The validation of home food inventory

Article (PDF Available)inInternational Journal of Behavioral Nutrition and Physical Activity 5(1):55 · December 2008with28 Reads
DOI: 10.1186/1479-5868-5-55 · Source: PubMed
Abstract
Home food inventories provide an efficient method for assessing home food availability; however, few are validated. The present study's aim was to develop and validate a home food inventory that is easily completed by research participants in their homes and includes a comprehensive range of both healthful and less healthful foods that are associated with obesity. A home food inventory (HFI) was developed and tested with two samples. Sample 1 included 51 adult participants and six trained research staff who independently completed the HFI in participants' homes. Sample 2 included 342 families in which parents completed the HFI and the Diet History Questionnaire (DHQ) and students completed three 24-hour dietary recall interviews. HFI items assessed 13 major food categories as well as two categories assessing ready-access to foods in the kitchen and the refrigerator. An obesogenic household food availability score was also created. To assess criterion validity, participants' and research staffs' assessment of home food availability were compared (staff = gold standard). Criterion validity was evaluated with kappa, sensitivity, and specificity. Construct validity was assessed with correlations of five HFI major food category scores with servings of the same foods and associated nutrients from the DHQ and dietary recalls. Kappa statistics for all 13 major food categories and the two ready-access categories ranged from 0.61 to 0.83, indicating substantial agreement. Sensitivity ranged from 0.69 to 0.89, and specificity ranged from 0.86 to 0.95. Spearman correlations between staff and participant major food category scores ranged from 0.71 to 0.97. Correlations between the HFI scores and food group servings and nutrients on the DHQ (parents) were all significant (p < .05) while about half of associations between the HFI and dietary recall interviews (adolescents) were significant (p < .05). The obesogenic home food availability score was significantly associated (p < .05) with energy intake of both parents and adolescents. This new home food inventory is valid, participant-friendly, and may be useful for community-based behavioral nutrition and obesity prevention research. The inventory builds on previous measures by including a wide range of healthful and less healthful foods rather than foods targeted for a specific intervention.
BioMed Central
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International Journal of Behavioral
Nutrition and Physical Activity
Open Access
Methodology
The validation of a home food inventory
Jayne A Fulkerson*
1
, Melissa C Nelson
2
, Leslie Lytle
2
, Stacey Moe
2
,
Carrie Heitzler
2
and Keryn E Pasch
2
Address:
1
School of Nursing, University of Minnesota, Minnesota, USA and
2
Division of Epidemiology & Community Health, University of
Minnesota, Minnesota, USA
Email: Jayne A Fulkerson* - fulke001@umn.edu; Melissa C Nelson - nels5024@umn.edu; Leslie Lytle - lalytle@umn.edu;
Stacey Moe - gerl0056@umn.edu; Carrie Heitzler - heitz022@umn.edu; Keryn E Pasch - pasc0074@umn.edu
* Corresponding author
Abstract
Background: Home food inventories provide an efficient method for assessing home food
availability; however, few are validated. The present study's aim was to develop and validate a home
food inventory that is easily completed by research participants in their homes and includes a
comprehensive range of both healthful and less healthful foods that are associated with obesity.
Methods: A home food inventory (HFI) was developed and tested with two samples. Sample 1
included 51 adult participants and six trained research staff who independently completed the HFI
in participants' homes. Sample 2 included 342 families in which parents completed the HFI and the
Diet History Questionnaire (DHQ) and students completed three 24-hour dietary recall
interviews. HFI items assessed 13 major food categories as well as two categories assessing ready-
access to foods in the kitchen and the refrigerator. An obesogenic household food availability score
was also created. To assess criterion validity, participants' and research staffs' assessment of home
food availability were compared (staff = gold standard). Criterion validity was evaluated with kappa,
sensitivity, and specificity. Construct validity was assessed with correlations of five HFI major food
category scores with servings of the same foods and associated nutrients from the DHQ and
dietary recalls.
Results: Kappa statistics for all 13 major food categories and the two ready-access categories
ranged from 0.61 to 0.83, indicating substantial agreement. Sensitivity ranged from 0.69 to 0.89, and
specificity ranged from 0.86 to 0.95. Spearman correlations between staff and participant major
food category scores ranged from 0.71 to 0.97. Correlations between the HFI scores and food
group servings and nutrients on the DHQ (parents) were all significant (p < .05) while about half
of associations between the HFI and dietary recall interviews (adolescents) were significant (p <
.05). The obesogenic home food availability score was significantly associated (p < .05) with energy
intake of both parents and adolescents.
Conclusion: This new home food inventory is valid, participant-friendly, and may be useful for
community-based behavioral nutrition and obesity prevention research. The inventory builds on
previous measures by including a wide range of healthful and less healthful foods rather than foods
targeted for a specific intervention.
Published: 4 November 2008
International Journal of Behavioral Nutrition and Physical Activity 2008, 5:55 doi:10.1186/1479-5868-5-55
Received: 2 June 2008
Accepted: 4 November 2008
This article is available from: http://www.ijbnpa.org/content/5/1/55
© 2008 Fulkerson et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0
),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
International Journal of Behavioral Nutrition and Physical Activity 2008, 5:55 http://www.ijbnpa.org/content/5/1/55
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Background
Availability of foods in the home has been shown to be
significantly associated with dietary practices, intake, and
eating patterns [1-5]. Although this area of research has
been growing and several instruments have been devel-
oped to assess the home food environment (see [6] for a
comprehensive review), often the instruments include a
limited number of items selected to address a specific aim
such as fruit and vegetable availability for cancer preven-
tion or high-fat food availability for cardiovascular health.
