A screening tool to assess obesogenic dietary habits in children

Original Research

E-KINDEX: A Dietary Screening Tool to Assess Children’sObesogenic Dietary Habits

Chrystalleni Lazarou, MSc, Demosthenes B. Panagiotakos, Dr Med Sci, George Spanoudis, PhD, Antonia-Leda Matalas,

PhD

Harokopio University, Department of Nutrition-Dietetics, Athens, GREECE (C.L., D.B.P., A.-L.M.), University of Cyprus,

Department of Psychology, Nicosia, CYPRUS (G.S.)

Key words: dietary index, childhood obesity, dietary patterns, self-monitoring tool, dietary assessment

Background: To date, no published dietary indices have evaluated the overall effects of various dietary

components, beliefs, and practices on the development of obesity in children.

Objective: To develop a dietary index that contains dietary characteristics and practices implicated in the

development of obesity.

Methods: The proposed index (i.e., the E-KINDEX [Electronic Kids Dietary Index]) incorporates 3

subindices: (1) a food groups intake index (13 items), (2) an index related to eating beliefs and behaviors (8

items), and (3) an index that evaluates dietary practices (9 items). The theoretical overall score ranges from 1

(worst) to 87 (best). For validation of the E-KINDEX, multiple linear and logistic regression analyses were

applied that had as dependent outcomes various body composition indices of 622 children (9–13 years) from the

CYKIDS (Cyprus Kids) study. In all models, adjustments were made for age, gender, physical activity level, TV

viewing time, socioeconomic status, breastfeeding, and parental obesity status.

Results: The highest E-KINDEX category (.60 points) was associated with 85% less likelihood of a child

being obese or overweight (odds ratio [OR], 0.15; 95% confidence interval [CI], 0.05–0.41) and 86% less

likelihood of having a waist circumference �75th percentile (OR, 0.14; 95% CI, 0.05–0.43). The correct

classification rate of E-KINDEX (C-statistic) against excess body fat percentage was 84% (CI, 0.74–0.94).

Moreover, for a single SD increase in the index score (i.e., 7.81 units), a decrease of 2.31 6 0.23 kg/m2 in body

mass index (BMI), of 2.23 6 0.35 in percent of body fat (BF%), and of 2.16 6 0.61 cm in waist circumference

was observed. Similar results were found during 1-year follow-up of study participants. In particular, baseline E-

KINDEX levels were inversely associated with children’s BMI at 1-year follow-up (p ¼ 0.024). Latent class

analysis showed that the index has good discriminative value for only 68% of the total sample.

Conclusion: The proposed E-KINDEX could be helpful in dietary assessment and in further research in the

field of childhood obesity.

INTRODUCTION

Childhood obesity is a growing global epidemic [1] that not

only affects wellness during childhood, but also jeopardizes

health in adulthood, as one third to one half of obese preschool-

and school-aged children become obese adults [1,2]. Conse-

quently, prevention of childhood obesity has become an area of

considerable interest among public health professionals [3].

Global increases in overweight and obese children can be

attributed to lifestyle factors and to several other determinants

that influence energy balance. Recently, the American

Academy of Pediatrics (AAP) underlined the need for research

aimed at developing suitable tools to comprehensively assess

all behaviors that could be determinants of obesity [4].

However, effective, simple, and accurate prevention and

screening tools for evaluating overall diet quality and

associating it with obesity development among children are

unavailable.

Lack of research in this area may be due to several

methodologic barriers that do not permit overall evaluation of

unhealthy behaviors. In the dietary assessment, analysis of

overall dietary patterns is a valuable approach that has been

Address reprint requests to: Chrystalleni Lazarou, MSc, 28 Kronos Street, 2369 Ayios Dhometios, Nicosia, CYPRUS. E-mail: [email protected]

Journal of the American College of Nutrition, Vol. 30, No. 2, 100–112 (2011)

Published by the American College of Nutrition

100

used in nutritional epidemiology over the past few years. It

permits the evaluation of dietary habits as a whole, as it

accommodates the complexity of food consumption practices.

To evaluate the level of adherence to specific dietary patterns or

recommendations, various quantitative indices have been

proposed [5,6]. Application of these indices has revealed the

relationship between diet and several risk factors for chronic

disease or mortality from cardiovascular disease and cancer [7–

9]. However, data related to the application of dietary pattern

analysis in children, and consequently of indices associated

with the status of their health, are scarce [10–14]. Only a few

dietary indices that can be used to evaluate the relationship of

various characteristics to obesity status or body mass index

(BMI) in populations of children have been located thus far

[11,12]. However, in general, observed associations were

weak. Thus, the development of valid dietary indices to screen

for unhealthy dietary behaviors that relate to the development

of obesity would be a valuable tool in the prevention of

childhood obesity and in primary health care.

The present study aimed to develop a composite dietary

index while assessing its relationship to obesity levels in

children. The proposed index concurrently assesses and

summarizes (in a single score) several food habits, dietary

beliefs, and practices known to be implicated in the

development of childhood obesity.

METHODS

Development of the E-KINDEX (Electronic KidsDietary Index): An Obesity Dietary Index for Kids

Several reviews [15–17], a recent consensus [18], and the

AAP’S Expert Committee recommendations [4,19–21] suggest

that targeted dietary parameters for obesity prevention and

management in children and adolescents should include eating

habits (including meal patterns, child feeding practices, and

environmental dietary triggers such as advertising) and

consumption of foods that are rich in calories but of low

nutritional value. Moreover, evidence from intervention studies

supports the notion that psychological and behavioral aspects

of food consumption may be of more importance in obesity

management than actual diet composition [20,22,23].

