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High-quality, Web-based dietary assessment tools for children are needed to reduce cost and improve user-friendliness when studying children’s dietary practices.
To evaluate the first Web-based dietary assessment tool for children in Norway, the Web-based Food Record (WebFR), by comparing children’s true school lunch intake with recordings in the WebFR, using direct unobtrusive observation as the reference method.
A total of 117 children, 8-9 years, from Bærum, Norway, were recruited from September to December 2013. Children completed 4 days of recordings in the WebFR, with parental assistance, and were observed during school lunch in the same period by 3 observers. Interobserver reliability assessments were satisfactory. Match, omission, and intrusion rates were calculated to assess the quality of the recordings in the WebFR for different food categories, and for all foods combined. Logistic regression analyses were used to investigate whether body mass index (BMI), parental educational level, parental ethnicity or family structure were associated with having a “low match rate” (≤70%).
Bread and milk were recorded with less bias than spreads, fruits, and vegetables. Mean (SD) for match, omission, and intrusion rates for all foods combined were 73% (27%), 27% (27%), and 19% (26%), respectively. Match rates were statistically significantly associated with parental educational level (low education 52% [32%] versus high 77% [24%],
Compared with other similar studies, our results indicate that the WebFR is in line with, or better than most of other similar tools, yet enhancements could further improve the WebFR.
High-quality dietary assessment tools are essential when studying children’s dietary practices. Traditional tools, such as food frequency questionnaires, 24-hour recalls, and food records, can be used to assess dietary intake [
In comparison with paper-based dietary assessment tools, Web-based tools facilitate data handling and improve user-friendliness; they reduce the burden for both the participant and researcher and can enhance motivation [
It is well established that assessment of dietary intake is associated with errors [
Direct observation is considered to be an appropriate high-quality method for validation studies of dietary assessment tools, because it provides direct unbiased information regarding what is eaten [
All the 4th graders (8-9 years old) from 4 elementary schools in Bærum, the fifth most populated municipality in Norway and a suburb of the capital city, were invited through the schools from September to December 2013. Convenience sampling was used; selected schools were in a short travel distance for the observers and had a highly cooperative school administration. Verbal and written information was given at plenary school meetings and in school classes to parents/guardians and children, respectively. To be included in the study, children needed an Internet access at their home, and their parents/guardians needed access to email. The final sample consisted of 117 of the 196 invited children (59.7%). The study was conducted in accordance with the Declaration of Helsinki. The Regional Ethical Committee in the South East of Norway found the study to fall outside their remit. Approval from the Norwegian Social Science Data Services was obtained, in addition to child assent and written parental consent from all participants.
The participants were instructed to record everything they ate and drank in the WebFR, for 4 consecutive days, including a weekend day. They were instructed to complete the recordings in the WebFR at home, with parental assistance, at the end of each recording day, after all meals were consumed. A practical demonstration was given at school in addition to written instructions on how to use the WebFR. During the days they recorded their diet, each child was observed once during school lunch. The children's weights and heights were also measured using standard procedures. After completing the study, the participants received a personal gift card with 2 cinema tickets.
The WebFR is designed as a food record, yet including elements of a dietary recall, as recordings are completed by the end of each recording day. It is structured by meals with photos for portion-size assessments. It was adapted from the WebDASC by replacing its food lists with approximately 550 of the most commonly eaten foods and beverages in Norway, based on data from the latest Norwegian National Dietary Survey [
Screenshot from the Web-based Food Record (WebFR), showing an example of one of the photo series illustrating different portion sizes.
The observer team included 1 registered dietician and 2 master’s students in nutrition. The observations were performed in classrooms in which the children ate their home-packed lunches during regular school days. Each child was observed one time, during the same period as when they were instructed to record data in the WebFR. Each observer monitored a maximum of 3 children at the same time in an unobtrusive manner (ie, avoiding interaction with the participants and blinding the observations for participants). The children were already familiar with the presence of the observers prior to the observations, through instructional sessions.
