0.80) 0.71 (0.57, 0.89) 0.76 (0.60, 0.95)
HbA
1c
i
binary (high vs low risk) 3-5×/week 0.82 (0.60, 1.12) 0.83 (0.60, 1.15) 0.86 (0.62, 1.19)
>5×/week 0.67 (0.50, 0.91) 0.68 (0.49, 0.93) 0.73 (0.53, 1.01)
Hypertension binary (yes vs no) 3-5×/week 0.92 (0.72, 1.17) 0.88 (0.69, 1.13) 0.89 (0.69, 1.14)
>5×/week 0.89 (0.71, 1.13) 0.84 (0.67, 1.07) 0.86 (0.67, 1.09)
a
Consumption of home cooked meals as main meal at home: comparisons with low consumption (<3×/week, reference), for medium consumption (3-5×/week),
and high consumption (>5×/week)
b
CI = 99% confidence interval
c
Adjusted for age, sex, alcohol intake, smoking, physical activity, working status, working overtime, years of full time education (+ family history diabetes for
HbA
1c
outcome)
d
Adjusted for age, sex, alcohol intake, smoking, physical activity, working status, working overtime, years of full time education, DASH score, MDS, vitamin C, fruit
intake, vegetable intake (+ family history diabetes for HbA
1c
outcome)
e
DASH = Dietary Approaches to Stop Hypertension
f
MDS = Mediterranean Diet Score
g
umol/l = micromole/l
h
BMI = Body Mass Index
i
HbA
1c
= Haemoglobin A
1c
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 6 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

(model 2), only the associations with having a normal range
BMI and lower percentage body fat remained significant.
Such associations indicated that consuming home cooked
meals more than five times per week compared with the
reference was associated with improved adiposity, inde-
pendent of the effects due to diet. Those consuming home
cooked meals more than five times per week were 28% less
likely to have a BMI in the overweight range (99% CI 8 to
43%), and 24% less likely to have excess percentage body fat
(99% CI 5 to 40%), compared with those who consumed
home cooked meals less than three times per week. Overall,
a higher frequency of consuming home cooked meals was
associated with markers of improved cardio-metabolic
health, including lower risk cholesterol ratio, normal range
BMI, lower percentage body fat, and lower risk of diabetes
according to HbA
1c
level.
Discussion
Statement of principal findings
In accordance with our hypothesis, a higher frequency of
consuming home cooked main meals was significantly as-
sociated with indicators of a healthier diet, namely DASH
score, MDS, plasma vitamin C, fruit intake and vegetable
intake. Similarly, eating home cooked meals more fre-
quently was significantly associated with several markers of
cardio-metabolic health, including lower likelihood of hav-
ing an overweight BMI, and lower likelihood of excess per-
centage body fat. Associations between frequency of home
cooked meal consumption and markers of cardio-
metabolic health were strongest at the highest consumption
frequency of eating meals more than five times per week.
To our knowledge, this is the first large scale, population-
based study to address associations between the frequency
of consuming home cooked meals and indicators of both
diet quality and cardio-metabolic status. The study has
been reported according to the STROBE-nut guidelines
[60] (see Additional file 3).
Strengths and weaknesses of the study
The Fenland study is a large cohort, with detailed sociode-
mographic data, objective physical measurements and sam-
ples, and comprehensive dietary measures. Participants in
this study were from the county of Cambridgeshire, which
is representative of the wider population in England in
terms of adult obesity and several lifestyle variables, such as
smoking and levels of physical activity [61].
Overall diet quality was assessed using two composite
diet scores, DASH and MDS. Using two composite scores
provided robust evidence in support of potential associa-
tions between consuming home cooked meals more fre-
quently and higher diet quality. These results were
supported by similar associations with higher fruit and
vegetable intakes, measured by both FFQ, and plasma vita-
min C as a biomarker. We used consumption, rather than
preparation, of home cooked meals as our exposure, which
is likely to be closer on the potential causal pathway to diet
and health outcomes. The use of objective measurements
for determining cholesterol ratio, BMI, percentage body fat,
HbA
1c
level and hypertension is likely to increase the valid-
ityofthesemarkersofcardio-metabolicstatus,andthe
confidence in conclusions drawn from resultant analyses.