Instruments that provide a more comprehensive assess-
ment of food and energy availability in the home are cur-
rently unavailable. Given current scientific interest in the
contextual and environmental influences on energy bal-
ance and obesity, there is a great need for such inventories.
When developing such assessment inventories, thorough
evaluation is needed, including several dimensions of
validity testing. Criterion validity tests the performance of
an instrument by comparing it to a gold standard [7]. A
rigorous but logistically-challenging method for assessing
criterion validity for a home food inventory is to require
participants and study staff to independently complete a
home food availability instrument, and then compare
their responses using the staff report as the "gold stand-
ard." Comparisons are evaluated for consistency of results
between the two methods using kappa or correlation sta-
tistics. In addition, comparisons may be made by examin-
ing sensitivity (i.e., proportion of foods in the home
assessed as present by the staff that were accurately identi-
fied as present in the home by the participant) and specif-
icity (i.e., proportion of foods in the home assessed as
absent by the staff that were accurately identified as absent
in the home by the participant). Construct validity is sug-
gested when expected relationships are shown between
the measure and other variables in a conceptual frame-
work [7]. For example, if an HFI shows high availability of
high fat foods in the home, one might expect family mem-
bers to report a high calorie intake in their diet.
The development of many of the home food availability
measures described in the literature include a very limited
number of foods, and have not included comprehensive
validity testing, particularly criterion and construct valid-
ity. Criterion validity is very important if participants in
research studies are expected to complete self-report home
food inventories on their own while in their homes. Con-
struct validity is also important because an instrument
should be associated with expected health outcomes.
Only four studies have demonstrated criterion validity of
home food inventories [2,8-10], and only two have dem-
onstrated construct validity [2,8], and each has its limita-
tions (as described below).
Crockett and colleagues [8] developed a shelf inventory of
80 foods in 12 categories based on foods targeted in a can-
cer risk reduction program. Only perishable foods tar-
geted in the intervention program were included on the
inventory. They conducted two criterion-related valida-
tion studies that compared participant and staff-reported
inventories. In the two studies, sensitivity was reported as
0.86 and 0.87, and specificity was reported as 0.92 and
0.90, respectively. In addition, Cohen's kappa showed sig-
nificant overall agreement (p < .0001) between partici-
pant and staff reports in both studies. Furthermore,
construct validation was assessed by comparing inventory
responses with food frequency questionnaires, with over-
all agreement of 73.6%
Miller and Edwards [9] assessed the face, content and cri-
terion validity for a 166-item shelf inventory that was
based on previous work [8,11] with modifications for fat-
and sugar-modified foods that would be relevant to the
purchases of individuals with diabetes. Similar to the pre-
vious inventory, mostly perishable foods were included.
Thirty-one older adults diagnosed with Type 2 diabetes
completed the inventory and within 48 hours study staff
directly observed foods in the home. Cohen's kappa sta-
tistic was 0.87 and sensitivity and specificity were 0.90
and 0.97, respectively. Construct validity was not
assessed.
Marsh and colleagues [10] conducted a study with 48 par-
ents that demonstrated criterion validity for a 34-item
fruit, juice, and vegetable availability questionnaire. Par-
ents reported the presence of these foods in the home
within the last seven days, and staff conducted inventories
on the same visit. In addition to perishable foods, frozen,
canned and dried fruit/vegetable products were included.
Cohen's kappa values ranged from 0.24–0.53 for juices,
0.12–0.76 for fruits, and 0.22–0.66 for vegetables. Overall
sensitivity and specificity values for total fruit, juice and
vegetables were 36.8 and 39.1, respectively. Construct
validity was not assessed.
Lastly, Raynor and colleagues [2] completed a type of cri-
terion validity and construct validity of a household food
availability instrument (22 high-fat and 22 low-fat items)
with 165 adults. Criterion validity was accomplished by
correlating the number of high-fat and low-fat food items
reported by two adults in the same household (r = 0.69, p
< .001 and r = 0.59, p < .001, respectively). However, gold
standard criterion validity comparing participant report to
trained staff report was not completed. Construct validity
considered correlations between availability of high-fat
foods and low-fat foods with fat consumption via a food
frequency questionnaire, and showed significant (p <
.001) correlations (r = 0.25 and r = -0.33, respectively).
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In comparison to other published home food inventories
[6], these four studies represent the most comprehensive
methods that have been used to test validity to date.
Despite their relatively rigorous validity testing, however,
only two studies demonstrated construct validity, and
each of these measures is somewhat limited in scope, with
none providing a comprehensive assessment of overall
home food and energy availability. Thus, given the lack of
comprehensive home food inventories that have been rig-
orously validated, there is a need for additional instru-
ment development in this area. An inventory assessing a
wide range of food exposures in the home, including both
healthful and less healthful foods, may be important in
understanding contextual influences on obesity, weight
gain, and nutritional intake. In addition, such an inven-
tory could be useful in determining appropriate interven-
tion strategies that may fit the needs of individual
households or might identify targets for public health
messages. The purpose of the present study was to develop
and validate a home food inventory that is easily com-
pleted by adults in their homes and includes both health-
ful and less healthful foods as well as reduced-fat and
regular-fat varieties of foods potentially related to the
obesity epidemic.
