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Record W4388980475 · doi:10.1016/j.clnesp.2023.11.010

The degree of food processing contributes to sugar intakes in families with preschool-aged children

2023· article· en· W4388980475 on OpenAlexafffund
Rahbika Ashraf, Alison M. Duncan, Gerarda Darlington, Andrea C. Buchholz, Jess Haines, David W.L.

Bibliographic record

VenueClinical Nutrition ESPEN · 2023
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineSugarDegree (music)Environmental healthFood scienceAdded sugar

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Evidence implicates ultra-processed food intake as a major contributor of excess dietary sugars. However, little research exists on the relationship between the degree of food processing and sugar intake in families with young children. We investigated associations between the degree of food processing and sugar intake (total and free sugars) in Canadian preschool-aged children and parents. METHODS: This cross-sectional study of 242 families included preschool-aged children (n = 267) and parents (n = 365) participating in the Guelph Family Health Study. Dietary intake was assessed via the web-based Automated Self-Administered 24-h Dietary Assessment Tool (ASA24-Canada-2016) and classified according to the NOVA Food Classification System including, unprocessed or minimally processed foods, processed culinary ingredients, processed foods and ultra-processed foods. Linear regression models with generalized estimating equations were used to examine associations between the energy contribution of each NOVA classification category and sugar intake (% kcal of total and free sugars). Pearson correlation coefficient estimates were used to assess dietary relationships between parents and children. RESULTS: Ultra-processed foods were the greatest source of energy (44.3%) and energy from total (8.7%) and free sugars (7.3%) in the parents' diets, and the greatest source of energy (41.3%) and energy from free sugars (7.6%) in the children's diet. Ultra-processed food intake was positively associated with sugar intake in parents (total sugars: B = 0.05, 95% CI: 0.02-0.09, p = 0.01; free sugars: B = 0.11, 95% CI: 0.08-0.15, p < 0.001) and children (total sugars: B = 0.10, 95% CI: 0.04-0.16, p = 0.001; free sugars: B = 0.16, 95% CI: 0.12-0.21, p < 0.001). Unprocessed or minimally processed food intake was negatively associated with free sugar intake in parents (B = -0.08, 95% CI: -0.12 to -0.05, p < 0.001) and children (B = -0.15, 95% CI: -0.19 to -0.10, p < 0.001). Weak correlations were found between parents and children for processed culinary ingredients and ultra-processed processed food intake (p < 0.001). CONCLUSIONS: This study highlights the associations between degree of food processing and sugar intake in parents and children, whereby ultra-processed foods were positively, and unprocessed or minimally processed foods were negatively, associated with sugar intake. These are important considerations in the development of policy and recommendations for foods to potentially promote or limit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.354
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractno

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