This research is also subject to some limitations. The
cross-sectional nature of the data means that direction of
cause and effect cannot be established, although follow-up
data collection in the Fenland study is currently underway,
which will enable future longitudinal analysis. Participants
were recruited between the ages of 29 and 64 years, and are
therefore not representative of the full UK population age
range. Given that food preparation practices vary with age
[62], our results may not be generalizable to younger popula-
tions. We excluded participants with missing data on any of
the analytic variables, and excluded participants were sys-
tematically different from the rest of the cohort in terms of
certain characteristics (see Additional file 2). Furthermore,
differences in cooking and food culture internationally may
mean that the relationships between consuming home
cooked meals, diet quality, and cardio-metabolic health, vary
between countries. Therefore, our findings may not necessar-
ily be generalizable to other populations.
The fruit and vegetable intakes and DASH and MDS
dietary scores were derived from FFQ data, which although
validated, may be subject to error and biases [31, 32]. The
composite scores assessed diet quality relative to other
participants, rather than establishing absolute values, and
ranking groups may constitute a broad range. The exposure
variable for consumption of home cooked meals was
derived from a questionnaire item, and given the absence of
consensus on home cooking terminology [63, 64], partici-
pants may have interpreted this question differently. We
collected data specifically on home cooked meals eaten at
home and not those eaten elsewhere, such as packed
lunches taken to work or place of study. The self-reported
nature of several sociodemographic and behavioural
variables, such as smoking, may have led to variables being
correlated with each other, with associated risk of type II
analytical errors.
Although we adjusted for a number of relevant potential
confounders in our analyses, residual confounding remains
possible. If people who consume home cooked meals more
frequently are also more likely to engage in other health
promoting behaviours, this could artificially strengthen as-
sociations between increased consumption of home cooked
meals and markers of cardio-metabolic health.
Interpretation of findings in the context of existing
research
Our findings reflect those of others that found associa-
tions between home food preparation and cooking and
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 7 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

higher quality diets. A recent systematic review [18]
identified that potential benefits included intake from
healthier food groups [19, 65, 66]; greater fruit and vege-
table preference and healthy eating self-efficacy [67]; en-
hanced nutrient intake [7, 68]; higher Diet Quality
Index-International score and intake from healthier food
groups [20]; trend towards higher Healthy Eating Index
score [69]; consumption of a healthful dietary pattern
[70]; and improved adherence to: Healthy People 2010
dietary intake objectives [8], Balance of Good Health
(now Eatwell Guide) criteria [71], and a Mediterranean
diet using the KIDMED index [72]. A greater frequency
of home cooked meals has also been associated with
higher Healthy Eating Index scores [24]. However, the
majority of this research has been cross-sectional and
therefore unable to conclusively indicate direction of
causation. Most studies have also employed self-reported
measures, which are vulnerable to bias [73], and have
used food preparation practices as an exposure, rather
than the consumption of home cooked food itself.
Our results also support previous studies that identified
associations between home food preparation and cooking
and potential advantages to health. Greater home cooking
frequency has been linked with longer lifespan [10] and more
frequent consumption of meals prepared at home has been
associated with reduced risk of developing type II diabetes
[12]. Amongst adolescents, healthier home cooking by a
caregiver was linked with lowered risk of having an over-
weight or obese BMI [11]. However, our findings conflict
with a US study that reported more time spent on home food
preparation and associated clean-up at baseline, or increased
involvement over time, was linked with an adverse cardio-
metabolic profile [16]. Possible reasons for this discrepancy
include that the US study used time spent preparing meals,
rather than meal consumption, as the exposure, and the ex-
posure included clean-up time, which may have a differential
impact on cardio-metabolic health. Since food preparation
activities are strongly patterned by gender [18, 74], this may
also confound observed associations with health.
Meaning of the study: possible mechanisms and
implications for clinicians and policymakers
Our findings indicate that an increased frequency of con-
suming home cooked meals is associated cross-sectionally
with markers of a healthier diet, and indicators of improved
cardio-metabolic health, particularly in terms of adiposity,
cholesterol and diabetes risk. Links between more frequent
consumption of home cooked meals and dietary benefits
could be attributable to healthier food preparation methods,
increased dietary variety and/or consumption of healthier
food groups. Such links may also be due to decreased intake
of convenience foods, which tend to prioritise ingredients
such as fat, sugar and salt to increase palatability and preser-
vation, over those for optimising health [75].
