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Time Spent on Home Food Preparation and Indicators of Healthy Eating

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Background The amount of time spent on food preparation and cooking may have implications for diet quality and health. However, little is known about how food-related time use relates to food consumption and spending, either at restaurants or for food consumed at home. Purpose To quantitatively assess the associations among the amount of time habitually spent on food preparation and patterns of self-reported food consumption, food spending, and frequency of restaurant use. Methods This was a cross-sectional study of 1,319 adults in a population-based survey conducted in 2008–2009. The sample was stratified into those who spent <1 hour/day, 1–2 hours/day, and >2 hours/day on food preparation and cleanup. Descriptive statistics and multivariable regression models examined differences between time-use groups. Analyses were conducted in 2011–2013. Results Individuals who spent the least amount of time on food preparation tended to be working adults who placed a high priority on convenience. Greater amount of time spent on home food preparation was associated with indicators of higher diet quality, including significantly more frequent intake of vegetables, salads, fruits, and fruit juices. Spending <1 hour/day on food preparation was associated with significantly more money spent on food away from home and more frequent use of fast food restaurants compared to those who spent more time on food preparation. Conclusions The findings indicate that time might be an essential ingredient in the production of healthier eating habits among adults. Further research should investigate the determinants of spending time on food preparation.
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Time Spent on Home Food Preparation and
Indicators of Healthy Eating
Pablo Monsivais, PhD, MPH, Anju Aggarwal, PhD, MS, Adam Drewnowski, PhD, MA
Background: The amount of time spent on food preparation and cooking may have implications
for diet quality and health. However, little is known about how food-related time use relates to food
consumption and spending, either at restaurants or for food consumed at home.
Purpose: To quantitatively assess the associations among the amount of time habitually spent on
food preparation and patterns of self-reported food consumption, food spending, and frequency of
restaurant use.
Methods: This was a cross-sectional study of 1,319 adults in a population-based survey conducted
in 20082009. The sample was stratied into those who spent o1 hour/day, 12 hours/day, and 42
hours/day on food preparation and cleanup. Descriptive statistics and multivariable regression
models examined differences between time-use groups. Analyses were conducted in 20112013.
Results: Individuals who spent the least amount of time on food preparation tended to be working
adults who placed a high priority on convenience. Greater amount of time spent on home food
preparation was associated with indicators of higher diet quality, including signicantly more
frequent intake of vegetables, salads, fruits, and fruit juices. Spending o1 hour/day on food
preparation was associated with signicantly more money spent on food away from home and more
frequent use of fast food restaurants compared to those who spent more time on food preparation.
Conclusions: The ndings indicate that time might be an essential ingredient in the production of
healthier eating habits among adults. Further research should investigate the determinants of
spending time on food preparation.
(Am J Prev Med 2014;](]):]]]]]])&2014 American Journal of Preventive Medicine
Introduction
Food preparation habits and skills have been
associated with healthier dietary intakes. In one
study,
1
young adults who regularly prepared food
consumed fast food less frequently and were more likely
to meet dietary recommendations. Another study
2
found
that families purchased a greater variety of vegetables on
a regular basis when the main food preparer had
condence in preparing these foods. In a third study,
3
women who planned meals ahead of time and enjoyed
trying new recipes were more likely to consume two or
more servings of fruit per day whereas women who found
cooking to be a chore and spent little time cooking were
less likely to consume fruit. However, recent surveys
from the U.S. have revealed that time spent on cooking
and food preparation has declined substantially since the
1960s, with Americans currently spending an estimated
33 minutes per day on food preparation and cleanup.
4
Limited time available for cooking may be one of the
barriers to the adoption of more healthy diets. Time
scarcity was prevalent among working parentsearning low
wages in the U.S. Even those parents who valued healthy
family meals often served their children foods that were
fast and easy to prepare,
5
including hot dogs, pizza, and
macaroni and cheese.
6
Research on low- and middle-
income working parents showed that they coped with time
pressures by relying more on takeout and restaurant meals
and basing family meals on prepared entrees and other
quick options.
7
Lack of time was the leading barrier to
adopting dietary guidance cited by European adults.
8
The need for convenience may also be at odds with
recommended meal plans that are optimized for
From the School of Public Health (Monsivais, Aggarwal, Drewnowski),
University of Washington, Seattle, Washington; and Centre for Diet
and Activity Research (Monsivais), University of Cambridge, Cambridge,
United Kingdom
Address correspondence to: Pablo Monsivais, PhD, MPH, Centre for
Diet and Activity Research and Medical Research Council Epidemiology
Unit, University of Cambridge School of Clinical Medicine, Box 285
Institute of Metabolic Science, Cambridge, United Kingdom, CB2 0QQ.
E-mail: pm491@medschl.cam.ac.uk.
0749-3797/$36.00
http://dx.doi.org/10.1016/j.amepre.2014.07.033
&2014 American Journal of Preventive Medicine Published by Elsevier Inc. Am J Prev Med 2014;](]):]]]]]] 1
nutrition and affordability. Economic analyses
9,10
of the
U.S. Department of Agricultures (USDAs) Thrifty Food
Plan have found that these nutritious, low-cost meal
plans were time-intensive to prepare and much more
costly when time was explicitly accounted for. Other
analyses
11
indicate that for single-headed households,
time was a greater constraint than money in achieving
the Thrifty Food Plans dietary targets.
More research is needed to understand how time
availability gures into the preparation and consumption
of healthy diets, but relatively few studies have accounted
for time use generally or food-related time use in
particular. The purpose of this study was to quantitatively
explore the interplay between food-related time use,
restaurant use, and indicators of a healthy diet. Further,
little is known about the associations between time spent
on cooking and food spending. The present study
analyzed data from a population-based study of adults
to test the hypothesis that more time spent preparing,
cooking, and cleaning up from meals at home would be
associated with healthier patterns of food consumption
and fewer meals consumed away from home.
