64 Child eating behaviours and zBMI during the COVID-19 pandemic in Canada: A prospective cohort study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".