The association between a higher frequency of consum-
ing home cooked meals and potential benefits for health in
terms of hypertension was not significant in the unadjusted
model, and in terms of cholesterol was no longer significant
after adjustment for sociodemographic and behavioural
variables.Thismaybebecausethehypertensionvariable
was poorly ascertained, since in addition to blood pressure
measurement, participants were required to report on any
previous diagnoses of hypertension, and receipt of
hypotensive medication. However, we conducted a sensitiv-
ity analysis for the relationship between frequency of con-
suming home cooked meals and hypertension, with the
inclusion and the exclusion of participants diagnosed with
hypertension by a doctor and/or receiving hypotensive
medication. Regardless of whether or not these participants
were excluded, the relationship was not significant. Choles-
terol is strongly genetically determined [76], and the impact
of home cooked meal consumption may not have been suf-
ficient to result in statistically significant changes.
The cross-sectional association between higher frequency
of consuming home cooked meals and lower adiposity was
robust to adjustment for sociodemographic, lifestyle, and
dietary covariates, whilst the association with lower likelihood
of being classified as at risk of diabetes according to HbA
1c
level was borderline significant. Although the direction of
causation cannot be established, this indicates that home
cooking potentially confers benefits to health, beyond those
mediated through dietary changes. Such benefits from eating
home cooked meals might be attributable to consumption of
smaller portion sizes [77]; moderated snacking behaviour
[78]; more structured mealtimes and the time of day at which
meals are consumed [79]. Increased social cohesion has been
linked with potential health benefits [80], and it is plausible
that higher social capital may be associated with more soci-
able eating patterns. Given the potential time and effort in-
volved in home cooking, home cooked meals may be more
likely to be shared together than meals from other sources,
and a range of benefits to diet, health and wellbeing derived
from shared mealtimes have been identified [81, 82].
Our results support previous research indicating putative
benefits from home cooked meals, suggesting that public
health promotional messages should advocate for cooking at
home as a positive approach for improving diet and health.
Strategies could also be considered for supporting people to
learn to cook healthy meals, and to use their skills often, for
example using digital technology and social media to
provide shopping list generators, food preparation teaching
videos, and nutritional information. Regularity is particularly
important, given that our findings indicated the greatest
potential advantages from consuming home cooked meals
were experienced at the highest frequencies of consumption.
Infrequent home cooking, such as a weekly Sunday lunch, is
unlikely to be of benefit to population health, and cooking
habits should be adopted as part of the daily routine. This is
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

in accordance with research suggesting that routinized
home cooking behaviour is more likely to be maintained
and prioritised over time [83].
Unanswered questions and future research
The evidence base for associations between home cooking,
dietary indicators and cardio-metabolic status requires
further longitudinal studies to establish causal relationships.
This could be facilitated by incorporating questions on home
cooking into current large scale national longitudinal sur-
veys, particularly those with more detailed existing dietary
components. Additional analyses, for example using struc-
tural equation modelling, could be employed to explore
causal pathways more fully in future. It will also be insightful
to identify who eats home cooked meals and why, and then
who prepares these meals and why. Other questions include
exploring further the potential benefits of home cooking be-
yond those mediated through diet, and determining the most
effective approaches to encourage home cooking, which may
require a combination of tailored interventions.
Conclusion
In a cross-sectional population-based study, consuming
home cooked main meals more frequently was associated
with a range of indicators of a healthier diet, and several
markers of cardio-metabolic health including adiposity,
cholesterol and diabetes risk. Strongest associations were ob-
served for the highest frequency of consuming home cooked
meals, more than five times per week. These findings suggest
that regularly eating home cooked meals may confer benefits
to diet and health, and that home cooking promotion and
skill development should form part of future public health
initiatives. Further research regarding causal relationships
between home cooking, diet and health; the wider social as-
pects of home food preparation; and evaluation of interven-
tions to promote home cooking, is required.