Following the previous rationale, we chose to include in the

proposed index several factors from each of the 3 main aspects

of the diet—diet composition, eating habits (including meal

patterns), and dietary beliefs—to develop a dietary obesity

index. We named this index the E-KINDEX (Electronic Kids

Dietary Index) because it is planned to be available in

electronic form as well. To assess the effects of combined

dietary behaviors on overall risk of developing obesity, the E-

KINDEX incorporates (into a score) the components of 3

dietary indices: (1) the Foods E-KINDEX (consisting of food

group components), (2) the Dietary Behavior E-KINDEX (with

components related to the psychology of eating), and (3) the

Dietary Habits E-KINDEX (composed of dietary practices

[including meal patterns] components); these indices comprise

13, 8, and 9 components, respectively.

The rationale behind the development of 3 distinct indices

and their combination is 3-fold: (1) to assess the relative

contribution of each dietary aspect to the overall score and thus

determine its individual association with observed obesity

status; (2) to determine whether the approach of total diet

assessment offers distinct advantages over the evaluation of

single dietary variables; and (3) to enable potential users to gain

insight into how each factor contributes to a final score, and

thus to facilitate further decisions and measures regarding

obesity prevention and management.

A detailed description of the 3 main parts of the index,

together with the scoring method, is provided here.

E-KINDEX components 1 through 13 evaluate the overall

quality of diet, as this is assessed by the balance of food choices

and methods of cooking; components 14 through 21 evaluate

psychological aspects of eating; and components 22 through 30

evaluate aspects of diet quality with reference to dietary practices

and meal patterns. The main focus is on food components and

additional aspects of the diet, as previously recommended in the

literature [11]. Components and relevant questions have been

designed in such a way as to permit their use by children and by

their parents or their administration by an interviewer (pediatri-

cian, dietitian, or nurse). Such an index is deemed suitable for

intervention and health education purposes.

Part 1 (Foods E-KINDEX) consists of 11 foods or food

groups and 2 aspects of cooking techniques; it seeks to assess the

quality of the diet from the perspective of varied and balanced

food choices. Overall quality of diet is a potential contributory

factor to adiposity [4,19–21,24]. The AAP and other scientific

bodies emphasize the importance of overall diet quality and

balanced food choices [4,15,17] in prevention and management

of childhood adiposity. Foods from all food groups were

included to address the basic principle of dietary variety.

Moreover, food items were selected on the basis that as whole

foods or as sources of fat, fiber, sugar, and less so, calcium, they

have been associated with obesity [15].

Part 2 (Behavior E-KINDEX) consists of 8 dietary attitudes

and their related behaviors; it aims to serve as a proxy indicator

of cognitive schemas regarding diet. Cognitive schemas are

fundamental in the development and shaping of children’s

dietary preferences, choices, and behaviors, and are significant

in the development of obesity [21,23].

Part 3 (Dietary Habits E-KINDEX) includes 9 items that

evaluate meal patterns and behaviors, as several studies have

examined the connection between these particular meal

patterns and behaviors and obesity [4,15,17].

A 4-point scoring system comprising monotonic and

nonmonotonic functions (ranging from 0 to 3 or the reverse)

was used for most components of the index. Nonmonotonic

JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION 101

A Dietary Screening Tool Associated with Obesity

functions were used primarily for items such as sweets, soft

drinks, fried foods, and meat items, taking into account the fact

that guidelines call for moderate consumption rather than total

exclusion of these foods from the diet [17]. Nonmonotonic

scoring was used when (1) intermediate choices were well below

the recommended consumption (e.g., ‘‘frequency of eating

bread,’’ ‘‘frequency of eating meals in afternoon school’’); (2)

intermediate choices were acceptable as the top range category

(e.g., ‘‘frequency of eating legumes’’); (3) top range choices

seemed unrealistic (with the possibility of an increased

likelihood of overestimation, e.g., ‘‘frequency of eating fish

and seafood’’); and (4) only 2 choices were possible: yes/no

(e.g., ‘‘whether eating foods because they are advertised’’).

With the rationale of using wide range scoring to develop

informative and accurate scales [25], we used scores ranging

between 0 and 3 for most items of the E-KINDEX. The specific

scores used for each item were based on existing dietary

recommendations for children [4,17,18], given available

evidence from published studies regarding the influence of

diet on childhood obesity [4,15,17] and the results of univariate

analysis of each variable of interest to obesity status, as

suggested by experts in the field of dietary index development

[25,26]. Higher scores indicate a healthier quality of diet,

which inhibits the development of childhood obesity. Maxi-

mum score values (i.e., 2 or 3) varied according to (1) the

importance of the specific item in the overall dietary

assessment, and (2) the results of univariate analysis of each

variable of interest to the obesity status. Thus, an indirect

weight, as suggested by the literature on development of

dietary screening tools [25,26], was given to items considered

more significant in the development of obesity. The method of

E-KINDEX development is depicted and summarized in Fig. 1.

The theoretical score of E-KINDEX ranges from 1 (worst)

to 87 (best). We purposefully let the lower response category

be equal to 1 point (and not 0) to render the index more user

friendly when used by children, as suggested by childhood

educational psychology [27]. Other dietary indices in adult and

child populations have used the value of 1 or some other

positive number as the lower cutoff value [28,29]. The

components of E-KINDEX, along with the scoring system,

the upper and lower cutoff points of each component, and the 4

E-KINDEX score range categories (based on degree of

achievement of maximum points), are presented in Table 1.