The observers used a standardized form to take notes during their observations. To ensure complete recordings, observers were present in the classroom from before the children started eating to until they all had stopped eating. Immediately after each observation session, the observers categorized all observed food items into categories and portion sizes that corresponded to the information in the WebFR, with the aid of tablets containing the lists of categories, items, and all photos found in the WebFR. When the observed foods were not found in the WebFR, the observers described the food item in detail in text and chose the food category and portion size they considered most appropriate for the specific food item. After completion of the data collection, the observer team determined what constituted matches, omissions, and intrusions, using a strict definition; that is, a match was considered a match only when the child and observer clearly described the same item.
Observer training prior to data collection was conducted over a period of 3 weeks, based on the training protocol by Richter et al [
Variables for “matches,” “omissions,” and “intrusions” were created by comparing the observational data with the participants’ school lunch recordings in the WebFR. Matches are items that are both observed as eaten and recorded as eaten by the child; omissions are items that are observed as eaten but not recorded as eaten; and intrusions are items not observed as eaten, but recorded as eaten by the child.
Participants’ height and weight were measured according to standard procedures, without shoes and in light clothing, to the nearest millimeter and 0.1 kg, respectively, by trained personnel. A digital scale was used (TANITA TBF-300, Tanita Corporation, Tokyo, Japan), in the privacy of a separate room, for each participant. Age and sex-specific body mass index (ISO-BMI) cutoffs defining overweight and obesity among the study participants were applied [
Parents/guardians provided information in the written consent form regarding each participant’s sex and age, parental education level (low, intermediate, or high), parental ethnicity (at least one versus no parents/guardians of Norwegian origin), and family structure (mother and father of participant living in same household versus other).
MS Excel (version 2010, Microsoft, Redmond, WA, USA) was used to create all the variables. IBM SPSS (version 21.0, 2012, IBM Corp, New York, NY, USA) was used in all analyses, with the exception of the bias-reduced logistic regression analysis, for which the statistical package R (version 3.0.1, 2013, The R Foundation for Statistical Computing, Vienna, Austria) was used.
Descriptive statistics for the observed food items, recorded food items, matches, omissions, and intrusions were performed. The rates of matches, omissions, and intrusions were calculated for each participant both for all food items combined and at the food item category level (eg, “fruit, berries,” “bread products”). Definitions of these variables are in accordance with previous definitions developed by Baxter et al [
Univariate analyses were conducted to find possible differences in the match rates, omission rates, and intrusion rates as continuous variables, for all foods combined, with regard to the following variables: sex, BMI category, parental educational level, parental ethnicity, and family structure. Parametric tests were used when appropriate. Because the omission rate is the inverse of the match rate (match rate=100 - omission rate), testing for the match rate was therefore equivalent to testing for the omission rate.
A log transformation of the match rate variable was conducted; nevertheless, the assumptions for doing a multivariate linear regression were not present. Hence, match rates were further recoded to a dichotomous variable, which was defined as either a “low match rate” (≤70%) or “high match rate” (>70%). Logistic regression analyses were used to investigate the association between participant characteristics and the quality of the recordings in the WebFR (ie, low versus high match rate). Because of low cell counts, Logistf (bias-reduced logistic regression, Firth correction) [
The characteristics of the study sample are shown in
Characteristics of participants (N=117) in a validation study of a Web-based Food Record in Norway.
Characteristics |
|
n | % |
|
|
|
|
|
8 | 13 | 11.1 |
|
9 | 104 | 88.9 |
|
|
|
|
|
Girls | 64 | 54.7 |
|
Boys | 53 | 45.3 |
|
|
|
|
|
Normal weight | 102 | 87.2 |
|
Overweight or obese | 15 | 12.8 |
|
|
|
|
|
Lowb | 12 | 10.8 |
|
Intermediatec | 22 | 19.8 |
|
Highd | 77 | 69.4 |
|
|
|
|
|
At least one parent/guardian of Norwegian origin | 105 | 91.3 |
|
Both parents/guardians of ethnic origin other than Norwegian | 10 | 8.7 |
|
|
|
|
|
Mother and father of participant living in same household | 87 | 78.4 |
|
Other | 24 | 21.6 |
aInformation from 111 participants was available for “parental education level.” Complete information on both parents/guardians was available from 108 participants; the 3 cases with missing information from 1 parent/guardian were included in the table based on the 1 available parent/guardian's educational level.
bBoth parents/guardians' education was maximum high-school level.
cOne parent/guardian's education was maximum high-school level, and the second parent/guardian's education was at university-college or university level.
dBoth parents/guardians' education was at the university college or university level.
eInformation from 115 participants was available for “parental ethnicity.”
fInformation from 111 participants was available for “family structure.”