Additional files
Additional file 1: This file provides requested information regarding
how the sample was recruited, how representative the sample was of the
target group, how the analysed sample differed from the recruited
sample, and how missing data were handled. (DOCX 17 kb)
Additional file 2: Characteristics of Fenland study participants included and
excluded from the analytic sample. This table compares the characteristics of
participants in the Fenland study who were included in the current study
analytic sample, and those who were excluded. (DOCX 24 kb)
Additional file 3: STROBE-nut: An extension of the STROBE statement for
nutritional epidemiology. This table provides a checklist, reporting
adherence of the current study to the STROBE-nut guidelines. (DOCX 34 kb)
Abbreviations
BMI: Body mass index; DASH: Dietary Approaches to Stop Hypertension;
EPIC: European Prospective Investigation into Cancer and Nutrition;
FFQ: Food Frequency Questionnaire; HbA
1c
: Haemoglobin A
1c
; HDL: High
density lipoprotein; MDS: Mediterranean Diet Score; NCDs: Non-
communicable diseases; UK: United Kingdom; US: United States
Acknowledgements
The authors would like to thank all the volunteers who participated in the
Fenland study, as well as the Fenland Study Coordination, Field
Epidemiology, and Data Cleaning teams.
Funding
The Fenland Study is funded by the Wellcome Trust and the Medical
Research Council. Support from Medical Research Council programmes
MC_UU_12015/1 and MC_UU_12015/5 is acknowledged. This report is
independent research arising from a Doctoral Research Fellowship Grant
DRF-2014-07-020 for the lead author (SM), supported by the National Insti-
tute for Health Research. JA and MW received funding from the Centre for
Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration
(UKCRC) Public Health Research Centre of Excellence. HB and SM are mem-
bers of Fuse, also a UKCRC Public Health Research Centre of Excellence.
Grant reference number is MR/K02325X/1. Funding for CEDAR and for Fuse
from the British Heart Foundation, Cancer Research UK, Economic and Social
Research Council, Medical Research Council, the National Institute for Health
Research and the Wellcome Trust, under the auspices of the UKCRC, is grate-
fully acknowledged. The views expressed in this publication are those of the
authors and not necessarily those of the National Health Service, the Depart-
ment of Health, the funders or UKCRC. The funders had no role in the study
design; in the collection, analysis and interpretation of data; nor in the writ-
ing of the report and the decision to submit for publication.
Availability of data and materials
The data that support the findings of this study are available from the MRC
Epidemiology Unit, under restrictions according to the MRC Policy and
Guidance on Sharing of Research Data from Population and Patient Studies.
Data are available upon reasonable request, with permission sought from
datasharing@mrc-epid.cam.ac.uk
Authors’contributions
SM, HB, WW, MW and JA designed the study. SM led the data analyses, supported
by JA and HB. SM drafted the manuscript. All authors contributed to the
interpretation of results and critically reviewed the manuscript. All authors read
and approved the final manuscript.
Ethics approval and consent to participate
The Fenland study was approved by the Health Research Authority National
Research Ethics Service Committee –East of England Cambridge Central –and
performed in accordance with the Declaration of Helsinki. All participants
provided written informed consent to participate in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Institute of Health & Society, Newcastle University, Baddiley-Clark Building,
Richardson Road, Newcastle upon Tyne NE2 4AX, UK.
2
Human Nutrition
Research Centre, Institute of Health & Society, Newcastle University, M1.151
William Leech Building, Medical School, Framlington Place, Newcastle upon
Tyne NE2 4HH, UK.
3
Centre for Diet and Activity Research (CEDAR), MRC
Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box
285 Biomedical Campus, Cambridge CB2 0QQ, UK.
Received: 15 May 2017 Accepted: 9 August 2017
References
1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A
comparative risk assessment of burden of disease and injury attributable to 67
risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis
for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–60.
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2. Möser A. Food preparation patterns in German family households. An
econometric approach with time budget data. Appetite. 2010;55:99–107.
3. Smith L, Ng S, Popkin B. Trends in US home food preparation and
consumption: analysis of national nutrition surveys and time use studies
from 1965–1966 to 2007–2008. Nutr J. 2013;12:45.
4. Michaud P, Condrasky M, Griffin S. Review and application of current
literature related to culinary programmes for nutrition educators. Top Clin
Nutr. 2007;22:336–48.
5. Short F. Domestic cooking skills: what are they? J Home Econ Inst Aust.
2003;10:13–22.
6. Condrasky MD, Hegler M. How culinary nutrition can save the health of a
nation. J Ext. 2010;48:1–6.