Validation Dataset

To test the diagnostic accuracy of the proposed index, data

from the Cyprus Kids Study (CYKIDS) were used. In 2005, a

total of 1589 children from the fourth, fifth, and sixth grades

(9–13 years; mean age, 10.7 6 0.98 years) from 24 primary

schools in all urban and rural areas of Cyprus were identified as

suitable participants; 1140 of these children agreed to

participate in the study (72% participation rate). Sampling

was multistage and was stratified by the number of students in

each of the 5 provinces, as provided by the Ministry of

Education (data available on request from the Department of

Primary Education), and by place of residence (place of school

was used as a proxy)—urban or rural—as provided by the

Cyprus Statistical Service. Further details of the sampling

procedure have been provided elsewhere [30]. The study

design was approved by the Harokopio Institutional Review

Board and by the Cyprus Ministry of Education and Culture

(Department of Primary Education). Signed informed consent

was provided by the parent or guardian of each child.

Dietary assessment was based on a validated [30] semiquan-

titative food frequency questionnaire (FFQ) consisting of 154

food items (including all of the foods commonly used in the

Greek-Cypriot cuisine) and on 3 supplementary questionnaires,

which evaluated other aspects of dietary habits. A detailed

description of the questionnaires is provided elsewhere [30].

Questionnaires were administered to students by a registered

dietitian during school hours between February and June 2005,

as prescribed by a written protocol describing the standardized

method for dietary data collection. A validated [31] physical

activity index (PAI) was calculated on the basis of 2 variables

that measured the frequency of all walking and running activities

performed on a weekly basis. Additionally, daily average TV

(television) viewing time (including weekends) was assessed.

Anthropometric data (i.e., weight, height, and waist

circumference [WC]) were collected from a subsample of

622 children, according to a standard protocol described by

Heymsfield et al. [32]. Obesity was defined by the International

Obesity Task Force on the basis of age- and gender-specific

body mass index (BMI) cutoff criteria [33]. Body fat

percentage (%BF) was calculated using the Deurenberg

formula [34]. Furthermore, a new variable called generalized

obesity [31] was computed, based on children’s BMI status and

WC, that could take 1 of 3 values: (1) both BMI and WC

normal (WC , 75th percentile); (2) either BMI or WC above

normal measures (WC � 75th percentile); or (3) both BMI and

WC above normal (WC � 75th percentile).

In addition to direct measurements, the weight and height of

all children were reported via questionnaire by parents at 2

times that were 1 year apart. Finally, parental overweight and

obesity status were evaluated via parental self-report as

described elsewhere [35].

Family socioeconomic status (SES) was defined by the

InterCollege Research Center of Cyprus, based on parents’

profession and educational level; the highest level of profession

reported by either parent was used as a proxy of the family’s

SES level, along with family income [35].

Statistical Analyses

The distribution of the E-KINDEX score is presented as

the mean, standard deviation, coefficient of variation,

tertiles, and range. The normality of the E-KINDEX score

variable was evaluated with the use of P-P plots, that is,

102 VOL. 30, NO. 2


measures of kurtosis and skewness, and was tested using the

Kolmogorov-Smirnov test. Collinearity among the various

E-KINDEX components as well as among the subindices

was assessed by correlation coefficients. A cutoff point

analysis was used to determine the optimal value of the E-

KINDEX score and each of its 3 subindices, which could

discriminate among children based on their obesity status.

Sensitivity and specificity values and the relevant 95%

confidence interval (CI) were calculated for the overall E-

KINDEX score, as well as for the 3 subindices. Descriptive

characteristics of the sample are presented according to the

level of the E-KINDEX score. Continuous variables are

presented as means 6 SD, whereas categorical variables are

presented as frequencies. Associations between normally

distributed variables were tested by ANOVA using the

Bonferroni correction rule to account for inflation of type I

error due to multiple comparisons; the Kruskal-Wallis test

was used for nonnormally distributed continuous variables.

Associations between categorical variables were tested by

contingency tables and by the v2 test.

Fig. 1. Flowchart depicting the methods of E-KINDEX development.



Multiple linear regression analysis was applied to evaluate

the association between E-KINDEX score and body compo-

sition indices (BMI, %BF, and WC) at baseline and at 1-year

follow-up; logistic regression analyses were applied to evaluate

the association between E-KINDEX score and obesity status,

WC, and generalized obesity levels. All models were adjusted

for age, gender, physical activity level, TV viewing time, SES,

breastfeeding, and parental obesity status.

Table 1. E-KINDEX Components, along with the Scoring System and the Upper and Lower Cutoff Points for Each Componenta

Foods E-KINDEX 0–37

Response Categories and Scoring System

RangeNever 1–2 Times/wk 3–5 Times/wk �6 Times/wk

Consumption frequency:

Bread 0 1 1 3 0–3

Cereals and grain foods (excluding bread) 0 1 2 2 0–2

Fruits and fruits juices 0 1 2 3 0–3

Vegetables 0 1 2 3 0–3

Legumes 0 1 3 3 0–3

Milk 0 1 2 3 0–3

Fish and seafood 0 3 3 2 0–3

Meat 1 3 0 1 0–3

Salted and smoked meat food 3 1 1 0 0–3

Sweets and junk food 2 2 1 0 0–2

Soft drinks 3 3 1 0 0–3

Fried food 2 3 1 0 0–3

Grilled food 0 3 3 2 0–3

Dietary Behavior E-KINDEX 0–23 Response Categories and Scoring System Range

Never Sometimes Much Very Much

Degree to which:

I think that my diet is healthy 0 1 2 0 0–2

I think that my weight is above normal 3 2 1 0 0–3

I have tried to be on a diet 3 2 1 0 0–3

I feel guilty when I eat something that I know is not healthy 3 2 1 0 0–3

I feel guilty when I eat something that I know is fattening 3 1 1 0 0–3

I eat things that I know are fattening 3 2 0 0 0–3

My parents insist upon eating all my food 0 1 2 3 0–3

I eat something I like even I am not hungry 3 2 1 1 0–3

Dietary habits E-KINDEX 1–27 Response Categories and Scoring System Range

Almost Daily 2–4 Times/wk Once a Week 1–3 Times/mo

Frequency of having breakfast 3 2 1 0 0–3

Response Categories and Scoring System Range

Never 1 Time �2 Times

Frequency of having eaten in fast food restaurants or

other eating places (excl houses) during the 2 previous days

3 2 1 1–3


Almost Daily 1–4 Times/wk 1–3 Times/mo Seldom

Frequency of eating least favorite but ‘‘healthy’’ food 3 1 1 0 0–3

Frequency of eating with family 3 1 1 0 0–3

Frequency of eating alone 0 1 2 3 0–3


Almost Daily 1–4 Times/wk 1–3 Times/mo

Frequency of eating meals in afternoon school 0 1 3 0–3


2–3/day 4–5/day �6/day

Number of main meals and snacks 0 3 3 0–3


Yes No

Whether eating foods because they are advertised 0 3 0–3

Whether eating whatever food is prepared in home 3 0 0–3

a Possible range of the overall E-KINDEX score: 1 to 87.

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Receiver operating characteristic (ROC) curve analysis was

used to estimate the sensitivity and specificity characteristics of

the proposed index and its subindices, and thus finally to

determine the diagnostic ability of the index.

C-statistics derived from ROC curves were used to assess

the discriminative power of the logistic regression models. The

internal reliability of the index was examined by Cronbach’s

alpha statistic (values of the statistic �0.6 suggest that the items

may be considered 1-dimensional and may be combined into a

single index or scale [36]). All reported p values were based on

2-sided hypotheses and on the 0.05 level of statistical

significance. Statistical analyses were performed using the

Statistical Package for the Social Sciences (SPSS), release 14.0

(SPSS Inc, Chicago, IL; 2005).

Latent class cluster analysis (LCA), using Latent GOLD 4.5

software (Vermunt & Magidson, 2008; Statistical Innovations,

Belmont, MA), was performed to evaluate potential heteroge-

neity among subindices of the E-KINDEX in relation to obesity

status. LCA was designed to reveal subgroups of children for

whom the E-KINDEX had low predictive value. The C-statistic

was computed using unweighted and weighted predicted values

for each latent cluster. The Akaike Information Criterion (AIC)

was calculated; lower values suggest a better model. The

weight was the proportion of each cluster in the total sample.

RESULTS

Description of the E-KINDEX Score

The range of the total E-KINDEX score in the present

sample was 32.0 to 77.0 points (mean 6 SD, 58.2 6 7.8).

Scores did not differ between genders (p ¼ 0.173). The

frequency distribution of the score was symmetrical and normal

(data not shown here). Correlation coefficients between total

and individual component scores ranged from r , 0.001 to

0.47 (with p values between 0.99 and ,0.001; data not shown).

Similarly, all 3 individual correlation coefficients among the 3

subindices were less than 0.118 (p . 0.05). The internal

reliability of the E-KINDEX as examined by Cronbach’s alpha

statistic was 0.601, which suggests that the index can be

proposed as a fairly reliable tool to screen for obesity risk–

linked dietary behaviors [36].

Descriptive Characteristics of Children Based onTheir E-KINDEX Score

Selected sociodemographic, anthropometric, and lifestyle

characteristics are presented in Table 2. With regard to obesity

status, the E-KINDEX was 51.4 (69.2) among obese children,

56.5 (67.5) among overweight children, and 59.4 (67.4)

among children of normal weight (p , 0.0001) (Table 2).

These values reflect an average of 59%, 65%, and 73%

achievement of dietary goals as evaluated by E-KINDEX for

obese (OB), overweight (OW), and normal weight (NW)

children, respectively. Relationships to other characteristics are

provided in Table 2.

Evaluation of the Accuracy of the E-KINDEX

The prevalence of OW and OB children in this nationwide

sample (of about 2% of the reference population) was 22.7%

and 7.6%, respectively. Given that no significant differences

between genders were observed regarding the prevalence of

OW/OB (v2¼2.37; df¼2; p¼0.144) or scores obtained for E-

KINDEX items, all analyses were performed without gender

stratification. Table 3 presents the characteristics of E-KINDEX

based on its ability to discriminate children’s obesity status.

Overall, the diagnostic ability of the score (as assessed by area

under the curve [AUC] from ROC analysis) appears to be more

effective in screening for obesity than for overweight status in

this sample. Cutoff point analysis showed that the optimal

value that best discriminates OW/OB from NW is 61 out of 87

(which is the highest possible total score of the index)

(sensitivity ¼ 74%), and OB from NW/OW children is 53

out of 87 (sensitivity ¼ 61%). Details on characteristics of the

overall E-KINDEX and its subindices are provided in Table 3.

Multiple linear regression analysis, after adjustments for

age, gender, physical activity, TV viewing, SES, breastfeeding,

and parental obesity status, revealed that each 1 SD (i.e., 7.81

points) increase in the E-KINDEX score was associated with a

2.31 6 0.23 kg/m2 decrease in BMI (p , 0.001), a 2.23 6 0.35

decrease in calculated %BF (p , 0.001), and a 2.16 6 0.61 cm

decrease in WC (p , 0.001).