Methods
Procedures, Participants, and Measures
The University of Minnesota's Institutional Review Board
approved this study, and all participants signed the appro-
priate parental consent and student assent forms. The
study was conducted in three phases. First, brief field test-
ing of the newly developed HFI was conducted with a
small sample of adults (n = 5) to assess ease of completion
and comprehension as the participants indicated which
foods were difficult to inventory. The inventory food list
was re-evaluated at this stage and foods were added if par-
ticipants frequently indicated that there were foods to
code without a place to do so on the inventory. Second,
adults in the community were recruited to complete the
home HFI in their homes while allowing trained staff
members in their homes to complete the inventory inde-
pendently (criterion validity testing with Sample 1).
Third, parents and students were recruited for participa-
tion in the IDEA (Identifying Determinants of Eating and
Activity) study [12] in which parents completed the HFI
and Diet History Questionnaire (DHQ) and students par-
ticipated in 24-hour recall dietary interviews (construct
validity with Sample 2).
Sample 1
For the criterion validation phase, 51 adults were recruited
from 19 area Minneapolis Park and Recreational Centers
via posted flyers. Trained research staff traveled to the par-
ticipants' homes to complete consent procedures and
complete to the HFI. Although participants and staff com-
pleted inventories at the same time, they began their
assessments in different parts of the home and were
instructed not to communicate with each other as they
completed the inventory. Participants were provided with
a $30 gift card for their participation.
Sample 2
For the construct validation phase, 349 families (one stu-
dent between the ages of 10 and 17 years and one parent/
guardian or other adult caregiver) were recruited from the
following sources: 1) an existing cohort of youth partici-
pating in the Minnesota Adolescent Community Cohort
(MACC) Tobacco Study [13], 2) a Minnesota Department
of Motor Vehicle (DMV) list restricted to the seven-county
metro area, or 3) a convenience sample drawn from local
communities. Of the 349 youth/adult pairs measured,
26% were recruited from the MACC cohort, 49% were
recruited from the DMV sample and 25% were recruited
from the convenience sample [12].
In the larger IDEA study, youth and adults pairs scheduled
a visit to an IDEA clinic where anthropometric measures
were taken and psychosocial surveys (that included demo-
graphic characteristics) were administered to both stu-
dents and adults. Instructions for additional measures
were given at this time and parents received a packet of
instruments to take home to complete and return by mail.
Included in this packet were the HFI and the DHQ. The
DHQ is a food frequency that has been widely used with
adults (NCI). Students were told to expect that three die-
tary recall interviews would be conducted with them by
telephone within the next month, and were provided with
a two-dimensional food model packet to help them esti-
mate portion size. The final sample for construct valida-
tion includes data from the 342 families who completed
the HFI, DHQ and dietary recalls (98% of sample).
Measures
Home Food Inventory
To develop the initial set of food items, the investigators
examined existing instruments [8,14] and reviewed the
literature that identified major contributors to overall
energy intake (e.g., [15]). This process allowed us to eval-
uate the foods listed in inventories that were developed
for a specific limited purpose (e.g., a diabetic population,
nonperishable foods, etc) as well as expand the items in
our inventory to include foods known to be associated
with energy intake in the population. In addition, based
on literature that demonstrates a high correlation between
readily accessible foods (i.e., foods in plain view) and
their intake [3], two items were added to assess the acces-
sibility of healthful foods within the main kitchen area
and the refrigerator. Thus, we evaluated the literature and
instruments to date, and added foods that provided a
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more comprehensive inventory of foods associated with
dietary intake of adults in the US.
During the course of initial validity testing (Sample 1),
changes in the number of items and instructions occurred.
For example, participants were allowed to "write in" foods
within given categories (e.g., dairy, fruits, vegetables), and
if "write in" responses were found to be frequent, they
were added as items on the inventory. During the validity
testing with Sample 1, the HFI had 186 items while the
final inventory administered to Sample 2 included 190
items.
HFI items are listed in a checklist type format with yes/no
(1/0) response options. Higher scores represent greater
availability. In addition, participants were instructed to
check whether the vegetable, fruit, and bread items were
fresh, frozen, dried or canned, as appropriate. The cate-
gory order was set up to facilitate ease of completion,
beginning with the refrigerated items, followed by frozen
items, and non-perishable items. Participants are
instructed to look for these foods in all areas of the home
where food is stored, including the refrigerator, freezer,
pantry, cupboard, and other areas (e.g., basement). Partic-
ipants were informed that lower fat products may be
labeled as "reduced-fat," "low-fat," "light," "nonfat," or
"skim." Foods in the dairy, added fats, frozen desserts,
prepared desserts, and savory snacks were categorized into
regular-fat or reduced-fat groupings; beverages were cate-
gorized into regular sugar and low sugar categories; and
foods in the two ready-access categories were further sub-
grouped into healthful and less healthful categories.
Although the categorization of foods into healthful and
less healthful categories may not be entirely straightfor-
ward, we assessed each food by its typical fat and sugar
content when determining its category. To assess the over-
all obesogenic home food availability, a summative score
was created that includes regular-fat versions of cheese,
milk, yogurt, other dairy, frozen desserts, prepared des-
serts, savory snacks, added fats; regular-sugar beverages;
processed meat; high-fat quick, microwavable foods;
candy; access to unhealthy foods in refrigerator and
kitchen. The obesogenic home food availability score
potential range was from 0–71 (present sample: range =
9–53, M = 29.4, SD = 7.6). The HFI can be requested from
the primary author. A table reflecting which foods are
included in each food group/subgroup is provided in
Additional file 1. The inventory took approximately
30–45 minutes to complete depending upon the amount
of food stored in the home.