7. Wolfson JA, Bleich SN. Is cooking at home associated with better diet
quality or weight-loss intention? Public Health Nutr. 2015;18:1397–406.
8. Larson N, Perry C, Story M, Neumark-Sztainer D. Food preparation by young
adults is associated with better diet quality. J Am Diet Assoc. 2006;106:2001–7.
9. SmithK,McNaughtonS,GallS,BlizzardL,DwyerT,VennA.Involvementof
young Australian adults in meal preparation: cross-sectional associations with
sociodemographic factors and diet quality. J Am Diet Assoc. 2010;110:1363–7.
10. Chen R, Lee M, Chang Y, Wahlqvist M. Cooking frequency may enhance
survival in Taiwanese elderly. Public Health Nutr. 2012;15:1142–9.
11. Kramer RF, Coutinho AJ, Vaeth E, Christiansen K, Suratkar S, Gittelsohn J.
Healthier home food preparation methods and youth and caregiver
psychosocial factors are associated with lower BMI in African American
youth. J Nutr. 2012;142:948–54.
12. Zong G, Eisenberg DM, Hu FB, Sun Q. Consumption of Meals Prepared at
Home and Risk of Type 2 Diabetes: An Analysis of Two Prospective Cohort
Studies. PLoS Med. 2016;13.
13. Jones M, Freeth EC, Hennessy-Priest K, Costa RJ. A systematic cross-sectional
analysis of British based celebrity chefs’recipes: is there cause for public
health concern? Food Public Health. 2013;3:100–10.
14. Howard S, Adams J, White M. Nutritional content of supermarket ready
meals and recipes by television chefs in the United Kingdom: cross-
sectional study. BMJ. 2012;345:e7607.
15. Pope L, Latimer L, Wansink B. Viewers vs. Doers. The relationship between
watching food television and BMI. Appetite. 2015;90:131–5.
16. Appelhans BM, Segawa E, Janssen I, Nackers LM, Kazlauskaite R, Baylin A, et
al. Meal preparation and cleanup time and cardiometabolic risk over 14
years in the Study of Women’s Health Across the Nation (SWAN). Prev Med.
2015;71:1–6.
17. Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary
quality among men and women in 187 countries in 1990 and 2010: a
systematic assessment. Lancet Glob Health. 2015;3:e132–42.
18. Mills S, White M, Brown H, Wrieden W, Kwasnicka D, Halligan J, et al. Health
and social determinants and outcomes of home cooking: A systematic
review of observational studies. Appetite. 2017;111:116–34.
19. Monsivais P, Aggarwal A, Drewnowski A. Time spent on home food preparation
and indicators of healthy eating. Am J Prev Med. 2014;47:796–802.
20. Chu YL, Storey KE, Veugelers PJ. Involvement in meal preparation at home
is associated with better diet quality among Canadian children. J Nutr Educ
Behav. 2014;46:304–8.
21. World Health Organization. Report of the commission on ending childhood
obesity. Geneva: World Health Organization; 2016. http://www.who.int/end-
childhood-obesity/en/. Accessed 13 Aug 2017.
22. de Lucia RE, Loos RJF, Druet C, Stolk RP, Ekelund U, Griffin SJ, et al.
Association between birth weight and visceral fat in adults. Am J Clin Nutr.
2010;92:347–52.
23. MRC Epidemiology Unit. Fenland Study. 2014. http://epi-meta.medschl.cam.
ac.uk/includes/fenland/fenland.html. Accessed 13 Aug 2017.
24. Tiwari A, Aggarwal A, Tang W, Drewnowski A. Cooking at Home: A Strategy
to Comply With U.S. Dietary Guidelines at No Extra Cost. Am J Prev Med.
2017;52:616–24.
25. InterAct Consortium. Mediterranean Diet and Type 2 Diabetes Risk in the
European Prospective Investigation Into Cancer and Nutrition (EPIC) Study.
Diabetes Care. 2011;34:1913–8.
26. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB.
Adherence to a dash-style diet and risk of coronary heart disease and stroke
in women. Arch Intern Med. 2008;168:713–20.
27. Bingham S, Welch A, McTaggart A, Mulligan A, Runswick S, Luben R, et al.
Nutritional methods in the European Prospective Investigation of Cancer in
Norfolk. Public Health Nutr. 2001;4:847–58.