Table 4 presents results from logistic regression models

with 4 obesity markers (i.e., BMI, %BF, WC, and generalized

obesity) as dependent outcomes. Significant and consistent

inverse associations between the E-KINDEX score and these

outcomes were observed in all models. Specifically, compared

with children belonging to the lowest E-KINDEX category

(i.e., �49 points), those with scores in the second, third, and

fourth categories had, on average, a 73%, 76%, and 85%

decreased likelihood of being overweight/obese, respectively.

Similarly, children with scores that fell into the second, third,

and fourth categories were, respectively, 62%, 78%, and 86%

less likely to exhibit WC � 75th percentile (Table 4).

Application of these 4 models in data obtained at 1-year

follow-up revealed similar results. In particular, being classified

in the highest scored category was associated with an 84%

decreased likelihood of an increase in BMI greater than 3 kg/

m2 in 1 year (odds ratio [OR], 0.16; 95% CI, 0.04–0.74).

The C-statistic of the logistic models ranged from 0.71 to

0.84 (Table 4), suggesting good predictive ability for the

population studied [37,38]; nevertheless, the predictive ability

of these models cannot be generalized.

LCA was performed and children’s BMI classification

(categorical) was used as the indicator variable, while

categorical variables of the 3 subindices were used as



Table 2. The E-KINDEX Score in Relation to Various Characteristics of the Sample (N ¼ 634)

Mean (SD) p

Gender 0.173

Girls 58.64 (7.99)

Boys 57.78 (7.57)

Age groups 0.376

10 years 58.54 (5.74)

11 years 57.58 (8.51)

12 years 58.83 (7.50)

13 years 58.22 (7.72)

Obesity status , 0.0001

Underweight/Normal weight 59.42 (67.36)

Overweight 56.54 (67.50)

Obese 51.43 (69.18)

Waist circumference in cm , 0.0001

�62 59.34 (7.21)

63–67 59.00 (7.88)

68–76 59.37 (7.54)

�77 53.56 (7.79)

Socioeconomic status 0.015

High 60.44 (7.34)

Medium 58.41 (7.67)

Low 57.91 (7.62)

Place of residence 0.821

Urban 58.18 (7.92)

Rural 58.32 (7.68)

Diastolic blood pressure, mmHg 0.012

�80 58.48 (7.82)

�81 53.90 (7.57)

Systolic blood pressure, mmHg 0.039

�120 58.49 (7.88)

�121 55.34 (7.36)

Breastfed 0.034

No 57.26 (7.40)

Yes 58.96 (7.65)

How parents rate their own dietary habits 0.049

Good/Very good 59.14 (7.99)

Modest/Need improvement 57.82 (7.23)

Weight status of father 0.864


Overweight/Obese 58.72 (7.62)

Weight status of mother 0.016


Overweight/Obese 57.33 (7.65)

Parental obesity status 0.082

Both lean 59.60 (7.82)

Both obese 57.98 (7.77)

Father obese/mother lean 59.03 (7.59)

Father lean/mother obese 56.54 (7.61)

Physical activity index 0.047

None ¼ 0 times/wk 56.23 (8.96)

Some ¼ 1–2 times/wk 57.59 (7.74)

Much ¼ 3–5 times/wk 59.32 (7.50)

Every day ¼ 6–7 times/wk 57.85 (8.15)

Mean screen time 0.005

Up to 2 hours/d 59.35 (7.66)

.2 hours/d 57.50 (7.86)

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covariates. According to AIC, the best solution for LCA was

the one in which 3 clusters were revealed (AIC¼�2.78. vs 1-

cluster model [AIC ¼ 21.15] and 2-cluster model [AIC ¼3.22]). Fig. 2 presents the characteristics of these 3 clusters in

relation to obesity status and scores obtained for the 3 E-

KINDEX subindices (the horizontal axis presents the catego-

ries of the obesity variable, the second [by default] category of

each E-KINDEX subindex, and the position of each cluster,

while the vertical axis presents the mean score for each variable

on a scale from 0 to 1).

Children who belong to the first cluster represent 68% of the

total sample and are those for whom the E-KINDEX seems

(only on the basis of the LCA) to have a highly discriminative

value (i.e., higher scores for normal weight and lower scores

for overweight/obese). For the remaining 32% of the children,

the E-KINDEX score seems to have a poor to low

discriminative value (except in OB children). The unweighted

C-statistic of this classification was 0.89 for NW vs OW/OB

(weighted C-statistic, 0.91). When the same analysis was

repeated, having in place of the indicator the BMI classification

with 2 classes (i.e., NW vs OW/OB), the best solution was the

one with 2 clusters (based on AIC values), representing 75%

and 25% of the sample, respectively (unweighted C-statistic¼0.76; weighted C-statistic ¼ 0.84). The analysis was repeated

with adjustments for all variables, as in logistic regression

models, and with BMI classification as the indicator (i.e., NW

vs OW/OB) and total E-KINDEX index (categorical) as the

covariate in the first place, and the 3 subindices as covariates

(categorical) afterward. With the total E-KINDEX variable in

the model, the best solution was that with 2 clusters (based on

AIC values), representing 72% and 28% of the sample,

respectively (unweighted C-statistic ¼ 0.80; weighted C-

statistic ¼ 0.78). With the 3 subindices as covariates, the best

solution (based on AIC values) was the one with 2 clusters,

representing 82% and 18% of the sample, respectively

(unweighted C-statistic¼ 0.76; weighted C-statistic ¼ 0.80).

DISCUSSION

The American Academy of Pediatrics recently stressed the

urgent need for further research in the area of obesity

prevention, so tools might be developed that are appropriate

for comprehensive examination and appraisal of every behavior

that may be an obesity determinant [4]. The present work

presents the E-KINDEX, a dietary index that correlates with

obesity status in children. This index when applied to a

representative sample of Cypriot children showed a good

ability to classify them according to overweight/obesity status.