24-Hour Recall Interviews
Students in Sample 2 completed three telephone-admin-
istered 24-hour dietary recalls following their clinic visit
(response rate for students completing three recalls was
86%). Dietary recalls were conducted for two weekdays
and one weekend day, with the aim of having each of the
three recalls completed within a 2-week period. In gen-
eral, multiple dietary recalls are widely accepted as a valid
and reliable method for dietary assessment, and have
yielded acceptable validity in children as young as 10
years [16]. Trained and certified staff from the Nutrition
Coordination Center (NCC) at the University of Minne-
sota administered the recalls, using the Nutrition Data
System Research (NDS-R) software [2006, Nutrition
Coordinating Center, University of Minnesota, Minneap-
olis, MN] with an interactive, interview format with direct
data entry linked to a nutrient database [17]. The NDS-R
data set allows for the examination of both nutrient
intakes and food group information (e.g., servings of
fruits and vegetables consumed).
Diet History Questionnaire
Dietary assessment for parents was conducted using the
Diet History Questionnaire (DHQ) food frequency instru-
ment developed by the National Cancer Institute (NCI).
Parents received the DHQ at the clinic visit and were
asked to mail it in when completed. This instrument con-
sists of 144 food items and includes both portion size and
dietary supplement questions. Requiring approximately
one hour to complete, it has been widely used to charac-
terize usual food and nutrient intakes in numerous adult
populations.
Several studies have been conducted to assess the validity
and calibration of the DHQ. Findings indicate that the
DHQ provides reasonably valid estimates for usual intake
of most nutrients and that it performs as well or better
than other well-known food frequency instruments avail-
able in the field [18-20]. The food list and nutrient data-
base used for standardized analysis of the DHQ are
derived using national dietary data from the US Depart-
ment of Agriculture's Continuing Survey of Food Intakes
by Individuals (1994–96) [20].
Data Analysis
To assess criterion validity using data from Sample 1, par-
ticipants' and research staffs' assessment of home food
availability were compared. Consistent in research of cri-
terion validity, the staff report was considered the gold
standard as they were trained on how to use the inventory.
Validity was evaluated by calculating kappa, sensitivity,
and specificity between participant and staff reports on
the presence of individual foods. To summarize these
results, we calculated the average of these individual kap-
pas across both major and minor food groupings. In addi-
tion, to test the performance of the instrument's
assessment of broad food categories (rather than individ-
ual foods), we assessed the extent of agreement between
food group summary scores between participant and staff
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reports by creating additive summary scores for major and
minor food groupings (e.g., overall dairy score, cheese
score, respectively). Using data from Sample 2, construct
validity was assessed by examining Spearman correlations
of five major food category scores on the HFI (i.e., dairy,
vegetables with and without potatoes, fruit, and meats &
other nondairy protein) with number of servings of the
same foods as well as nutrients that should be correlated
with these foods (e.g., calcium with dairy, Vitamin C with
fruit) with foods and nutrients from the DHQ and 24-
hour recall interviews. These five categories were chosen
since it was possible to create similar categories across the
three measures. In addition, we assessed construct validity
of the obesogenic home food availability score by com-
paring it to both parental and adolescent reports of energy
intake. All analyses were conducted in SAS (v9.1, SAS
Institute, Inc., Cary, NC, 2003).
Results
Sample Characteristics
As shown in Table 1, Sample 1 consisted of 51 adults (pre-
dominantly female). About two-thirds of the sample was
white, followed by African American, American Indian,
Mixed race/ethnicity, Latino, and Asian. More than half of
the sample had a college degree. Student gender for Sam-
ple 2 was equally distributed and most identified them-
selves as white. The majority of students reported living
with their mother and father together. Students were pri-
marily attending public schools, and more than two-
thirds of the sample was in 9
th
–12
th
grade. Parent partici-
pants in Sample 2 were predominantly white females with
some college education.
Criterion Validity
Table 2 provides information regarding criterion validity
with comparisons of reports of the presence of individual
food items between the trained staff data (gold standard)
and participants' data, including the number of items, the
average kappa statistic, the average sensitivity value, and
the average specificity value for each food category. In
addition, Spearman correlations of major food categories
comparing data from staff and participants are provided.
The number of items in each major food category ranged
from five to 26.
For all 13 major food categories and the two accessibility
categories, Cohen's kappa ranged from 0.61 (prepared
desserts) to 0.83 (Fruits), sensitivity values ranged from
0.69 (prepared desserts) to 0.89 (vegetables), and specifi-
city ranged from 0.86 (meat & other nondairy protein,
bread, and beverages) to 0.95 (fruits, microwavable/
quick-cook foods, and kitchen accessibility). All kappas
assessing agreement between staff and participant reports
of major food categories were greater than 0.60, indicating
substantial agreement. Spearman correlations between
staff and participant major food category scores ranged
from .71 (frozen desserts) to .97 (candy). All correlations
between staff and participant reports of major food group
scores were greater than 0.70. All validity statistics for the
obesogenic home availability score were equally accepta-
ble.