Omission ratea and intrusion rateb within different food categories, listed in descending order from the most to the least frequently observed, for all 8- and 9-year old children (N=117) in a validation study of a Web-based Food Record in Norway.
|
Omission rate |
Intrusion rate |
Coinciding omissions and |
|||
|
Nd | Mean (SD) | Nd | Mean (SD) | Ne | n (%) |
All food items | 117 | 27 (27) | 117 | 19 (26) | 136 | 18 (13.2) |
Spreads | 93 | 29 (43) | 79 | 17 (33) | 41 | 7 (17.1) |
Bread products | 95 | 5 (22) | 97 | 7 (26) | 5 | 3 (60.0) |
Fruit, berries | 42 | 39 (48) | 36 | 25 (44) | 22 | 1 (4.5) |
Vegetables, salads | 33 | 45 (49) | 23 | 21 (39) | 23 | 0 (0.0) |
Milk | 49 | 6 (24) | 52 | 12 (32) | 3 | 1 (33.3) |
Beverages, otherf | 44 | 18 (39) | 62 | 42 (50) | 8 | 2 (25.0) |
Dinner leftovers | 17 | 33 (43) | 14 | 7 (27) | 7 | 0 (0.0) |
Miscellaneous | 17 | 44 (50) | 12 | 21 (40) | 8 | 1 (12.5) |
Biscuits, buns, waffles, cakes, and candy | 12 | 85 (31) | 4 | 38 (48) | 12 | 1 (8.3) |
Yogurt | 11 | 64 (50) | 9 | 56 (53) | 7 | 2 (28.6) |
aOmission rate = omissions/observed eaten food items × 100 = omissions/(omissions + matches) × 100. Omission rates were calculated for each participant within the different food categories. Participants who were not observed eating foods within a certain category (eg, “fruit, berries”) were excluded from the analyses for this category, regardless of what was recorded eaten.
bIntrusion rate = intrusions/recorded eaten food items × 100 = intrusions/(intrusions + matches) × 100. Intrusion rates were calculated for each participant within the different food categories. Participants who did not record eating foods within a certain category (eg, “fruit, berries”) were excluded from the analyses for this category, regardless of what was observed eaten.
cCases where a participant had an omission that corresponds to an intrusion, within the same food category and within the same meal. For example, “apple” omitted and “pear” intruded during the same school lunch. Formula used: coinciding omissions and intrusions/omissions × 100.
dNumber of participants included in analyses.
eNumber of food items included in analyses.
fOf all intruded “beverages, other” 96% are drinking water.
In addition,
Omissions and intrusions of large portion sizes are considered to be more severe than the omission or intrusion of small portion sizes. In
Proportion of different sizes of omitteda and intrudedb food items during school lunch for all 8- and 9-year-old participants (N=117) in a validation study of a Web-based Food Record in Norway.