The relationship of the proposed dietary index to obesity status,

Table 3. The E-KINDEX Score and Its Diagnostic Ability in Relation to Obesity and to Overweight and Obesity in Preadolescent

Cypriot Children

AUC Cutoff Point Sensitivity Specificity

Foods E-KINDEX

OW/OB vs NW 54.00 (48.10–59.80) 27.5/37 82.03 (78.46–85.54) 24.92 (20.94–28.90)

OB vs NW/OW 65.70 (56.20–75.10) 22.5/37 62.86 (58.41–67.31) 61.48 (57.00–65.96)

Dietary Behavior E-KINDEX

OW/OB vs NW 64.70 (59.30–70.10) 13.5/23 48.03 (43.66–52.40) 76.0 (72.26–79.74)

OB vs NW/OW 65.50 (57.20–73.80) 13.5/23 52.00 (47.63–56.37) 71.0 (67.03–74.97)

Dietary Habits E-KINDEX

OW/OB vs NW 61.10 (55.30–66.80) 19.5/27 61.54 (56.82–66.26) 57.55 (52.75–62.35)

OB vs NW/OW 59.30 (50.10–68.50) 19.5/27 65.0 (60.37–69.63) 53.26 (48.42–58.10)

Foods E-KINDEX and Dietary Behavior E-KINDEX

OW/OB vs NW 63.10 (57.10–69.10) 35.5/60 42.62 (37.88–47.36) 77.03 (73.00–81.06)

OB vs NW/OW 74.70 (65.60–83.70) 32.5/60 50.00 (45.21–54.79) 88.54 (85.49–91.59)

Foods E-KINDEX and Dietary Habits E-KINDEX

OW/OB vs NW 57.60 (50.80–64.40) 39.5/64 34.02 (28.69–39.45) 76.89 (72.15–81.63)

OB vs NW/OW 67.10 (55.40–78.90) 42.5/64 66.67 (61.52–71.82) 58.05 (52.66–63.44)

Dietary Behavior E-KINDEX and Dietary Habits E-KINDEX

OW/OB vs NW 68.70 (62.80–74.60) 34.5/50 71.68 (67.03–76.33) 57.66 (52.56–62.76)

OB vs NW/OW 67.90 (58.70–77.10) 34.5/50 78.79 (74.57–83.01) 51.22 (46.12–56.32)

Overall E-KINDEX

OW/OB vs NW 64.10 (57.30–70.90) 60.5/87 73.91 (68.97–78.85) 46.22 (40.62–51.82)

OB vs NW/OW 72.50 (61.10–83.90) 52.5/87 60.87 (55.38–66.36) 79.00 (74.42–83.58)

NW ¼ normal weight; OB¼ obese; OW ¼ overweight.



central obesity status, excess body fat, and generalized obesity

was evaluated. Overall, children with better E-KINDEX scores

(i.e., greater adherence to healthy dietary patterns and

behaviors) had lower levels of obesity.

It is noteworthy, that beyond ascertainment of the

diagnostic accuracy of the E-KINDEX, LCA was applied to

test for heterogeneity of the proposed index within obesity

classes. The observed heterogeneity (which was expected) may

be attributed to several factors other than diet, such as genetics

and lifestyle aspects. In fact, a substantial body of research

concludes that diet may not be associated with obesity status

[15]; this may be due to misclassification in the E-KINDEX

categories resulting from incorrect information provided by

children, or to misclassification in the obesity status categories,

as obesity was assessed by the proxy measure of BMI and not

via a direct method. Nevertheless, it should be pointed out that

Table 4. Results from Multiple Logistic Regression Analysis That Evaluated the Association between Obesity Status (Dependent)

and E-KINDEX Score (Independent)*

Variables Included

in the Model

Model for Obesity1

(OR [95% CI])

Model for Excess

Body Fat2

(OR [95% CI])

Model for Above

Normal Waist3

(OR [95% CI])

Model for

Generalized Obesity4

(OR [95% CI])

Model for BMI

Difference5

(OR [95% CI])

E-KINDEX categories

�49 points 1 1 1 1

50–54 points 0.27 (0.08–0.87) 0.21 (0.04–1.21) 0.38 (0.11–1.35) 0.39 (0.12–1.21) 0.13 (0.01–1.57)

55–59 points 0.24 (0.08–0.73) 0.27 (0.05–1.36) 0.22 (0.06–0.77) 0.26 (0.08–0.77) 0.57 (0.11–2.81)

.60 points 0.15 (0.05–0.41) 0.07 (0.01–0.39) 0.14 (0.05–0.43) 0.20 (0.07–0.53) 0.16 (0.04–0.74)

Physical Activity Index

0 times/wk 1 1 1 1 1

1–2 times/wk 0.37 (0.11–1.23) 0.83 (0.08–8.59) 0.47 (0.12–1.82) 0.48 (0.15–1.57) 0.39 (0.05–2.95)

3–5 times/wk 0.37 (0.11–1.21) 1.72 (0.18–16.35) 0.40 (0.10–1.53) 0.40 (0.12–1.28) 0.52 (0.07–3.73)

6–7 times/wk 0.21 (0.06–0.78) 0.83 (0.08–9.26) 0.29 (0.07–1.23) 0.27 (0.08–0.95) 0.29 (0.03–2.58)

TV viewing time

Up to 2 hours/d 1 1 1 1 1

2þ up to 4 hours/d 1.29 (0.57–2.91) 1.55 (0.36–6.57) 1.19 (0.45–3.18) 1.25 (0.57–2.73) 1.64 (0.42–6.31)

