Technological tools for assessing children's food intake: a scoping review.

Technological innovations can standardise and minimise reporting errors in dietary assessment. This scoping review aimed to summarise the characteristics of technological tools used to assess children's food intake. The review followed the Joanna Briggs Institute's manual. The main inclusion criterion was studied that assessed the dietary intake of children 0-9 years of age using technology. We also considered articles on validation and calibration of technologies. We retrieved 15 119 studies and 279 articles were read in full, after which we selected 93 works that met the eligibility criteria. Forty-six technologies were identified, 37 % of which had been developed in Europe and 32⋅6 % in North America; 65⋅2 % were self-administered; 27 % were used exclusively at home; 37 % involved web-based software and more than 80 % were in children over 6 years of age. 24HR was the most widely used traditional method in the technologies (56⋅5 %), and 47⋅8 % of the tools were validated. The review summarised helpful information for studies on using existing tools or that intend to develop or validate tools with various innovations. It focused on places with a shortage of such technologies.

The Alcohol Use Disorders Identification Test (AUDIT) in Adolescents: Using a Model-Based Approach to Identify Patterns of Alcohol Misuse.

OBJECTIVE: To explore the latent structure of the Alcohol Use Disorders Identification Test (AUDIT) among adolescents of different school grades (age strata). Methods: Data derived from two simultaneous run cohort studies from the "Adolescent Nutritional Assessment Longitudinal Study-ELANA" conducted in private and public schools of Rio de Janeiro/Brazil. Participants comprised 564 seventh-graders and 419 ninth-graders, respectively sampled in 2011 and 2013 from cohort 1, and 730 eleventh-graders sampled in 2011 from cohort 2. Latent class factor analytical (LCFA) models were applied to the AUDIT items to identify internally homogeneous latent groups of individuals representing distinct patterns of alcohol use, and optimal group-separating cutoffs. The classification agreement was also evaluated. Results: Three and two groups (classes) were found for the eleventh and the earlier grades, respectively. These best-fitting models held a very high degree of class separation (entropy >0.9). By contrasting the AUDIT's raw scores (0-10) with the model-based latent classes, the cutoff separating the base (milder) category was found between scores 0 and 1 in all grades. The eleventh-graders differed from the others by showing a third and more intense category of alcohol use (cutoff between 4 and 5). The classification agreement was almost perfect in eleventh-graders (98.6%) and perfect in the earlier grades (100%). Conclusions: Findings show that the AUDIT may be adequately used in adolescents of different ages and school grades, although the number of homogeneous categories may differ accordingly. Besides, raw scores may be pragmatically used to identify groups with confidence by applying specific optimal cutoffs.

Suitability of the eight-item version of the Brazilian Household Food Insecurity Measurement Scale to identify risk groups: evidence from a nationwide representative sample.

OBJECTIVE: The Brazilian Household Food Insecurity Measurement Scale (EBIA) has eight general/adult items applied in all households and six additional items exclusively asked in households with children and/or adolescents (HHCA). Continuing an investigation programme on the adequacy of model-based cut-off points for EBIA, the present study aims to: (i) explore the capacity of properly stratifying HHCA according to food insecurity (FI) severity level by applying only the eight 'generic' items; and (ii) compare it against the fourteen-item scale. DESIGN: Latent class factor analysis (LCFA) models were applied to the answers to the eight general/adult items to identify latent groups corresponding to FI levels and optimal group-separating cut-off points. Analyses involved a thorough classification agreement evaluation and were performed at the national level and by macro-regions. SETTING: Data derived from the cross-sectional Brazilian National Household Sample Survey of 2013. PARTICIPANTS: A nationally representative sample of 116 543 households. RESULTS: In all households and investigated domains, LCFA detected four distinct household food (in)security groups (food security and three levels of severity of FI) and the same set of cut-off points (1/2, 4/5 and 6/7). Misclassification in the aggregate data was 0·66 % in adult-only households and 1·06 % in HHCA. Comparison of the scale reduced to eight items with the 'original' fourteen-item scale demonstrated consistency in the classification. In HHCA, the agreement between both classifications was 96·2 %. CONCLUSIONS: Results indicate the eight 'generic' items in HHCA can be reliably used when it is not possible to apply the fourteen-item scale.