28. Bingham SA, Gill C, Welch A, Cassidy A, Runswick SA, Oakes S, et al.
Validation of dietary assessment methods in the UK arm of EPIC using
weighed records, and 24-hour urinary nitrogen and potassium and serum
vitamin C and carotenoids as biomarkers. Int J Epidemiol. 1997;26:S137.
29. Mulligan AA, Luben RN, Bhaniani A, Parry-Smith DJ, O'Connor L, Khawaja
AP, et al. A new tool for converting food frequency questionnaire data into
nutrient and food group values: FETA research methods and availability.
BMJ Open. 2014;4.
30. Hastings C, Mosteller F, Tukey JW, Winsor CP. Low Moments for Small
Samples: A Comparative Study of Order Statistics. Ann Math Statist. 1947;
8(3): 413–26. doi:10.1214/aoms/1177730388.
31. Drewnowski A. Diet image: A new perspective on the food-frequency
questionnaire. Nutr Rev. 2001;59:370–2.
32. Willett W. Nutritional Epidemiology. 3rd ed. New York: Oxford University
Press; 1998.
33. de Koning L, Chiuve SE, Fung TT, Willett WC, Rimm EB, Hu FB. Diet-Quality
Scores and the Risk of Type 2 Diabetes in Men. Diabetes Care. 2011;34:1150–6.
34. Berz JB, Singer MR, Guo X, Daniels SR, Moore LL. Use of a dash food group
score to predict excess weight gain in adolescent girls in the national
growth and health study. Arch Pediatr Adolesc Med. 2011;165:540–6.
35. Folsom AR, Parker ED, Harnack LJ. Degree of Concordance With DASH Diet
Guidelines and Incidence of Hypertension and Fatal Cardiovascular Disease.
Am J Hypertens. 2007;20:225–32.
36. Karanja N, Erlinger T, Pao-Hwa L, Miller E, Bray G. The DASH diet for high blood
pressure: from clinical trial to dinner table. Cleve Clin J Med. 2004;71:745–53.
37. United States Department of Health and Human Services, National Heart
Lung and Blood Institute. Healthy Eating Plan. https://www.nhlbi.nih.gov/
health/educational/lose_wt/eat/calories.htm. Accessed 13 Aug 2017.
38. Willett WC, Sacks F, Trichopoulou A, Drescher G, Ferro-Luzzi A, Helsing E, et
al. Mediterranean diet pyramid: a cultural model for healthy eating. Am J
Clin Nutr. 1995;61:1402S–6S.
39. Bach-Faig A, Berry EM, Lairon D, Reguant J, Trichopoulou A, Dernini S, et al.
Mediterranean diet pyramid today. Science and cultural updates. Public
Health Nutr. 2011;14:2274–84.
40. Estruch R, Ros E, Salas-Salvadó J, Covas M-I, Corella D, Arós F, et al. Primary
Prevention of Cardiovascular Disease with a Mediterranean Diet. N Engl J
Med. 2013;368:1279–90.
41. Hu FB, Stampfer MJ, Rimm E, Ascherio A, Rosner BA, Spiegelman D, et al.
Dietary Fat and Coronary Heart Disease: A Comparison of Approaches for
Adjusting for Total Energy Intake and Modeling Repeated Dietary
Measurements. Am J Epidemiol. 1999;149:531–40.
42. Dehghan M, Akhtar-Danesh N, McMillan CR, Thabane L. Is plasma vitamin C
an appropriate biomarker of vitamin C intake? A systematic review and
meta-analysis. Nutr J. 2007;6:41–1.
43. World Health Organization, Food and Agriculture Organization. Diet,
nutrition and the prevention of chronic diseases: Report of the joint WHO/
FAO expert consultation. Geneva: World Health Organization; 2003.
44. Public Health England. The Eatwell Guide. 2016. https://www.gov.uk/
government/publications/the-eatwell-guide. Accessed 13 Aug 2017.
45. O’Connor L, Brage S, Griffin SJ, Wareham NJ, Forouhi NG. The cross-sectional
association between snacking behaviour and measures of adiposity: the
Fenland Study, UK. Br J Nutr. 2015;114:1286–93.