Methods
Subjects
The Seattle Obesity Study was a population-based study of social
determinants of diet and health.
12,13
A stratied sampling scheme
ensured adequate representation by income range and race/
ethnicity. Following standard procedures, randomly generated
telephone numbers were matched with residential addresses using
commercial databases. A pre-notication letter was mailed out to
alert potential participants that their household had been ran-
domly selected by the University of Washington School of Public
Health for a research study. Telephone calls were placed in the
afternoons and evenings by trained, computer-assisted inter-
viewers with up to 13 follow-up calls. Once the household was
contacted, an adult member of the household was randomly
selected to be the survey respondent.
Exclusion criteria were cell phone numbers, numbers that were
not associated with a residence, no person aged Z18 years living in
the residence, residents away for the duration of the interviewing
period, English language not spoken, and discordance between
address data obtained from the vendor and self-reported by the
respondent. Over the course of the study, 16,500 pre-notication
letters were mailed out and 5,102 of these were ruled out as
ineligible by the exclusion criteria.
Eligibility could not be conrmed for a large fraction of the
sample (9,292/16,500=56%). Of the 2,420 conrmed residential
households, 23 refused to participate and another 291 asked to be
called back but were not later reached; thus, eligibility for these
households could not be conrmed. Of the 2,106 conrmed
eligible households, 105 terminated the interview midway or only
partially completed the survey.
A 20-minute telephone survey was then administered to 2,001
participants to collect self-reported data on cooking and eating
habits; diet quality; and sociodemographic, lifestyle, and health
measures. Data were collected in 20082009; the protocols were
modeled on the Behavioral Risk Factors Surveillance System
(BRFSS) surveys for Washington State
14,15
and were approved
by the University of Washington IRB.
Measures
The main independent variable of interest was time spent on
activities related to food preparation. Specically, all participants
were asked the following open-ended question: How many hours
on average do you spend preparing, cooking, and cleaning up from
meals each time? Responses were recorded on a per-week basis.
This time-use question is similar to one currently used in the
Flexible Consumer Behavior Survey (FCBS) module administered
to participants in the National Health and Nutrition Examination
Survey (NHANES).
16
Based on the distribution of responses, data
were grouped into three time-use strata: o1hour/day,12 hours/day,
42hours/day.
Food consumption, food spending, and restaurant use were the
dependent variables of interest. Food consumption was measured
by frequency of consumption of six food groups that reected
healthier and less-healthy intakes: fruit (excluding juice); green
salad; vegetables other than salad or potatoes; fruit juice; sugar-
sweetened beverages (including fruit drinks, soft drinks, and colas
but excluding diet or sugar-free drinks); and sweetened grain-
based snacks (including cookies and cakes).
These food groups were based on standard dietary questions
used in the BRFSS: fruit, fruit juice, and vegetables, from the BRFSS
core questionnaire
17
; sweet snacks, adapted from a module
previously used to examine sources of fat
18
; and sugar-sweetened
beverages, from state-specic BRFSS modules.
19
Respondents were
asked to report their frequency of consumption for each food,
which was coded in number of times per week by the survey
administrator.
Two estimates of self-reported, household-level food spending
were examined: total weekly food spending when eating out
(including restaurants, coffee shops, and fast food outlets) and
food spending excluding eating out, which primarily represented
food expenditures at supermarkets and grocery stores. These
questions were adapted from the NHANES FCBS
16
and were
phrased as follows: How much does your household spend on eating
out in an average week, not including alcohol or tips? and
Altogether, how much does your household spend on food in an
average week, excluding eating out? These household-level esti-
mates were then divided by number of people in each household to
obtain per-person weekly spending variables.
Restaurant use by the participant was documented by asking
questions again based on the NHANES FCBS
16
:When you eat out,
how often you go to each type of restaurant? Responses were
recorded separately for full-service and fast food/quick-service
restaurants. The ve response options ranged from never or less
than twice a month to 4þtimes per week. For analytic purposes,
the variables were dichotomized into once per week or more
versus less than once per week.
Socioeconomic variables were educational attainment and house-
hold income. Both variables were re-grouped to reduce the degrees
of freedom in multivariable models and cut points for regroupings
were driven by the distribution of the sample and a priori categories
of interest. Six categories of education were re-coded into three
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categories: high school or less, some college, and college grad-
uate or higher. Household income groups were also combined
into three categories: o$50,000 per year, $50,000$99,999, and
Z$100,000.
Demographic variables of interest were age, gender, race/
ethnicity, and household size. Smoking was used as a lifestyle
indicator and was characterized as current smoker, former smoker,
and never having smoked. Respondentsattitude toward conven-
ience foods was captured using the following statement: It is
important to me that the foods I usually eat take little time to
purchase, cook, and clean up. Response options were on a 5-point
Likert-type scale ranging from strongly agree to strongly disagree
with a neutral midpoint of neither agree nor disagree.
Because this study explored time use in relation to food
preparation and cooking, only main food providers (1,555/
2,001¼78% of the sample) were included in analyses. These
participants were identied based this on an afrmative response
to the following question: In your household, are you the person
who most often buys groceries and prepares meals? Further
restricting the analyses to those with complete data on dependent
and independent variables and other covariates resulted in an
analytic sample of 1,319 adults.
Statistical Analysis
Descriptive statistics were used to characterize the sociodemo-
graphic and attitudinal prole of three groups differing in food-
related time use. Pearson chi-square tests and ANOVA were used
to test for systematic differences in sociodemographic character-
istics and attitudes by group. General linear models were used to
provide covariate-adjusted means of food intake (servings per
week) and spending ($ per week), across the three time-use groups.