Construct Validity
Results for construct validity (correlations between five
HFI major food categories and the same food categories
and expected associated nutrients from the DHQ and 24-
hour recalls) are presented on Table 3. All of the HFI
Table 1: Characteristics of criterion validity sample (Sample 1) and construct validity sample (Sample 2)
Sample 1 Sample 2
Characteristic Adults (n = 51) Parents (n = 342) Students (n = 342)
Gender, %
Females 94.1 75.6 51.0
Education, %
Less than HS 4.0 0.6 100.0
HS or GED 8.0 8.6 --
Some college or vocational school 26.0 26.1 --
College degree 28.0 35.2 --
Training beyond college degree 34.0 27.8 --
Race/ethnicity, %
White 68.6 98.8 93.4
African American 13.7 0.3 1.4
American Indian 5.9 0.0 0.0
Asian 2.0 0.0 0.3
Hispanic/Latino 3.9 0.0 0.0
Mixed 5.9 0.9 4.6
Age (years; M, SD)
39.4 46.7 (0.3) 15.4 (0.1)
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major food category scores among the parents were signif-
icantly and positively correlated with the corresponding
food group servings on the DHQ. Furthermore, Vitamin
A, Vitamin C, fiber, and calcium were significantly and
positively associated with their respective HFI major food
categories (e.g., calcium with the dairy score, Vitamin C
Table 2: Inventory major and subgroup food category validity indices (Sample 1, n = 51)
Food category # Items Average kappa for category Sensitivity Specificity Staff/participant correlation
a
Dairy 21 0.72 0.81 0.91 0.92
Cheese 11 0.64 0.74 0.90 0.85
Regular fat 5 0.62 0.76 0.84 0.70
Reduced fat 6 0.65 0.72 0.95 0.80
Milk/other dairy beverages 6 0.89 0.90 0.97 0.90
Regular fat 1 0.94 1.00 0.98 0.88
Reduced fat 5 0.87 0.88 0.97 0.89
Yogurt 2 0.71 0.85 0.86 0.89
Regular fat 1 0.70 0.78 0.91 0.70
Reduced fat 1 0.72 0.91 0.81 0.73
Other Dairy 3 0.78 0.87 0.90 0.87
Regular fat 2 0.73 0.90 0.86 0.78
Reduced fat 1 0.88 0.82 1.00 0.88
All vegetables, including potatoes 20 0.80 0.89 0.90 0.88
All vegetables, no potatoes 19 0.80 0.89 0.90 0.88
Fruits 26 0.83 0.87 0.95 0.95
Meats & other nondairy protein 16 0.74 0.88 0.86 0.85
Processed meat 4 0.74 0.84 0.91 0.78
All other protein 11 0.74 0.89 0.85 0.83
Added Fat 13 0.76 0.84 0.92 0.79
Regular fat 8 0.78 0.88 0.91 0.76
Reduced fat 5 0.72 0.78 0.94 0.77
Frozen Desserts 7 0.64 0.70 0.94 0.71
Regular fat 3 0.83 0.86 0.95 0.82
Reduced fat 4 0.50 0.58 0.93 0.70
Prepared Desserts 8 0.61 0.69 0.93 0.73
Regular fat 6 0.58 0.68 0.92 0.65
Reduced fat 2 0.81 0.80 0.98 0.81
Savory Snacks 18 0.73 0.84 0.91 0.95
Regular fat 10 0.71 0.88 0.89 0.93
Reduced fat 8 0.76 0.78 0.95 0.91
Microwavable/quick-cook foods 8 0.71 0.78 0.95 0.81
Bread
b
12 0.71 0.80 0.91 0.86
Wheat 5 0.77 0.76 0.95 0.74
White 7 0.66 0.83 0.87 0.79
Dry breakfast cereal
c
Whole grain 1 -- -- -- 0.75
High sugar 1 -- -- -- 0.87
Low sugar 1 -- -- -- 0.77
Candy 5 0.79 0.87 0.94 0.97
Beverages 9 0.76 0.86 0.88 0.84
Regular sugar 6 0.74 0.86 0.86 0.78
Low sugar 3 0.82 0.85 0.93 0.89
Kitchen accessibility 12 0.74 0.74 0.95 0.75
Access to healthy foods 6 0.66 0.63 0.95 0.67
Access to unhealthy foods 6 0.83 0.84 0.96 0.86
Refrigerator accessibility 15 0.63 0.75 0.89 0.73
Access to healthy foods 9 0.59 0.70 0.91 0.72
Access to unhealthy foods 6 0.68 0.81 0.86 0.73
Obesogenic food availability score 71 0.73 0.83 0.91 0.94
a
Spearman correlation between staff and participant report of foods present in home.
b
Pilot form did not include croissant item.
c
Kappa statistic not available owing to multiple category response options for item.
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with the fruit score). Similarly, the obesogenic home
availability was significantly and positively associated
with parental energy intake. As shown in Table 4, signifi-
cant, but attenuated, correlations were found between sev-
eral of the HFI major food category scores and 24-hour
recall nutrient intake values among the adolescents (HFI
dairy score with recall dairy servings and recall calcium,
HFI vegetable scores with recall vegetable servings and
recall Vitamin A, and HFI fruit score and recall Vitamins
A, C, and fiber). However, for nearly half of the food
group categories and nutrients, dietary intake based on
the 24-hour recalls was not found to be associated with
availability of related products in the home. The
obesogenic home food availability score was significantly
and positively associated with adolescent energy intake.