Items | Nd | Proportion of different sizesc of omitted food items, n (%) | Nf | Proportion of different sizesc of intruded food items, n (%) | ||||||||
|
|
XS | S | M | L | Missinge |
|
XS | S | M | L | Missinge |
All food items | 136 | 28 (20.6) | 29 (21.3) | 21 (15.4) | 22 (16.2) | 36 (26.5) | 91 | 9 (9.9) | 24 (26.4) | 30 (33.3) | 28 (30.8) | — |
Spreads | 41 | 7 (17.1) | 7 (17.1) | 12 (29.3) | 3 (7.3) | 12 (29.3) | 22 | 2 (9.1) | 10 (45.5) | 6 (27.3) | 4 (18.2) | — |
Bread products | 5 | — | 1 (20.0) | 1 (20.0) | 3 (60.0) | 0 (0.0) | 7 | — | 0 (0.0) | 5 (71.4) | 2 (28.6) | — |
Fruit, berries | 22 | 10 (45.5) | 5 (22.7) | 0 (0.0) | 3 (13.6) | 4 (18.2) | 12 | 2 (16.7) | 2 (16.7) | 3 (25.0) | 5 (41.7) | — |
Vegetables, salads | 23 | 5 (21.7) | 9 (39.1) | 5 (21.7) | 1 (4.3) | 3 (13.0) | 7 | 1 (14.3) | 4 (57.1) | 1 (14.3) | 1 (14.3) | — |
Milk | 3 | 0 (0.0) | 0 (0.0) | 0 (0.0) | 2 (66.7) | 1 (33.3) | 6 | 0 (0.0) | 0 (0.0) | 2 (33.3) | 4 (66.7) | — |
Beverages, other | 8 | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 8 (100.0) | 26 | 2 (7.7) | 8 (30.8) | 9 (34.6) | 7 (26.9) | — |
Dinner leftovers | 7 | 1 (14.3) | 2 (28.6) | 2 (28.6) | 1 (14. 3) | 1 (14.3) | 1 | 1 (100) | 0 (0.0) | 0 (0.0) | 0 (0.0) | — |
Miscellaneous | 8 | 1 (12.5) | 0 (0.0) | 1 (12.5) | 2 (25.0) | 4 (50.0) | 3 | 0 (0.0) | 0 (0.0) | 3 (100.0) | 0 (0.0) | — |
Biscuits, buns, waffles, cakes, and candy | 12 | 4 (33.3) | 5 (41.7) | 0 (0.0) | 2 (16.7) | 1 (8.3) | 2 | 1 (50.0) | 0 (0.0) | 1 (50.0) | 0 (0.0) | — |
Yogurt | 7 | — | 0 (0.0) | 0 (0.0) | 5 (71.4) | 2 (28.6) | 5 | — | 0 (0.0) | 0 (0.0) | 5 (100.0) | — |
aItems observed eaten, but not recorded.
bItems recorded, but not observed eaten.
cPortion sizes were divided into the following categories: XS=extra small, S=small, M=medium, L=large, based on the photo series available for each food item.
dNumber of omitted food items included in analyses.
ePortion size not possible to observe with certainty, that is, when participants drank from dark-colored drinking bottles or milk cartons, or when participants ate a sandwich where spreads were partially hidden because it was placed in between 2 slices of bread.
fNumber of intruded food items included in analyses.
The very few omissions in the “bread products” and “milk” categories were mostly of large portion sizes, whereas the omitted portion sizes from “spreads” were mostly of medium sizes. By contrast, the majority of omitted items in the categories “fruit, berries” and “vegetables, salads” were of small portion sizes.
Along the same lines as the omissions, the few intrusions in the categories “bread products” and “milk” were all of medium or large sizes. In the categories “fruit, berries,” “vegetables, salads,” and “spreads,” intrusions occurred for all portion sizes.
Mean rates within subgroups are presented in
Match rate,a omission rate,b and intrusion ratec within different subgroups among the 8- and 9-year-old participants (N=117) observed during school lunch in a validation study of a Web-based Food Record in Norway.