Covariates in these models were the respondents age, gender, race/
ethnicity, educational attainment, income, and employment status.
Multivariable logistic regressions were used to analyze the
likelihood of visiting restaurants once per week or more, adjusting
for the same covariates used in the general linear models above. In
all multivariable regressions, sensitivity analyses were conducted to
examine the importance of marital status in the analyses. All
analyses were conducted between 2011 and 2013 using SPSS,
version 18.0, for Mac.
Results
The analytic sample of 1,319 was composed mostly of
women (67.4%), which was a higher percentage than the
full sample, where women comprised 61.7%. The mean
age of the sample was 54 years overall, with women
slightly older on average (54.6 years) than men (53.8
years). Most of the respondents described themselves as
white (81.0%), with the rest made up of African
American (7.5%); Asian (6.7%); Hispanic (2.7%); and
other ethnic or racial groups (2.0%). About 16% of the
sample (212/1,319) reported spending o1 hour/day on
food preparation, cooking, and cleaning. Nearly 43%
(566/1,319) of participants reported spending 12hours/day
and 41% (541/1,319) reported spending 42 hours/day on
these tasks.
The sociodemographic prole and attitudes toward
convenience in food choices across the three time-use
groups are presented in Table 1. The group spending the
greatest amount of time preparing, cooking, and cleaning
up from meals (42 hours/day) tended to be women,
non-Hispanic whites, younger, and married as compared
to the group spending the least amount of time in these
activities (o1 hour/day). Moreover, those who spent
more time on food-related behaviors tended to have
higher household size and income but were less likely to
be employed or self-employed. Notably, the importance
of convenience in food choices was highest in the group
spending the least amount of time in food behaviors
(po0.001). Smoking behaviors did not appear to vary
systematically across the three groups (p¼0.744).
Fruit and vegetable consumption was systematically
and positively associated with food-related time use.
Multiple linear regression models were used to estimate
adjusted mean (SE) frequency of intake for six food
groups. These analyses were adjusted for key sociodemo-
graphic variables: age, gender, race, employment status,
education, and income. Table 2 shows that among those
who spent the most time in meal-related behaviors, fruit
(excluding juice) was consumed 8.4 times per week
compared to 6.1 times per week for those in the lowest
time-use group (pdifferenceo0.001).
Similarly, vegetables (excluding green salads and
potatoes) were consumed 13.6 times per week among
the top group compared to 10.6 times per week in the
bottom group (pdifferenceo0.001). Fruit juice con-
sumption also showed a positive but weaker association
with food-related time use, and consumption of sweet-
ened beverages and snacks showed no signicant varia-
tion among the groups. Including marital status in these
models did not alter the magnitude or signicance of any
of the associations.
Weekly food spending for meals and beverages away
from home showed an inverse and signicant association
with meal-related time use, even after adjusting for
covariates. The per-person expenditure in the lowest
time-use group was 4$22/week whereas that in the
highest group was approximately $15/week (po0.001).
However, spending for food at home (including gro-
ceries) was not signicantly associated with meal-related
time use.
Food-related time use showed some association with
frequency of restaurant use. Table 3 shows that the crude
percentage of each time-use group visiting full-service
restaurants at least once per week did not differ across
groups, but visits to quick-service (i.e., fast food restau-
rants) appeared to differ systematically. Approximately
43% of those who spent o1 hour/day on food prepara-
tion visited quick-service restaurants once per week or
Monsivais et al / Am J Prev Med 2014;](]):]]]]]] 3
]2014
more compared to just over 30% of those who spent
Z2 hours/day. On multivariable analysis, those who
spent o1 hour/day on meal-related activities were about
1.8 times more likely to visit quick-service restaurants
once per week or more compared to those who spent the
most time on food preparation. Analysis for use of full-
service restaurants showed no differences across groups.
Discussion
The results indicate that healthier food consumption
patterns may have an associated time cost. In this study,
healthier food consumption patterns, characterized by
more frequent consumption of fruits and vegetables, less
money spent on food away from home, and fewer visits to
fast food restaurants, were all signicantly associated with
more time spent preparing, cooking, and cleaning up from
meals. One interpretation of these ndings is that time
spent cooking at home is a prerequisite to achieving
healthier food consumption patterns. Even the USDAs
Thrifty Food Plan (characterized by a healthy diet plan at
the lowest cost) heavily relies on cooking at home,
implying that home cooking is important for achieving
higher diet quality at lower costs.
20
However, a number of individual-level factors may
prevent individuals from cooking at home, including
limited time availability
5
and lack of cooking skills.
21
In
the present study, respondents who spent the least
amount of time cooking were more likely to be employed
or self-employed adults who prioritized convenience over
home-cooked meals. These individuals spent substan-
tially more money on food away from home and
patronized fast food restaurants more frequently com-
pared to the other groups, conrming that time savings
and convenience does come at a price.
22,23
These results
are consistent with analyses of U.S. consumer expendi-
ture data, which found that spending at quick-service
outlets was strongly and positively associated with hours
spent in paid employment.