Modeling Optimal Cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a Nationwide Representative Sample.

Background: This is the second part of a model-based approach to examine the suitability of the current cutoffs applied to the raw score of the Brazilian Household Food Insecurity Measurement Scale [Escala Brasileira de Insegurança Alimentar (EBIA)]. The approach allows identification of homogeneous groups who correspond to severity levels of food insecurity (FI) and, by extension, discriminant cutoffs able to accurately distinguish these groups.Objective: This study aims to examine whether the model-based approach for identifying optimal cutoffs first implemented in a local sample is replicated in a countrywide representative sample.Methods: Data were derived from the Brazilian National Household Sample Survey of 2013 (n = 116,543 households). Latent class factor analysis (LCFA) models from 2 to 5 classes were applied to the scale's items to identify the number of underlying FI latent classes. Next, identification of optimal cutoffs on the overall raw score was ascertained from these identified classes. Analyses were conducted in the aggregate data and by macroregions. Finally, model-based classifications (latent classes and groupings identified thereafter) were contrasted to the traditionally used classification.Results: LCFA identified 4 homogeneous groups with a very high degree of class separation (entropy = 0.934-0.975). The following cutoffs were identified in the aggregate data: between 1 and 2 (1/2), 5 and 6 (5/6), and 10 and 11 (10/11) in households with children and/or adolescents <18 y of age (score range: 0-14), and 1/2, between 4 and 5 (4/5), and between 6 and 7 (6/7) in adult-only households (range: 0-8). With minor variations, the same cutoffs were also identified in the macroregions. Although our findings confirm, in general, the classification currently used, the limit of 1/2 (compared with 0/1) for separating the milder from the baseline category emerged consistently in all analyses.Conclusions: Nationwide findings corroborate previous local evidence that households with an overall score of 1 are more akin to those scoring negative on all items. These results may contribute to guide experts' and policymakers' decisions on the most appropriate EBIA cutoffs.

A Model-Based Approach to Identify Classes and Respective Cutoffs of the Brazilian Household Food Insecurity Measurement Scale.

BACKGROUND: The Brazilian Household Food Insecurity Measurement Scale (EBIA) is the main tool for assessing household food insecurity (FI) in Brazil, assisting in monitoring and improving national public policies to promote food security. Based on the sum of item scores, households have been classified into 4 levels of FI, with the use of cutoffs arising from expert discussions informed by psychometric analyses and policy considerations. OBJECTIVES: This study aimed to identify homogeneous latent groups corresponding to levels of FI, examine whether such subgroups could be defined from discriminant cutoffs applied to the overall EBIA raw score, and compare these cutoffs against those currently used. METHODS: A cross-sectional population-based study with a representative sample of 1105 households from a low-income metropolitan area of Rio de Janeiro was conducted. Latent class factor analysis (LCFA) models were applied to the answers to EBIA's items to identify homogeneous groups, obtaining the number of latent classes for FI measured by the scale. Based on this and a thorough classification agreement evaluation, optimal cutoffs for discriminating between different severity levels of FI were ascertained. Model-based grouping and the official EBIA classification cutoffs were also contrasted. RESULTS: LCFA identified 4 homogeneous groups with a very high degree of class separation (entropy = 0.906), endorsing the classification of EBIA as a 4-level measure of FI. Two sets of cutoffs were identified to separate such groups according to household type: 1/2, 5/6, and 10/11 in households with children and adolescents (score range: 0-14); and 1/2, 3/4, and 5/6 in adult-only households (score range: 0-7). CONCLUSION: Although roughly classifying EBIA as in previous studies, the current approach suggests that, in terms of raw score, households endorsing only one item of the scale would be better classified by being placed in the same stratum as those remaining negative on all items.