Discussion
The present study describes the validation of a home food
inventory designed to include a wide range of foods that
contribute to energy intake, including more and less
healthful foods. The wide range of foods included on the
inventory speak to its content validity and study findings
Table 3: Correlations between HFI food categories and DHQ food serving and nutrient data (Sample 2, n = 342)
HFI major food category DHQ servings and nutrients Spearman Correlation p-value
Total number of dairy products Total number of dairy servings 0.15 <.01
Average calcium (mg) 0.16 <.01
Total number of vegetables (including potatoes) Total number of vegetable servings (including potatoes) 0.34 <.0001
Vitamin A (IU) 0.27 <.0001
Vitamin C (mg) 0.27 <.0001
Fiber (g) 0.20 <.001
Total number of vegetables (excluding potatoes) Total number of vegetable servings (excluding potatoes) 0.34 <.0001
Vitamin A (IU) 0.26 <.0001
Vitamin C (mg) 0.26 <.0001
Fiber (g) 0.20 <.001
Total number of fruits Total number of fruit servings 0.37 <.0001
Vitamin A (IU) 0.26 <.0001
Vitamin C (mg) 0.30 <.0001
Fiber (g) 0.26 <.001
Total number of meats & other nondairy protein Total number of non-dairy protein servings 0.23 <.0001
Protein (g) 0.13 <.01
Obesogenic food availability score Energy (kcals) 0.16 <.01
* Summative score that includes regular-fat versions of cheese, milk, yogurt, other dairy, frozen desserts, prepared desserts, savory snacks, added
fats; regular-sugar beverages; processed meat; high-fat quick, microwavable foods; candy; access to unhealthy foods in refrigerator and kitchen.
Table 4: Correlations between HFI food categories and dietary recall food serving and nutrient data (Sample 2, n = 342)
HFI major food category Dietary recall servings and nutrients Spearman Correlation p-value
Total number of dairy products Total number of dairy servings 0.15 <.01
Average calcium (mg) 0.13 <.05
Total number of vegetables (including potatoes) Total number of vegetable servings (including potatoes) 0.16 <.01
Vitamin A (IU) 0.13 <.05
Vitamin C (mg) 0.10 .08
Fiber (g) 0.06 .28
Total number of vegetables (excluding potatoes) Total number of vegetable servings (excluding potatoes) 0.17 <.01
Vitamin A (IU) 0.13 <.05
Vitamin C (mg) 0.10 .08
Fiber (g) 0.05 .34
Total number of fruits Total number of fruit servings 0.07 .17
Vitamin A (IU) 0.11 <.05
Vitamin C (mg) 0.13 <.05
Fiber (g) 0.17 <.01
Total number of meats & other nondairy protein Total number of non-dairy protein servings 0.03 .56
Protein (g) 0.02 .68
Obesogenic food availability score* Energy (kcals) 0.13 <.05
* Summative score that includes regular-fat versions of cheese, milk, yogurt, other dairy, frozen desserts, prepared desserts, savory snacks, added
fats; regular-sugar beverages; processed meat; high-fat quick, microwavable foods; candy; access to unhealthy foods in refrigerator and kitchen.
International Journal of Behavioral Nutrition and Physical Activity 2008, 5:55 http://www.ijbnpa.org/content/5/1/55
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indicate substantial criterion, and construct validity for
the new inventory, particularly for adults. In addition, the
checklist type format is easily completed by research par-
ticipants in their homes without undue response burden.
The demonstrated criterion validity of the new home food
inventory as shown by high kappa, sensitivity, specificity
values and high correlations between participants' and
staffs' reports of foods in the home suggests that the
instrument could be used effectively for data collection by
participants, thus, alleviating the need for staff home visits
which are expensive, time-consuming and potentially
intrusive. In comparing our criterion validity indices to
previous research, kappa, sensitivity, and specificity values
appear to be similar to two previous studies reporting
these values [8,9], and substantially higher than those
reported by Marsh and colleagues [10]. Raynor and col-
leagues [2] are the only previous investigators to demon-
strate significant criterion validity of groupings of high-fat
and low-fat foods by showing significant correlations
between reports of two adults living in the household.
Our study examined several validity indices regarding staff
and participant reports of regular fat and reduced fat ver-
sions of dairy, added fats, frozen and prepared desserts,
and savory snacks and demonstrated substantial validity
as well, and extended the previous work by examining
subgroups of foods within major categories (e.g., cheese
within the dairy category) rather than grouping all low-fat
foods together. However, it should be noted that,
although the comparison of staff and participant
responses is an accepted practice for measuring criterion
validity, this testing does not attest to the inventory's cap-
ture of all relevant foods, nor does it eliminate the possi-
bility that participants may have altered their responses
since the research staff were in their homes.
Although all of the HFI major food groups and many of
the food subgroups showed substantial criterion validity,
several of the food subgroups did not perform as well and
deserve mention. In particular, the reduced-fat frozen des-
sert, regular fat prepared dessert, and white bread catego-
ries had lower than desired criterion validity. Our findings
regarding lower validity for prepared desserts is similar to
that found in previous research [8]. Several anecdotal
observations early in the study indicate that the wide vari-
ety of dairy, soy and other frozen desserts available in the
marketplace may make it difficult to assess nutrient and
fat content. Similar observations were made for prepared
desserts. In addition, the proliferation of whole grain
white breads and light wheat breads available today con-
fuse participants and staff alike when categorizing bread
types.
Our findings regarding construct validity are similar to
those reported by Raynor and colleagues [2]. In the
present study, all of the correlations between the HFI
major food categories and DHQ servings and nutrients
were statistically significant in the expected direction. Sev-
eral correlations between the HFI major food categories
and child reported servings and nutrients were significant
but attenuated in comparison to those of their parents.