24
The present ndings were also largely in line with
patterns of time use observed in the general U.S.
population. Analyses
4,25
based on the American Time
Table 1. Sociodemographic and attitudinal characteristics of three time-use groups, % unless otherwise noted
Hours per day spent preparing, cooking, and
cleaning up from meals
Total
(n¼1,319) p-value
a
o1 hour
(n¼212)
12 hours
(n¼566)
42 hours
(n¼541)
Age (years, M) 56.6 53.8 54.0 54.4 0.046
Women 55.2 68.6 71.0 67.4 o0.001
Married
b
21.2 39.9 54.9 43.1 o0.001
Non-Hispanic white 82.5 82.3 78.9 81.0 0.798
Household size
c
(M) 1.6 2.0 2.6 2.2 o0.001
Households with nchildrenr18
n¼0 86.3 74.9 62.3 71.5 o0.001
n¼1 4.7 13.5 15.2 12.7
nZ2 9.0 11.7 22.6 15.7
Four-year degree 49.5 54.8 56.6 54.7 0.229
Employed or self-employed 68.4 67.1 53.2 61.6 o0.001
Household income 450,000/year 46.7 58.3 57.1 56.0 0.005
Never smoker 55.2 53.4 52.1 53.1 0.744
High priority on convenience
d
43.1 26.3 20.2 26.5 o0.001
Note: Boldface indicates statistical signicance.
a
p-value from ANOVA for age and household size and from Pearson χ
2
test for other all other variables.
b
Sample for marital status n¼1,317.
c
Household size, number of adults and children in the household.
d
Those who strongly agreed with the statement It is important to me that the foods I usually eat take little time to purchase, cook and clean up.
Sample for Convenience question n¼1,314.
Monsivais et al / Am J Prev Med 2014;](]):]]]]]]4
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Use Survey (ATUS) found that the responsibility for food
preparation was held primarily by women, whereas
adults who were married or not in paid employment
spent more time on food preparation. The ATUS also
indicates that time spent on food preparation increases
with greater numbers of children in the household, which
aligns with the present nding that participants in the
highest time-use group were most likely to have one or
more child resident in their households.
Although analyses of the ATUS have found that
respondents with lower income tended to spend slightly
more time on food preparation,
25
the present results
indicated that more time spent on food preparation was
associated with higher income. This contrast may be
related to the nature of the current study sample, which
had higher incomes and educational attainment than the
general U.S. population.
Limitations
Features of this study are worth addressing because they
may have implications for the results presented here.
First, the cross-sectional nature of the study limits the
Table 2. Adjusted
a
estimates of food consumption and food spending for three food-related time-use groups, M (SE)
Hours per day spent preparing, cooking, and cleaning up from
meals
p-value
b
o1 hour 12 hours 42 hours
Food consumption (frequency of consumption per week)
Fruit (not including juice) 6.1 (0.7) 7.1 (0.6) 8.4 (0.6) o0.001
Green salad, lettuce 2.8 (0.3) 3.2 (0.2) 3.6 (0.2) o0.001
Vegetables other than green salad or potatoes 10.6 (0.9) 12.1 (0.8) 13.6 (0.8) o0.001
Fruit juice 4.2 (0.5) 4.8 (0.4) 5.0 (0.4) 0.038
Sugar-sweetened beverages
c
3.3 (0.5) 3.4 (0.4) 3.7 (0.4) 0.369
Sweet snacks
d
2.9 (0.4) 2.6 (0.3) 2.7 (0.3) 0.309
Food spending ($ per person per week)
Eating out 22.8 (2.7) 16.4 (2.3) 15.1 (2.3) o0.001
Food at home 43.8 (4.3) 44.6 (3.7) 46.5 (3.6) 0.406
Note: Boldface indicates statistical signicance.
a
Means adjusted in general linear models containing respondents age, gender, race, employment status, educational attainment, and income as
covariates.
b
p-value indicated for difference between lowest and highest time-use groups.
c
Including soft drinks, cola, and sweetened fruit drinks.
d
Including cakes, cookies, and other sweetened baked goods.
Table 3. Full- and quick-service restaurant use by three food-related time-use groups
Restaurant type
Hours per day spent preparing, cooking, and cleaning up from meals
p-value
a
o1 hour 12 hours 42 hours
Crude percentage of each time-use group with Z1 restaurant visit per week (%)
Full-service 37.7 41.0 38.8
Fast food/quick-service
b
42.9 38.9 30.7
Covariate-adjusted ORs
c
(95% CIs) for Z1 restaurant visit per week
Full-service 1.09 (0.77, 1.54) 1.13 (0.88, 1.45) ref 0.581
Fast food/quick-service
b
1.79 (1.27, 2.53) 1.44 (1.11, 1.86) ref 0.001
Note: Boldface indicates statistical signicance.
a
p-value indicated for difference between lowest and highest time-use groups.
b
Fast food/quick-service dened as food outlets where payment is made prior to receiving food.
c
ORs from logistic regression models adjusting for respondents age, gender, race, employment status, educational attainment, and income as
covariates.
Monsivais et al / Am J Prev Med 2014;](]):]]]]]] 5
]2014
ability to draw conclusions regarding causality. Whether
more time spent on preparing food enables healthier
eating habits or whether individuals who consume
healthier diets also tend to enjoy spending more time
cooking could not be determined. Second, the independ-
ent variable (time use) and the three main outcomes
(food consumption, food spending, and restaurant use)
were all self-reported, which subjects them to both error
and bias.
26,27
Third, dietary intake of only a few food
groups was assessed and only frequency but not quantity
of intake. As a result, a more detailed and nuanced pic-
ture of diet quality in this sample could not be obtained.
Finally, the present analyses could not identify
whether these food behaviors lead to better health
outcomes, which would ideally be explored in longitudi-
nal study designs. These limitations are balanced by a
number of strengths, including a relatively large,
population-based sample of adults involved in food
shopping and preparation and what the authors believe
is a novel linkage of food intake, food spending, restau-
rant use, and time spent on food preparation, which is a
neglected domain in understanding dietary behavior.