Variables |
|
Total (N) | Match rate |
Omission rate |
Intrusion rate |
|||
|
|
|
Mean (SD) |
|
Mean (SD) |
|
Mean (SD) |
|
Total participants (N) |
|
117 | 73 (27) | 27 (27) | 19 (26) | |||
|
|
.59 | .59 | .28 | ||||
|
Girls | 64 | 71 (30) | 29 (30) | 22 (29) | |||
|
Boys | 53 | 76 (22) | 24 (22) | 16 (23) | |||
|
|
.44 | .44 | .80 | ||||
|
Normal weight | 102 | 74 (27) | 26 (27) | 19 (26) | |||
|
Overweight or obese | 15 | 69 (27) | 31 (27) | 21 (28) | |||
|
|
.008 | .008 | .006 | ||||
|
Lowf | 12 | 52 (32) | 48 (32) | 40 (38) | |||
|
Intermediateg | 22 | 69 (31) | 31 (31) | 24 (32) | |||
|
Highh | 77 | 77 (24) | 23 (24) | 15 (21) | |||
|
|
.04 | .04 | .49 | ||||
|
At least one parent/guardian of Norwegian origin | 105 | 75 (26) | 25 (26) | 19 (26) | |||
|
Both parents/guardians of other ethnic |
10 | 57 (28) | 44 (28) | 24 (27) | |||
|
|
.08 | .08 | .86 | ||||
|
Mother and father of participant living in same household | 87 | 75 (27) | 25 (27) | 20 (26) | |||
|
Other | 24 | 64 (29) | 36 (29) | 21 (31) |
aMatch rate = matches/observed eaten food items × 100 = matches/(omissions + matches) × 100. Match rates were calculated for each participant, for all food items combined.
bOmission rate = omissions/observed eaten food items × 100 = omissions/(omissions+ matches) × 100. Omission rates were calculated for each participant, for all food items combined.
cIntrusion rate = intrusions/recorded eaten food items × 100 = intrusions/(intrusions+ matches) × 100. Intrusion rates were calculated for each participant, for all food items combined.
d
eInformation from 111 participants was available for “parental education level.” Complete information on both parents/guardians was available from 108 participants; the 3 cases with missing information from 1 parent/guardian were included in the table based on the 1 available parent/guardian's educational level.
fBoth parents/guardians' education was maximum high-school level.
gOne parent/guardian's education was maximum high-school level, and the second parent/guardian's education was at the university college or university level.
hBoth parents/guardians' education was at the university college or university level.
iInformation from 115 participants was available for “parental ethnicity.”
jInformation from 111 participants was available for “family structure.”
For intrusion rates, the differences between groups were not statistically significant, except for parental education wherein higher intrusion rates were associated with lower parental educational levels (
The logistic regression model in
Variables associated with having a low match rate (≤70%) among 8- and 9-year-old children recording in a Web-based Food Record compared with unobtrusive school lunch observation in Norway.
Variables |
|
n (%) of children | Odds ratio (95% CI) | ||
|
Overall |
With low match rate (≤70%) |
Unadjusted |
Adjusteda
|
|
|
|
||||
|
Normal weight | 96 (86.5) | 36 (81.8) | 1 | 1 |
|
Overweight or obese | 15 (13.5) | 8 (18.2) | 1.9 (0.6-5.7) | 1.6 (0.4-5.4) |
|
|
|
|||
|
Norwegian origin | 101 (91.0) | 36 (81.8) | 1 | 1 |
|
Non-Norwegians | 10 (9.0) | 8 (18.2) | 7.2 (1.5-35.9) | 6.9 (1.3-36.4) |
|
|
||||
|
High | 77 (69.4) | 25 (56.8) | 1 | 1 |
|
Intermediate | 22 (19.8) | 10 (22.7) | 1.7 (0.7-4.6) | 1.6 (0.6-4.5) |
|
Low | 12 (10.8) | 9 (20.5) | 6.2 (1.6-25.1) | 3.8 (0.9-17.2) |
|
|
||||
|
Mother and father of participant living in same household | 87 (78.4) | 31 (70.5) | 1 | 1 |
|
Other | 24 (21.6) | 13 (29.5) | 2.1 (0.9-5.3) | 2.0 (0.7-5.3) |
aAdjusted for all other variables in the model in a logistic regression analyses.
bISO-BMI cutoffs applied.
cBoth parents/guardians of ethnic origin other than Norwegian, compared with at least one parent/guardian of Norwegian origin (reference).
dFamily structure defined as everything else but “mother and father of participant living in same household” (ie, other) compared with “mother and father of participant living in same household” (reference).
We found that 8-9-year-old children on average had a match rate of 73%, an omission rate of 27%, and an intrusion rate of 19%, when comparing parental-assisted entries of school lunch data in a WebFR with unobtrusive observations. Mean omission and intrusion rates for different food categories varied greatly. Lower parental educational levels and a non-Norwegian background were associated with less accurate recordings, but this must be interpreted with caution because of the low numbers in these subgroups.