46. Pencina MJ, D'Agostino RB, Larson MG, Massaro JM, Vasan RS. Predicting
the Thirty-year Risk of Cardiovascular Disease: The Framingham Heart Study.
Circulation. 2009;119:3078–84.
47. Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease
in the United Kingdom: independent and external validation of an updated
version of QRISK2. Br Med J. 2012;344.
48. NHS Choices. High cholesterol. 2015. http://www.nhs.uk/conditions/
cholesterol/Pages/Introduction.aspx. Accessed 13 Aug 2017.
49. Baumgartner RN, Heymsfield SB, Roche AF. Human Body Composition and
the Epidemiology of Chronic Disease. Obes Res. 1995;3:73–95.
50. World Health Organization. Body mass index - BMI. 2017. http://www.euro.
who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/
body-mass-index-bmi. Accessed 13 Aug 2017.
51. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y.
Healthy percentage body fat ranges: an approach for developing guidelines
based on body mass index. Am J Clin Nutr. 2000;72:694–701.
52. John WG, on behalf of the UKDoHACoD. Use of HbA1c in the diagnosis of
diabetes mellitus in the UK. The implementation of World Health
Organization guidance 2011. Diabet Med. 2012;29:1350–7.
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 10 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

53. Franklin SS, Wong ND. Hypertension and Cardiovascular Disease:
Contributions of the Framingham Heart Study. Glob Heart. 2013;8;49–57.
54. National Institute for Health and Care Excellence. Hypertension in adults:
diagnosis and management. 2016. https://www.nice.org.uk/guidance/CG127/
chapter/1-Guidance#diagnosing-hypertension-2. Accessed 13 Aug 2017.
55. McGowan L, Pot GK, Stephen AM, Lavelle F, Spence M, Raats M, et al. The
influence of socio-demographic, psychological and knowledge-related
variables alongside perceived cooking and food skills abilities in the
prediction of diet quality in adults: a nationally representative cross-
sectional study. Int J Behav Nutr Phys Act. 2016;13:111.
56. Brage S, Brage N, Franks PW, Ekelund U, Wareham NJ. Reliability and validity
of the combined heart rate and movement sensor Actiheart. Eur J Clin Nutr.
2005;59:561–70.
57. Brage S, Ekelund U, Brage N, Hennings MA, Froberg K, Franks PW, et al.
Hierarchy of individual calibration levels for heart rate and accelerometry to
measure physical activity. J Appl Physiol. 2007;103:682–92.
58. Stegle O, Fallert S, MacKay D, Brage S. Gaussian Process Robust Regression
for Noisy Heart Rate Data. IEEE Trans Biomed Eng. 2008;55:2143–51.
59. Brage S, Brage N, Franks PW, Ekelund U, Wong M-Y, Andersen LB, et al.
Branched equation modeling of simultaneous accelerometry and heart rate
monitoring improves estimate of directly measured physical activity energy
expenditure. J Appl Physiol. 2004;96:343–51.
60. Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al.
Strengthening the Reporting of Observational Studies in
Epidemiology—Nutritional Epidemiology (STROBE-nut): An Extension of the
STROBE Statement. PLoS Med. 2016;13:e1002036.
61. Public Health England. Health Profile Cambridgeshire. 2016. http://
fingertipsreports.phe.org.uk/health-profiles/2016/e10000003.pdf&time_
period=2016. Accessed 13 Aug 2017.
62. Lavelle F, Spence M, Hollywood L, McGowan L, Surgenor D, McCloat A, et
al. Learning cooking skills at different ages: a cross-sectional study. Int J
Behav Nutr Phys Act. 2016;13:119.
63. Short F. Kitchen secrets: the meaning of cookery in everyday life. Oxford:
Berg; 2006.
64. Wolfson JA, Bleich SN, Smith KC, Frattaroli S. What does cooking mean to
you?: Perceptions of cooking and factors related to cooking behavior.
Appetite. 2016;97:146–54.
65. Laska MN, Larson NI, Neumark-Sztainer D, Story M. Does involvement in
food preparation track from adolescence to young adulthood and is it
associated with better dietary quality? Findings from a 10-year longitudinal
study. Public Health Nutr. 2012;15:1150–8.
66. Smith KJ, McNaughton SA, Gall SL, Blizzard L, Dwyer T, Venn AJ. Involvement of
young Australian adults in meal preparation: cross-sectional associations with
sociodemographic factors and diet quality. J Am Diet Assoc. 2010;110:1363–7.
