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Record W4417296752 · doi:10.1093/pch/pxaf116.064

64 Child eating behaviours and zBMI during the COVID-19 pandemic in Canada: A prospective cohort study

2025· article· en· W4417296752 on OpenAlexaffabout
Joseph Jamnik, Charles Keown‐Stoneman, Laura N. Anderson, Jonathon L. Maguire, Catherine S. Birken

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of TorontoMcMaster UniversityImpact
Fundersnot available
KeywordsBody mass indexOvereatingProspective cohort studyPandemicObesityCohort studyChildhood obesity

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 pandemic was associated with increased body weight and obesity rates in children, but the underlying mechanisms driving these changes are not understood. Few studies have evaluated the impact of child eating behaviours on body mass index during the pandemic. Objectives To determine whether eating behaviours during the COVID-19 pandemic (overeating, difficulty getting into an eating routine, undereating, picky eating, and mealtime screen use) were associated with age- and sex-standardized body mass index (zBMI) in Canadian children. Design/Methods A prospective cohort study was conducted with children age 1-12y participating in the TARGet Kids! COVID-19 Study for Children and Families, a practice-based research network in Canada. Eating behaviours were assessed using biweekly questionnaires from March 2020-April 2023. Height and weight were measured at primary care office visits or by parents at home using standardized instructions. Linear mixed effects models accounting for repeated measures were used to explore the association between child eating behaviours and zBMI during the pandemic. All models were adjusted for covariates identified a priori, including child age, sex, birthweight, maternal ethnicity, maternal BMI, family income, and zBMI measurement method. Results A total of 330 children (548 observations) were included in the study, with a mean age 6.4 ± 2.9 years, and 52% female. Overeating was positively associated with higher mean zBMI (overall p=0.0005). Children who ‘often’ overate during the pandemic had higher zBMI (1.09 units; 95%CI 0.36,1.82) compared with those who ‘never’ overate (0.17 units; 95%CI -0.30,0.63; p=0.004). Difficulty getting into an eating routine was associated with higher mean zBMI (overall p=0.009), with those who ‘often’ had difficulties getting into an eating routine having higher zBMI (1.08 units; 95%CI 0.38,1.78) compared to those who ‘never’ had difficulty (0.44 units; 95% CI -0.04,0.92; p=0.008). Mealtime screen use was also associated with higher mean zBMI (p=0.04), with each additional meal consumed with a screen device per day being associated with a 0.13 unit (95% CI 0.003,0.25) higher zBMI. There were no statistically significant differences in zBMI between response categories for picky eating and undereating. Conclusion Overeating, difficulty getting into an eating routine and mealtime screen use were associated with increased zBMI in children during the COVID-19 pandemic. Such eating behaviours represent targets for interventions that aim to mitigate the adverse health impacts of future pandemics on children. Potential competing interests JJ - consultant work for Nutrigenomix Inc, a University of Toronto start-up which offers evidence-based genetic testing for personalized nutrition. Consulting work terminated July 1, 2023. This relationship is unrelated to the work submitted.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.365
Teacher spread0.340 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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