.4 hours/d 1.23 (0.46–3.29) 0.92 (0.17–5.11) 1.55 (0.50–4.86) 1.43 (0.56–3.67) 0.38 (0.06–2.65)

Parental obesity status

Both lean 1 1 1 1 1

Father lean/mother obese 4.02 (1.14–14.15) 3.84 (0.26–57.26) 1.84 (0.37–9.17) 3.38 (0.99–11.56) 1.21 (0.18–8.25)

Father obese/mother lean 1.97 (0.79–4.88) 6.73 (0.71–64.01) 1.41 (0.48–4.10) 1.83 (0.78–4.29) 0.89 (0.21–3.78)

Both obese 2.54 (0.88–7.32) 12.22 (1.12–133.10) 4.20 (1.30–13.62) 2.73 (1.01–7.40) 0.48 (0.08–2.91)

Breastfeeding

No 1 1 1 1 1

Yes 0.78 (0.35–1.71) 0.23 (0.07–0.79) 0.71 (0.27–1.87) 0.89 (0.41–1.94) 1.72 (0.40–7.39)

Socioeconomic status

High 1 1 1 1

Medium 0.85 (0.38–1.92) 1.17 (0.33–7.92) 0.65 (0.26–1.66) 0.81 (0.38–1.75) 1.60 (0.35–7.33)

Low 0.94 (0.38–2.33) 2.78 (0.48–16.02) 0.43 (0.14–1.28) 0.70 (0.29–1.71) 3.54 (0.76–16.42)

Gender

Boy vs girl 1.48 (0.75–2.94) 0.33 (0.09–1.25) 2.98 (1.31–6.77) 1.20 (0.69–2.08) 1.45 (0.42–4.96)

Age

Per 6 months 0.94 (0.53–1.67) 2.28 (0.78–6.65) 2.28 (1.13–4.60) – –

0.34 (0.07–1.73)C-statistic 0.73 (0.65–0.81)6 0.84 (0.74–0.94) 0.78 (0.69–0.88) 0.72 (0.64–0.80) 0.57 (0.48–0.67)

1 Depended variable overweight or obesity vs normal weight status.2 Depended variable obesity (%BF . 30) vs normal weight status (%BF , 30).3 Depended variable waist circumference �75th percentile vs ,75th percentile.4 Depended variable overweight or obese (based on BMI) and waist circumference �75th percentile vs normal weight (based on BMI) and waist circumference �75th

percentile.5 Depended variable BMI difference .3 kg/m2 vs up to 3 units between 2 evaluations (i.e., in 2005 and in 2006).6 C-statistic for obese vs overweight/normal weight is 0.78 (0.67–0.88).

* Statistically significant results are shown in bold.

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the E-KINDEX score seems to have high discriminative value

for ;8 out of 10 children in this population, especially for

obese children.

The E-KINDEX consists of 3 subscales: (1) the Foods E-

KINDEX, (2) the Behavior E-KINDEX, and (3) the Dietary

Habits E-KINDEX; of these subscales, the Behavior E-

KINDEX subindex had the best discriminative ability and

contributes significantly to the discriminative ability of the

overall E-KINDEX score. Furthermore, the most significant

combination of subscales for association with obesity status

was the Behavior E-KINDEX and the Dietary Habits E-

KINDEX, followed by the Behavior E-KINDEX and the Foods

E-KINDEX. The latter may imply that dietary beliefs and

patterns are more important determinants of obesity develop-

ment than actual food choices. It is well known that behaviors

are expressions of cognitive schemas [39]. Therefore, dietary

attitudes and feelings that may serve as valid indicators of these

cognitive schemas seem to be fundamental predecessors of

children’s dietary preferences, choices, and habits [23,39], and

therefore are important targets in obesity prevention.

The rationale for including the third subindex (i.e., the

Dietary Habits E-KINDEX) as part of the overall index, even

though the AUC value and associated CIs of the overall index

and of the combined Foods and Dietary Behavior subindex

seemed to be very similar (as seen in Table 3), is based on the

following: (1) overwhelming evidence from research that

points to the importance of dietary habits, such as meal habits

(the variables of the third subindex), in childhood obesity (as

discussed in the Methods section); (2) the better combination of

sensitivity (and specificity) values (as seen in Table 3) and the

AUC values of the overall index as well; (3) the very good beta

values obtained in linear regression models when the validity

of the E-KINDEX against obesity is tested, holding indepen-

dent in different models the overall index, as well as the foods

and dietary behavior subindices; and (4) the value of

Cronbach’s alpha, which was acceptable (as mentioned in

Methods) when all questions in the overall E-KINDEX were

included.

The proposed index showed high discriminating ability

across several sociodemographic factors and health behaviors.

Children who had low E-KINDEX scores had low SES, as well

as lower levels of physical activity and higher levels of

sedentary behaviors (as assessed by TV viewing). Furthermore,

parents of children with high scores appeared to have good or

adequate dietary habits and appeared to be within a normal

weight range. These findings are of importance in obesity

prevention and management, as they may be used to identify

target groups for intervention.

Fig. 2. The profile plot for the best fitting 3-cluster model, showing the probability of each cluster belonging to a higher category of each E-KINDEX

subindex. By default, the last category for dichotomous variables and all categories for the nominal variable of obesity status are displayed.



Previous research conducted on the development and

validation of dietary indices in children is limited. Among

the few published dietary indices for children, all were

originally validated against dietary quality and nutritional

status only [11–13,40], and only a few were tested against

obesity [11–13,41–43]. Specifically, of those dietary indices

tested against obesity in children, most failed to show any

significant associations [11,12], or, when significant inverse

relationships were shown, they did not adjust for possible

confounders [11,41,42], or the adjustments made were only

partial [42,43].