Conclusions
The ndings reported here indicate that spending time on
food preparation at home might be essential to healthier
dietary habits among adults. More research is needed to
identify barriers that limit cooking at home and the
lifestyles of those who prioritize convenience in food
choice. Meanwhile, dietary advice and food producers and
retailers should encourage foods and meals that are
nutritious and yet quick and convenient for the consumer.
Moreover, government efforts toward identifying lowest-
cost yet healthy food patterns
20
need to explicitly account
for the associated time costs of producing healthier meals.
Doing so would make the true costs of healthier diets
more realistic
9,10
and, importantly, inform improvements
to the Supplemental Nutrition Assistance Program to
better support healthy eating in low-income populations.
This work was supported by a grant from the National Institute of
Diabetes and Digestive and Kidney Diseases (grant No. R01
DK076608). Pablo Monsivais also received support from the
Centre for Diet and Activity Research, a United Kingdom Clinical
Research Collaboration Public Health Research Centre of Excel-
lence funded by the British Heart Foundation, Economic and
Social Research Council, Medical Research Council, the National
Institute for Health Research, and the Wellcome Trust. The study
sponsors had no role in the study design, analysis, or publication.
No nancial disclosures were reported by the authors of
this paper.
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]2014
... For some, as a result of their hectic schedules, working individuals are unable to prepare their own meals or access food ingredients, so they opt for quick and easy options in purchasing food. Parents pressed for time will choose quick and easy meals (Monsivais et al., 2014). Moreover, making greater use of takeout and restaurant meals, and basing family meals on prepared entrees and other quick options for their family, is one of the ways that working parents with low and middle incomes typically cope with time scarcity or time pressure (Devine et al., 2006). ...
... 36 In addition, although it is recognized that the act of cooking is relevant for a healthy diet, studies indicate that it is necessary to invest effort and time for food preparation, and that the lack of the time is one of the main impediments to healthy eating. 9,37,38 In some cases, this lack of available time to cook, together with the absence of culinary skills, can be an obstacle to the practice of healthy eating among university students. 9 According to the GAPB, 13 one of the ways to manage the time necessary for the preparation of meals in the home environment is through the sharing of food-related tasks. ...
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ResumoIntrodução: A pandemia decorrente da contaminação por coronavírus (Covid-19) forçou países a implementarem regimes sanitários rígidos, incluindo medidas de isolamento social. Objetivo: Este estudo original avaliou aspectos da autonomia culinária de universitários antes e durante o isolamento social decorrente da pandemia da Covid-19. Método: Desenvolveu-se um estudo transversal descritivo, conduzido entre junho e julho de 2020, com a aplicação de um questionário on-line sobre dados sociodemográficos, aspectos da autonomia culinária antes e durante o isolamento, para comparação e avaliação. A associação entre as variáveis de interesse foi avaliada por meio do teste Qui-quadrado de Pearson. Resultados: Participaram da pesquisa 233 universitários, a maioria do sexo feminino (71,2%) e com idade entre 20 e 25 anos (74,6%). Durante o período de isolamento, a frequência com que esses estudantes cozinhavam aumentou, assim como a utilização da panela de pressão (um indicador de confiança na cozinha), a divisão de tarefas nesse espaço e o consumo de alimentos in natura ou minimamente processados, embora o acesso e idas às feiras tenha diminuído. Conclusão: Nesta população, o período de isolamento parece ter contribuído, em nível individual, para o desenvolvimento da autonomia culinária. Em tempos de pandemia ou não, esse desenvolvimento é necessário para que o agente crie maneiras de economizar e preparar suas próprias refeições. Nesse sentido, os achados contribuem para o diálogo sobre a promoção da saúde e qualidade de vida por meio da culinária caseira.
... Cooking, sitting around the table and sharing food in the company of family and friends is a social support and gives a sense of community. Conviviality at the table has been rediscovered as an element for the prevention of eating disorders (Monsivais et al., 2014;Bach-Faig et al., 2011;Burgesse-Champoux et al., 2009).  Seasonality, biodiversity, eco-compatibility, and local and traditional food products are presented at the bottom of the pyramids to highlight how the modern MD is comparable with the development of a sustainable food model for present and future generations. ...