67. Chu YL, Farmer A, Fung C, Kuhle S, Storey KE, Veugelers PJ. Involvement in
home meal preparation is associated with food preference and self-efficacy
among Canadian children. Public Health Nutr. 2012;16:108–12.
68. Larson NI, Story M, Eisenberg ME, Neumark-Sztainer D. Food preparation and
purchasing roles among adolescents: associations with sociodemographic
characteristics and diet quality. J Am Diet Assoc. 2006;106:211–8.
69. Blake CE, Wethington E, Farrell TJ, Bisogni CA, Devine CM. Behavioral
contexts, food-choice coping strategies, and dietary quality of a multiethnic
sample of employed parents. J Am Diet Assoc. 2011;111:401–7.
70. Leech RM, McNaughton SA, Crawford DA, Campbell KJ, Pearson N, Timperio
A. Family food involvement and frequency of family dinner meals among
Australian children aged 10-12years. Cross-sectional and longitudinal
associations with dietary patterns. Appetite. 2014;75:64–70.
71. Swanson V, Power KG, Crombie IK, Irvine L, Kiezebrink K, Wrieden W, et al.
Maternal feeding behaviour and young children’s dietary quality: A cross-
sectional study of socially disadvantaged mothers of two-year old children
using the Theory of Planned Behaviour. Int J Behav Nutr Phys Act. 2011;8.
72. Da Rocha Leal FM, De Oliveira BMPM, Pereira SSR. Relationship between
cooking habits and skills and Mediterranean diet in a sample of Portuguese
adolescents. Perspect Public Health. 2011;131:283–7.
73. Black AE, Cole TJ. Biased Over- Or Under-Reporting is Characteristic of
Individuals Whether Over Time or by Different Assessment Methods. J Am
Diet Assoc. 2001;101:70–80.
74. Adams J, Goffe L, Adamson AJ, Halligan J, O’Brien N, Purves R, et al.
Prevalence and socio-demographic correlates of cooking skills in UK adults:
cross-sectional analysis of data from the UK National Diet and Nutrition
Survey. Int J Behav Nutr Phys Act. 2015;12:1–13.
75. Jaworowska A, Blackham T, Stevenson L, Davies IG. Determination of salt content
in hot takeaway meals in the United Kingdom. Appetite. 2012;59:517–22.
76. Humphries SE, Peacock RE, Talmud PJ. The genetic determinants of plasma
cholesterol and response to diet. Bailliere Clin Endocrinol Metab. 1995;9:
797–823.
77. Benton D. Portion Size: What We Know and What We Need to Know. Crit
Rev Food Sci Nutr. 2015;55:988–1004.
78. Miller R, Benelam B, Stanner SA, Buttriss JL. Is snacking good or bad for
health: An overview. Nutr Bull. 2013;38:302–22.
79. St-Onge MP, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, et al.
Meal Timing and Frequency: Implications for Cardiovascular Disease
Prevention: A Scientific Statement From the American Heart Association.
Circulation. 2017. doi:10.1161/cir.0000000000000476.
80. Walsh D, McCartney G, McCullough S, van der Pol M, Buchanan D, Jones R.
Comparing levels of social capital in three northern post-industrial UK cities.
Public Health. 2015;129:629–38.
81. Hammons AJ, Fiese BH. Is Frequency of Shared Family Meals Related to the
Nutritional Health of Children and Adolescents? Pediatrics. 2011;127:e1565–74.
82. Fiese BH, Schwartz M. Reclaiming the family table: mealtimes and child
health and wellbeing. Soc Policy Rep. 2008;22.
83. Gatley A, Caraher M, Lang T. A qualitative, cross cultural examination of
attitudes and behaviour in relation to cooking habits in France and Britain.
Appetite. 2014;75:71–81.
• We accept pre-submission inquiries
• Our selector tool helps you to find the most relevant journal
• We provide round the clock customer support
• Convenient online submission
• Thorough peer review
• Inclusion in PubMed and all major indexing services
• Maximum visibility for your research
Submit your manuscript at
www.biomedcentral.com/submit
Submit your next manuscript to BioMed Central
and we will help you at every step:
Mills et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:109 Page 11 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com