The discrepancy observed between findings of the present

study and those of previous studies that showed no significant

results when dietary indices against obesity were evaluated

may be attributed to the initial aims of these studies.

Specifically, previously tested indices were developed to assess

dietary quality in children, whereas in the E-KINDEX, dietary

components associated with childhood obesity were purpose-

fully included to assess the obesogenic dietary habits of

children. The discrepancy may also be due to the fact that

dietary factors may not be strongly associated with obesity

status, as demonstrated by other studies that did not use dietary

indices [15].

Limitations and Strengths

Despite the significance of the findings already described,

the present study has some limitations that might affect the

characteristics of the proposed index. First, obesity was

assessed according to BMI, which although highly specific is

not sensitive for the identification of obesity [44]; therefore,

some cases may have been misclassified. Thus, the diagnostic

ability of the E-KINDEX may have been incorrectly evaluated.

Second, direct anthropometric measurements were not avail-

able prospectively; parentally referenced values were used

instead. Third, validation of this index remains to be tested in

more extensive follow-up studies and with other ethnic

populations; this will permit objective assessment of the

discriminatory/predictive ability of the test. Moreover, it should

be emphasized that the accuracy of a dietary index is limited to

the availability and strength of dietary guidelines related to the

disease or health index under investigation [45]. This is

particularly true for childhood obesity, in which the contribu-

tions of individual dietary factors are investigated; in general,

no definite answers have been obtained as to what, how, and to

what degree particular dietary factors contribute to obesity [15].

As a result, the selection of individual components, the

definition of cutoff points for each component, and the relative

contribution of each component to the total score possess a

degree of subjectivity [45]. Strengths of this work include the

detailed analytical procedures followed and the fact that it is a

first attempt at developing a monitoring tool that concurrently

assesses a wide range of dietary behaviors in relation to

childhood obesity.

Implications and Applications

The proposed tool may be used as an aid for various

preventive and management procedures in attempts to confront

obesity in children. It may be used by dietitians, pediatricians,

educators, and even parents, as a practical set of guidelines for

the prevention or management of childhood obesity. Although

various guidelines exist [4,15,20], they have not been tested as

a set for their synergistic contribution to obesity prevention. It

has been demonstrated here that all 3 aspects of dietary

behavior (i.e., diet composition, psychology of eating, and

dietary-meal behaviors) should be taken into consideration

when dietitians assess and advise for childhood obesity. Thus

dietitians could use this tool to demonstrate to their clients the

combined role of several dietary factors in obesity prevention.

Furthermore, even though children often know what and how

they should eat to prevent/manage obesity [46], behavioral

modification and maintenance are often achieved at very low

rates [46]. A dietary index such as the E-KINDEX may serve as

a visual educational tool (depicts assessment in the form of a

number/a score) to indicate to a child where she or he stands,

thus promoting compliance with dietary guidelines. It can

therefore provide continuous feedback in a measurable and

easily understandable way to children and their parents.

Moreover, individual components of the proposed index can

be used to determine specific areas and goals for improvement

in relation to its components; thus counseling and dietary

intervention can be targeted into everyone’s individual needs.

For instance, dietitians may use it as counseling tool to

demonstrate how small changes in dietary habits can lead to

significant improvements that may have an impact on obesity

status as well.

The proposed index has other potential applications in the

public health sector and in research. The total score of the index

could potentially be used to rank and/or compare within and

between individuals or groups of individuals (such as school

populations or certain subgroups within a population), to

identify individuals at high risk of developing obesity, thus

helping to target public health programs for these populations,

and to monitor trends in dietary patterns over time. In addition,

this score may help the clinician to gain an understanding of

why specific populations have higher or lower prevalence of

obesity, thus supporting obesity prevention programs. Finally,

the E-KINDEX can be used in research to investigate

associations between eating patterns and obesity or other

health outcomes.

CONCLUSIONS

In summary, this study may represent the first attempt to

develop and test a self-monitoring tool that holistically assesses

aspects of dietary factors that may be implicated in the

development of childhood obesity. The proposed E-KINDEX

110 VOL. 30, NO. 2


is a promising tool for screening obesity in children, although

improvements to this index may be needed. The proposed

dietary obesity index has applications in public health,

education, private clinical settings, and research. Dietitians

should consider all 3 aspects of dietary behavior (i.e., diet

composition, psychology of eating, and dietary-meal behav-

iors) when assessing and advising for childhood obesity.

Further research is needed to test this index prospectively and

in diverse populations.

ACKNOWLEDGMENTS

This work was supported by a Cyprus Research Promotion

Foundation research grant (AKGEN/0506/05) and was

partially supported by the ‘‘Charalambides’’ dairies and the

Cyprus Dietetic Association. The authors would like to

sincerely thank participating children and their parents.

Finally, we would like to thank the Cyprus Ministry of

Education and Culture (Department of Primary Education)

and all teachers who readily consented to carry out the study

during school hours.

The contributions of the authors were as follows: CL

designed the study, obtained the funding, performed the data

analysis, interpreted the results, and wrote the paper; DBP

supervised the design of the study and the statistical analyses,

contributed to presentation and interpretation of the results, and

critically reviewed the paper; GS contributed to data analysis

and interpretation of results and critically reviewed the paper;

and A-LM contributed to the presentation and interpretation of

results and critically reviewed the paper. All authors contrib-

uted to the final version of the manuscript.

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Received February 11, 2010; revision accepted November 16,

2010.

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A screening tool to assess obesogenic dietary habits in children

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