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BACKGROUND: Over the past 20 years there has been an increase in obesity rates among university students, therefore they should be seen as a group that requires special attention regarding health promotion. The interest in a healthy diet can lead to a psychological obsession known as orthorexia nervosa (ON), frequent among students in the biomedical field and in the sports context. The high levels of stress recorded in university students have been related to the use of drugs to enhance their cognitive abilities. However, a high adherence to the Mediterranean diet (MD) can bring cognitive benefits, with an improvement in depressive symptoms and anxiety, enabling a better academic outcome. AIM: The aim of this study was to evaluate self-medication, adherence to MD, and the relationship between lifestyle and biomarkers of metabolic status in a university population. METHODS: Students, doctoral students, post-docs and specialists have been recruited in Italy (N = 108) and Spain (N = 86). Data were collected through questionnaires in order to evaluate lifestyle, self-medication, alcohol consumption (AUDIT), eating habits, in particular adherence to MD and food neophobia (FN), level of physical activity (IPAQ), orthorexia (ORTO-15 and subscores), body concerns (MBSRQ and BUT), psychological distress (K10), eating attitude (EAT-26) and malnutrition (SSI). Participants have been evaluated with clinical parameters of metabolic status (glycaemia, cholesterol, triglycerides, and ketones). RESULTS: Italian females had higher MED-55 and FNS (Food Neophobia Scale) scores, and a lower AUDIT score than Spaniards (p < 0.01). Students who stayed with their family (resident) were more adherent to MD than those who moved away from home. Resident Italians consumed less beer, hard liquors, and cocktails than Spaniards on Saturday nights (p < 0.01). There were negative correlations between AUDIT and QueMD (R2: 0.137, p < 0.05), and AUDIT and non-typical MD foods score (ntMED) (R2: 0.201, p < 0.01) in Spaniards; however, there was no relationship between AUDIT and other MD scores. Most of the sample (72.8% of IT and 62.3% of SP) used medicines without medical prescription, with a higher tendency to self-medication among Italian females (IT-F) and Spanish males (SP-M). Moreover, 47.6% of IT and 31.1% of SP read the leaflet before taking a drug (p < 0.05). The ORTO-15 positive subjects, assessed with the originally proposed cut-off, were above 70% in both IT and SP students, with a higher prevalence in the Spanish sample (96-97%). According to ORTO-7, about 30% of Italian and 48% of Spanish students were positive to ON, with not significant gender differences. When excluding students underweight, overweight or obese, as well as those potentially at risk of eating disorders or presenting mild, moderate, and severe distress (K10neg-EAT-26neg subgroup), we did not find many correlations between ORTO scores and BUT, SSI, total MBSRQ and some of its components. ORTO-7 resulted the only ON score unrelated with body mass index, MBSRQ components and IPAQ-assessed intense activity, in the NW - K10neg -EAT-26neg subgroup. After this sort of “exclusion diagnosis”, the prevalence of ON of these students on the overall sample resulted of 16.9%, 12.2%, 15.2% and 25.9% for IT-F, IT-M, SP-F and SP-M, respectively. As regards the metabolic status, Spaniards had higher blood glucose levels than Italians (IT-F vs SP-F, p < 0.01; IT-M vs SP-M, p < 0.001), whereas a state of ketosis has been observed in SP-M. CONCLUSION: Results of this study suggest that non-typical MD foods and Saturday night consumptions, related to being far from home, have a great impact on alcohol consumption. In some university students ON could be a symptom of other conditions related to body image concerns and distress, as well as to high PA and appearance, fitness, health, or illness orientation. However, ORTO-7 became independent from these confounding, after the exclusion of underweight, overweight, obese and students positive to EAT-26 and K10, suggesting the possibility of identifying orthorexic subjects with this specific questionnaire.
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By the end of 2020, over 1.8 million Americans will be diagnosed with cancer and 600,000 will die from the disease. Despite experiencing lower incidence rates of cancer compared to non-Hispanic Whites, the Hispanic population in the United States faces a number of barriers to care, which may result in more involved, costlier, and potentially less successful treatments. Hispanic men in particular experience disproportionate cancer-related health disparities compared to other racial and ethnic groups and Hispanic women. Hispanic men cancer survivors (HMCS) have unique supportive care needs and use a variety of coping mechanisms, which remain largely unaccounted for and unaddressed. This chapter presents a brief description of cancer epidemiology and relevant disparities in diagnosis and care for the Hispanic population in the United States. It also explores merging research centered on preliminary data about the supportive care needs of HMCS and concludes with recommendations for public health research and practice.
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Over the last several decades, the survival for pediatric acute lymphoblastic leukemia (ALL) has increased from about 40–90%. However, current treatment strategies are associated with several acute and long-term toxicities, including neurotoxicity. Further, racial and ethnic disparities persist in both incidence and outcomes for ALL. In particular, Latino children experience both the highest incidence of ALL and less favorable outcomes. The incidence of neurotoxicity during ALL therapy potentially jeopardizes treatment efficacy, and long-term neurocognitive impairment profoundly affects quality of life for survivors. Emerging evidence indicates that Latino patients may be particularly susceptible to these adverse side effects of therapy. Unfortunately, studies of neurotoxicity during ALL therapy have not included large populations of Latino children. Therefore, well-designed studies are needed to characterize neurotoxicity outcomes in Latino patients, while considering factors associated with disparities in cognitive performance in the general population, including socioeconomic status and acculturation. Ultimately, a better understanding of the various factors likely responsible for disparities in neurotoxicity is needed to improve outcomes for Latino children with ALL; these factors include inherited genetic variation, clinical characteristics, and sociocultural differences.