Only a few other validation studies of Web-based 24-hour recalls/records for children have used observation during school meals as a reference method. Among these studies are the one on the Automated Self-Administered 24-hour Recall-Kids-2012 (ASA24-Kids-2012) among 9-11-year olds by Diep et al [
Our results are not directly comparable with these validation studies, partly because the rates of matches, omissions, and intrusions were not calculated in the same way as they were in our study. Nonetheless, we assert that it is possible to interpret the direction of the findings; in the CAAFE and ASA24-Kids-2012 studies, lower agreement between the recordings in the Web-based assessment tool and observations of school lunch were reported than in our study. The CAAFE study had average rates of 44% matches, 30% omissions, and 26% intrusions [
The PAC24 study shows results that are more in line with our results, despite the lack of parental/adult assistance during recording [
Because the WebFR is a Norwegian version of the Danish WebDASC, we expected the results to be consistent with the findings from the WebDASC validation study [
A very high reporting accuracy was reported in the small validation study (n=25) of SACINA by Hunsberger and co-workers; in their study, overall food matches ranged from 86% to 98% [
Baxter et al [
Only a few studies report the rates of omissions and intrusions for selected food subcategories that are comparable with our findings. Vegetables and sweets were reported as the most often omitted food items in the PAC24 study, whereas beverages were the most commonly intruded item [
To our knowledge, we are the first to report on “coinciding omissions and intrusions,” and by doing so we add important knowledge as to whether the omissions and intrusions represent major errors, and not just slightly imprecise recordings. The food category “spreads” had a high omission rate, and most of the omissions were major errors, not “coinciding omissions and intrusions.” This discovery has already led us to improve the WebFR, by including tailor-made prompts for “spreads.”
Taking the portion sizes of the omitted and intruded food items into account is important because it provides a better understanding of whether these omissions and intrusions are of great concern or not. We observed high omission rates in the food categories “fruit, berries” and “vegetables, salads”; however, the portion sizes of these categories were mostly small in contrast to the portion sizes of omitted “spreads.” Thus, we argue that the omissions of spreads are more troublesome than the omissions of fruits and vegetables in our WebFR.
Lower parental education levels have been associated with a higher degree of misreporting among children in the form of underreporting, or both underreporting and overreporting [
Studies indicate that underreporting among children increases as BMI increases [
The use of direct unobtrusive observations is one of the strengths of this study, because these provide exact information about what is consumed, without affecting the recordings [
The small number of individuals in some of the subgroups is a limitation of this study, as the preferable adjustment for cluster effects (school level) proved infeasible due to lack of established statistical methods. Hence, the point estimates in the logistic regression analysis should be interpreted with caution.
For practical reasons, observations were restricted to school lunches and to children in 4th grade (8-9 years). Thus, a limitation is that we do not know whether our findings can be extrapolated to other meals or age groups. In addition, our participants had more highly educated parents/guardians and were less overweight or obese, than the average Norwegian population in which 29% have higher education [
We have demonstrated that 8-9-year-old children had a mean match rate of 73% when recording their food intake from school lunch, with parental assistance, in a WebFR. Some children had difficulties recording, but the mean results were better than what have been reported in most validation studies of other Web-based dietary assessment tools among children. The WebFR could be improved further by including additional prompts for high omission rate foods. We suggest that children and their parents/guardians with language difficulties should be given extra support and information about how to use the WebFR in future studies.
Selected screenshots from the Web-based Food Record (WebFR).
Automated Self-Administered 24-hour Recall-Kids-2012
body mass index
the Food Intake and Physical Activity of School Children
interobserver reliability
age and sex-specific body mass index
the Portuguese Self-Administered Computerised 24-h Dietary Recall
Self-Administered Children and Infant Nutrition Assessment
Web-based Dietary Assessment Software for Children
Web-based Food Record
The authors thank Susanne Strohmaier for assistance with the statistical analyses.
None declared.