Perhaps it should be expected that food availability and
intake would be more similar from the same informant
(in this case parents) either because he/she purchases
foods he/she prefers and eats. Another potential explana-
tion for the poorer construct validity for the youth is that
the youth's dietary intake was assessed using 24-hour
recalls while the parents completed a food frequency that
assessed usual food intake over a longer period of time.
Further, our findings that an obesogenic food availability
score for the household is significantly and positively
associated with energy intake of both parents and adoles-
cents indicate that high fat foods available in the home
and captured on the inventory are potentially good start-
ing points for public health messages for healthful eating.
The use of previous instruments and literature associated
with energy consumption to determine the selection of
foods for the instrument makes the home food inventory
useful for many purposes. Previous home food availabil-
ity measures were developed for specific study objectives
such as fruit/vegetable consumption or foods associated
with cancer. The broader selection of foods in our inven-
tory increases its utility in nutrition- and obesity-based
intervention programs.
There are several limitations that should be noted when
interpreting the results of the present study. The present
study did not assess test-retest reliability and therefore
cannot address consistency of foods available in homes
over time. However, consistency of foods may be less of
an issue given that Raynor and colleagues [2] conducted
two-week test-retest reliability of the absolute number of
high-fat and low-fat foods and showed substantial stabil-
ity. We also did not assess time since last shopping trip
which could have influenced the home availability of per-
ishable items or preferred foods which may be consumed
more readily [6]. However, our significant correlations
between the HFI scores and the DHQ food servings and
nutrients indicate that perhaps this potential confounder
was not influential. Another potential limitation is that
the new home food inventory does not assess quantity;
participants either check "yes" if the food is present in the
home or "no" to indicate that the food is not present in
the home. Accordingly, a household may "score" high on
the number of fruits and vegetables even when quantity is
limited or low on the number of fruits and vegetables
when quantity is high for only a few foods. In addition,
the list of foods is not an exhaustive list of all possible
foods contributing to the obesity epidemic; however, in
International Journal of Behavioral Nutrition and Physical Activity 2008, 5:55 http://www.ijbnpa.org/content/5/1/55
Page 9 of 10
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selecting foods to be included on the inventory, we bal-
anced the number and types of foods with response bur-
den. Our goal was to create an inventory that was simple
and quick to complete. Attempting to collect data on
more foods, quantities of foods, or more specifics about
foods such as brand, would have impacted the time and
complexity of completing the inventory and added to
response burden. Moreover, our construct validity testing
indicated that the foods measured on the inventory were
significantly associated with energy intake from other
measures, suggesting that we have captured a significant
amount of the variation in the adult diet. Furthermore,
the sample used for construct validity (Sample 2) over
represented educated, Caucasian adults and findings may
not generalize to less educated or minority populations.
The present study had several strengths. It is one of the few
measures of the home food environment that has under-
gone criterion and construct validity testing, and it also
has content validity for a broad range of foods that may be
useful for assessing the obesogeneity of the home food
environment. In particular, the criterion validity in which
staff visited participants' homes to assess food availability
was strong as was the construct validity between the HFI
and the adult dietary intakes.
Conclusion
This new home food inventory is a valid and participant-
friendly tool to assess foods in the home, and may be use-
ful for community-based behavioral nutrition and obesity
prevention research. The inventory builds on previous
measures by including a wide range of foods (both health-
ful and less healthful) rather than foods targeted for a spe-
cific intervention, as well as both perishable and
nonperishable foods. Thus, the inventory may be useful in
studies examining the contextual influences on obesity,
weight gain, and nutritional intake. It might also be help-
ful in studies determining appropriate intervention strate-
gies for individual households or might identify targets
for public health messages. Moreover, the present study
findings indicate that the inventory has both construct
and criterion validity, validity indices not typically
assessed for the same instrument.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JF conducted the literature review, drafted the items for
the Home Food Inventory (HFI), trained staff, collected
data for field testing and criterion validity testing, con-
ducted analyses of the pilot data, and drafted the manu-
script; MN and LL assisted in finalizing items for the HFI,
assisted in scale score development, and edited the manu-
script; SM trained staff, coordinated and collected data,
and edited the manuscript; CH and KP assisted in scale
score development and conducted data analyses. All
authors have given final approval of this manuscript.
Additional material
Acknowledgements
We would like to thank Anne Samuelson who coordinated and collected
data; and Jeanna Rex, Rachel Cope, and Dawn Nelson who collected data.
We would also like to thank the parents and students in the study for their
time and willingness to share their personal lifestyles. The study was funded
as part of the IDEA study (PI: Leslie Lytle, PhD) funded by NCI's Transdis-
ciplinary Research in Energetics and Cancer Initiative (NCI Grant 1 U54
CA116849-01, Examining the Obesity Epidemic Through Youth, Family,
and Young Adults, PI: Robert Jeffery, PhD).
We thank Data Collection and Support Services (DCSS) in the School of
Public Health at the University of Minnesota for their forms design, data
entry, and quality control work for the IDEA study.
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Additional file 1
A table reflecting which foods are included in each food group/subgroup.