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Problem/condition: Promoting preconception health can potentially improve women's health and pregnancy outcomes. Evidence-based interventions exist to reduce many maternal behaviors and chronic conditions that are associated with adverse pregnancy outcomes such as tobacco use, alcohol use, inadequate folic acid intake, obesity, hypertension, and diabetes. The 2006 national recommendations to improve preconception health included monitoring improvements in preconception health by maximizing public health surveillance (CDC. Recommendations to improve preconception health and health care-United States: a report of the CDC/ATSDR Preconception Care Work Group and the Select Panel on Preconception Care. MMWR 2006;55[No. RR-6]). Reporting period covered: 2009 for 38 indicators; 2008 for one indicator. DESCRIPTION OF SURVEILLANCE SYSTEMS: The Pregnancy Risk Assessment Monitoring System (PRAMS) is an ongoing state- and population-based surveillance system designed to monitor selected self-reported maternal behaviors, conditions, and experiences that occur shortly before, during, and after pregnancy among women who deliver live-born infants. The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing state-based telephone survey of noninstitutionalized adults aged ≥18 years in the United States that collects state-level data on health-related risk behaviors, chronic conditions, and preventive health services. This surveillance summary includes PRAMS data from 29 reporting areas (n = 40,388 respondents) and BRFSS data from 51 reporting areas (n = 62,875 respondents) for nonpregnant women of reproductive age (aged 18-44 years). To establish a comprehensive, nationally recognized set of indicators to be used for monitoring, evaluation, and response, a volunteer group of policy and program leaders and epidemiologists identified 45 core state preconception health indicators, of which 41 rely on PRAMS or BRFSS as data sources. This report includes 39 of the 41 core state preconception health indicators for which data are available through PRAMS or BRFSS. The two indicators from these data sources that are not described in this report are human immunodeficiency virus (HIV) testing within a year before the most recent pregnancy and heavy drinking on at least one occasion during the preceding month. Ten preconception health domains are examined: general health status and life satisfaction, social determinants of health, health care, reproductive health and family planning, tobacco and alcohol use, nutrition and physical activity, mental health, emotional and social support, chronic conditions, and infections. Weighted prevalence estimates and 95% confidence intervals (95% CIs)for 39 indicators are presented overall and for each reporting area and stratified by age group (18-24, 25-34, and 35-44 years) and women's race/ethnicity (non-Hispanic white, non-Hispanic black, non-Hispanic other, and Hispanic). Results: This surveillance summary includes data for 39 of 41 indicators: 2009 data for 23 preconception health indicators that were monitored by PRAMS and 16 preconception health indicators that were monitored by BRFSS (one BRFSS indicator uses 2008 data). For two of the indicators that are included in this report (prepregnancy overweight or obesity and current overweight or obesity), separate measures of overweight and obesity were reported. All preconception health indicators varied by reporting area, and most indicators varied significantly by age group and race/ethnicity. 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Although 43% of women reported that their most recent pregnancy was unintended (unwanted or wanted to be pregnant later), approximately half (53%) of those who were not trying to get pregnant reported not using contraception at the time of conception. Smoking during the 3 months before pregnancy was reported by 25.1% of women, and drinking alcohol 3 months before pregnancy was reported by 54.2% of women. Daily use of a multivitamin, prenatal vitamin, or a folic acid supplement during the month before pregnancy was reported by 29.7% of women. Selected BRFSS results included indicators pertaining to the nutrition and physical activity, emotional and social support, and chronic conditions domains among women of reproductive age. Approximately one fourth (24.7%) of women were identified as being obese according to body mass index (BMI) on the basis of self-reported height and weight. Overall, 51.6% of women reported participation in recommended levels of physical activity per U.S. Department of Health and Human Services physical activity guidelines. Non-Hispanic whites reported the highest prevalence (85.0%) of having adequate emotional and social support, followed by other races/ethnicities (74.9%), Hispanics (70.5%), and non-Hispanic blacks (69.7%). Approximately 3.0% of persons reported ever being diagnosed with diabetes, and 10.2% of women reported ever being diagnosed with hypertension. Interpretation: The findings in this report underscore opportunities for improving the preconception health of U.S. women. Preconception health and women's health can be improved by reducing unintended pregnancies, reducing risky behaviors (e.g., smoking and drinking) among women of reproductive age, and ensuring that chronic conditions are under control. Evidence-based interventions and clinical practice guidelines exist to address these risks and to improve pregnancy outcomes and women's health in general. The results also highlight the need to increase access to health care for all nonpregnant women of reproductive age and the need to encourage the use of essential preventive services for women, including preconception health services. In addition, system changes in community settings can alleviate health problems resulting from inadequate social and emotional support and environments that foster unhealthy lifestyles. Policy changes can promote health equity by encouraging environments that promote healthier options in nutrition and physical activity. Finally, variation in the preconception health status of women by age and race/ethnicity underscores the need for implementing and scaling up proven strategies to reduce persistent health disparities among those at highest risk. Ongoing surveillance and research in preconception health are needed to monitor the influence of improved health-care access and coverage on women's prepregnancy and interpregnancy health status, pregnancy and infant outcomes, and health disparities. Public health action: Public health decision makers, program planners, researchers, and other key stakeholders can use the state-level PRAMS and BRFSS preconception health indicators to benchmark and monitor preconception health among women of reproductive age. These data also can be used to evaluate the effectiveness of preconception health state and national programs and to assess the need for new programs, program enhancements, and policies.
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This chapter provides an overview of nutritional epidemiology for those unfamiliar with the field. The field of nutritional epidemiology developed from an interest in the concept that aspects of diet may influence the occurrence of human disease. Although it is relatively new as a formal area of research, investigators have used basic epidemiologic methods for more than 200 years to identify numerous essential nutrients. The most serious challenge to research in nutritional epidemiology has been the development of practical methods to measure diet. Because epidemiologic studies usually involve at least several hundred and sometimes hundreds of thousands of subjects, dietary assessment methods must be not only reasonably accurate but also relatively inexpensive. Epidemiologic approaches to diet and disease and the interpretation of epidemiologic data are discussed.
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The Behavioral Risk Factor Surveillance System (BRFSS) is designed to provide statewide estimates of the prevalence of preventive health practices, including screening. We assessed the reproducibility of responses to the women's health module, which covers breast and cervical cancer screening, hysterectomy, and pregnancy. A random sample of women in Massachusetts ( n = 91; response rate for the repeat interview, 70.0%) and a separate random sample of minority women in the state ( n = 179; response rate for the repeat interview, 69.4%) were interviewed by telephone twice, 21 to 94 days apart. Differences across administrations in mean prevalence of screening were small. Concordance exceeded 85% for almost all the variables examined, but tended to be lower for nonwhite respondents. After correction for agreement occurring by chance, moderate to excellent values of κ (range, 0.41 to 0.86) were observed. The women's health module of the BRFSS questionnaire yields highly consistent group mean estimates of prevalence when administered repeatedly to the same individuals. Individual reproducibility is excellent, but may be reduced among minority respondents.
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This book is intended to increase understanding of the complex relationships between diet and the major diseases of western civilization, such as cancer and atherosclerosis. The book starts with an overview of research strategies in nutritional epidemiology-a relatively new discipline which combines the knowledge compiled by nutritionists during this century with the methodology developed by epidemiologists to study the determinants of disease with multiple etiologies and long latent periods. A major part of the book is devoted to methods of dietary assessment using data on food intake, biochemical indicators of diet, and measures of body size and composition. The reproducibility and validity of each approach and the implications of measurement error are considered in detail. The analysis, presentation, and interpretation of data from epidemiologic studies of diet and disease are discussed. Particular attention is paid to the important influence of total energy intake on findings in such studies. As examples of methodologic issues in nutritional epidemiology, three substantive topics are examined in depth: the relations of diet and coronary heart disease, fat intake and breast cancer, and Vitamin A and lung cancer.
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Food production at home requires money and time. Food assistance programs focus exclusively on the money cost, while ignoring the time cost. This one-dimensional focus could undermine the effectiveness of food assistance programs. In the spirit of Vickery (1977), this paper uses a cost difference approach to develop a money-time threshold, and several related metrics, to determine whether money or time is the most limiting resource in reaching the Thrifty Food Plan (TFP) target. In our empirical analysis we find that when time is ignored, single headed households spend on average 35% more than required to meet the TFP target. However, when time is included, these households spend on average 40% less than required to meet the TFP target. In addition, we find that when time is ignored, 62% of single headed households on average spend enough money to reach the TFP target, but when time is included, only 13% of single headed households spend enough on average to reach the TFP target. Our empirical results suggest that time is more constraining than money in reaching the TFP target. These results imply that metrics solely focusing on money could severely underestimate the gap between actual expenditures and those required to reach the TFP target.
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Cooking skills are emphasized in nutrition promotion but their distribution among population subgroups and relationship to dietary behavior is researched by few population-based studies. This study examined the relationships between confidence to cook, sociodemographic characteristics, and household vegetable purchasing. This cross-sectional study of 426 randomly selected households in Brisbane, Australia, used a validated questionnaire to assess household vegetable purchasing habits and the confidence to cook of the person who most often prepares food for these households. The mutually adjusted odds ratios (ORs) of lacking confidence to cook were assessed across a range of demographic subgroups using multiple logistic regression models. Similarly, mutually adjusted mean vegetable purchasing scores were calculated using multiple linear regression for different population groups and for respondents with varying confidence levels. Lacking confidence to cook using a variety of techniques was more common among respondents with less education (OR 3.30; 95% confidence interval [CI] 1.01 to 10.75) and was less common among respondents who lived with minors (OR 0.22; 95% CI 0.09 to 0.53) and other adults (OR 0.43; 95% CI 0.24 to 0.78). Lack of confidence to prepare vegetables was associated with being male (OR 2.25; 95% CI 1.24 to 4.08), low education (OR 6.60; 95% CI 2.08 to 20.91), lower household income (OR 2.98; 95% CI 1.02 to 8.72) and living with other adults (OR 0.53; 95% CI 0.29 to 0.98). Households bought a greater variety of vegetables on a regular basis when the main chef was confident to prepare them (difference: 18.60; 95% CI 14.66 to 22.54), older (difference: 8.69; 95% CI 4.92 to 12.47), lived with at least one other adult (difference: 5.47; 95% CI 2.82 to 8.12) or at least one minor (difference: 2.86; 95% CI 0.17 to 5.55). Cooking skills may contribute to socioeconomic dietary differences, and may be a useful strategy for promoting fruit and vegetable consumption, particularly among socioeconomically disadvantaged groups.
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Measures of neighborhood deprivation used in health research are typically based on conventional area-based SES. The aim of this study is to examine new data and measures of SES for use in health research. Specifically, assessed property values are introduced as a new individual-level metric of wealth and tested for their ability to substitute for conventional area-based SES as measures of neighborhood deprivation. The analysis was conducted in 2010 using data from 1922 participants in the 2008-2009 survey of the Seattle Obesity Study (SOS). It compared the relative strength of the association between the individual-level neighborhood wealth metric (assessed property values) and area-level SES measures (including education, income, and percentage above poverty as single variables, and as the composite Singh index) on the binary outcome fair/poor general health status. Analyses were adjusted for gender, categoric age, race, employment status, home ownership, and household income. The neighborhood wealth measure was more predictive of fair/poor health status than area-level SES measures, calculated either as single variables or as indices (lower DIC measures for all models). The odds of having a fair/poor health status decreased by 0.85 (95% CI=0.77, 0.93) per $50,000 increase in neighborhood property values after adjusting for individual-level SES measures. The proposed individual-level metric of neighborhood wealth, if replicated in other areas, could replace area-based SES measures, thus simplifying analyses of contextual effects on health.
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Socio-economic disparities in nutrition are well documented. This study tested the hypothesis that socio-economic differences in nutrient intakes can be accounted for, in part, by diet cost. A representative sample of 1295 adults in King County (WA) was surveyed in 2008-2009, and usual dietary intakes were assessed based on a food-frequency questionnaire. The monetary value of individual diets was estimated using local retail supermarket prices for 384 foods. Nutrients of concern as identified by the 2005 Dietary Guidelines Advisory Committee were fibre, vitamins A, C and E, calcium, magnesium and potassium. A nutrient density score based on all seven nutrients was another dependent measure. General linear models and linear regressions were used to examine associations among education and income, nutrient density measure and diet cost. Analyses were conducted in 2009-2010. Controlling for energy and other covariates, higher-cost diets were significantly higher in all seven nutrients and in overall nutrient density. Higher education and income were positively and significantly associated with the nutrient density measure, but these effects were greatly attenuated with the inclusion of the cost variable in the model. Socio-economic differences in nutrient intake can be substantially explained by the monetary cost of the diet. The higher cost of more nutritious diets may contribute to socio-economic disparities in health and should be taken into account in the formulation of nutrition and public health policy.