Click here for file
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5868-5-55-S1.doc]
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    • "The idea that parents pack the lunch to please the child rather than to inculcate healthy eating patterns is reinforced in other research conducted with the population of families that participated in the trial for Lunch is in the Bag. That research showed the families' home food inventory assessed with a validated food checklist [57] included large variety of vegetables and relatively less availability of sweets and unhealthy salty snacks; but the lunches they packed for their preschool children seldom contained vegetables and often contained sweets and/or chips [58]. The disconnect between home food inventory and food in the children's lunch bags suggests the possibility that the chips and sweets in the home inventory were purchased specifically for lunch packing, responsive perhaps to children's requests and/or the parents' expectations of what the child will be happy to find in the lunch bag. "
    [Show abstract] [Hide abstract] ABSTRACT: Lunches that parents pack for their young children to eat at school or the Early Care and Education (ECE) center fall short of recommended standards. Lunch is in the Bag is a multi-level behavioral nutrition intervention to increase parents’ packing of fruit, vegetables, and whole grains in their children’s lunches. Designed for implementation in ECE centers, the five-week long intervention is followed three months later with a one-week booster. Efficacy of Lunch is in the Bag was tested in cluster randomized trial. Participants were 633 families from 30 ECE centers (15 intervention, 15 control) across Austin, San Antonio, and Houston, Texas, USA. Primary outcomes were servings of fruit, vegetables, and whole grains observed in the children’s parent-packed bag lunches. Servings of refined grains, meats/beans/eggs/nuts, dairy, chips, and sweets also were observed. Data were collected at baseline, post-intervention (6-week follow-up), pre-booster (22-weeks follow-up), and post-booster (28-week follow-up). Time-by-treatment interactions were analyzed separately for each of the food groups using multi-level models to compare changes from baseline. Analyses were adjusted for relevant demographic variables and clustering within centers and parents. The intervention effected increases from baseline to 6-week follow-up in vegetables (0.17 servings, SE = 0.04, P < 0.001) and whole grains (0.30 servings, SE = 0.13, P = 0.018). The increase in whole grains was maintained through the 28-week follow-up (0.34 servings, SE = 0.13, P = 0.009). Fruit averaged more than 1.40 servings with no differences between groups or across time. The intervention prevented increase in sweets (-0.43 servings, SE = 0.11, P < .001, at the 22-week follow-up). Parents persisted, however, in packing small amounts of vegetables (averages of 0.41 to 0.52 servings) and large amounts of sweets and chips (averages of 1.75 to 1.99 servings). The need for and positive effects of the Lunch is in the Bag intervention at ECE centers where parents send bag lunch for their preschool-aged children was confirmed. An important direction for future research is discovery of more options for leveraging the partnership of ECE centers and families to help young children learn to eat and enjoy vegetables and other healthy foods in preference to less healthy choices such as chips and sweets. Trial registration The Clinical Trials Number is NCT01292434.
    Full-text · Article · Dec 2016
    • "Self-regulation is measured using two validated ques- tionnaires [106, 107]. The presence of foods with high reinforcement value in the home environment will be assessed using the Home Food Inventory [108], which yields an obesogenic home food availability score. Additional assessments include maternal weight history [109], physical activity [110], perceived stress [111, 112], sleep quality [113] , nausea/vomiting, provider advice regarding GWG, and postpartum depression [114]. "
    Article · Dec 2016
    • "This suggests that the ability of the checklist to accurately reflect what was found in homes by researchers varied between households. Other validation studies including those by Crockett et al. [6], Miller and Edwards [7] and Fulkerson et al. [8] report similar sensitivity and specificity results to those of the HFAI; however Kappa values were higher. This might be explained by the differences in study design as, instead of comparing checklist data to exhaustive researcher conducted inventories, these other studies validated their participant completed checklists by comparing data to repeated administration of the checklists by researchers. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Despite interest in the importance of the home food environment and its potential influence on children’s diets and social norms, there remain few self-report checklist methods that have been validated against the gold standard of researcher-conducted inventories. This study aimed to assess the criterion validity and reliability of the ‘Home Food Availability Inventory Checklist’ (HFAI-C), a 39-item checklist including categories of fruit, vegetables, snacks and drinks. Methods The HFAI-C was completed by 97 participants of White and Pakistani origin in the UK. Validity was determined by comparing participant-reported HFAI-C responses to data from researcher observations of home food availability using PABAK and weighted kappa statistics. The validity of measuring the amount of items (in addition to presence/absence) available was also determined. Test-retest reliability compared repeated administrations of the HFAI-C using intra-class correlation coefficients. Results Validity and reliability was fair to moderate overall. For validity, the average category-level PABAK ranged from 0.31 (95 % CI: 0.25, 0.37) for vegetables to 0.44 (95 % CI: 0.40, 0.49) for fruits. Assessment of the presence/absence of items demonstrated higher validity compared to quantity measurements. Reliability was increased when the HFAI-C was repeated close to the time of the first administration. For example, ICCs for reliability of the measurement of fruits were 0.52 (95 %CI: 0.47, 0.56) if re-administered within 5 months, 0.58 (95 % CI: 0.51, 0.64) within 30 days and 0.97 (95 %CI: 0.94, 1.00) if re-administered on the same day. Conclusions Overall, the HFAI-C demonstrated fair to moderate validity and reliability in a population of White and South Asian participants. This evaluation is consistent with previous work on other checklists in less diverse, more affluent populations. Our research supports the use of the HFAI-C as a useful, albeit imperfect, representation of researcher-conducted inventories. The feasibility of collecting information using the HFAI-C in large, multi-ethnic samples can facilitate examination of home food availability in relation to exposures such as ethnicity and outcomes including behavioural, social and health outcomes. Future work using the HFAI-C could provide important insights into a modifiable influence with potential to impact health. Electronic supplementary material The online version of this article (doi:10.1186/s12966-016-0381-y) contains supplementary material, which is available to authorized users.
    Full-text · Article · Dec 2016
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