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Record W3175933841 · doi:10.3390/children8060524

Trend and Causes of Overweight and Obesity among Pre-School Children in Kuwait

2021· article· en· W3175933841 on OpenAlexaff
Nawal Alqaoud, Ayoub Al‐Jawaldeh, Fahima Al-Anazi, Monica Subhakaran, Radhouene Doggui

Bibliographic record

VenueChildren · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité de Sherbrooke
FundersWorld Health Organization
KeywordsOverweightObesityMedicineEnvironmental healthLogistic regressionDemographyPsychological interventionOddsOdds ratioChildhood obesityScreen timeGerontologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Identifying life risk factors of obesity early will help inform policymakers to design evidence-based interventions. The following study aims to assess the trend of overweight and obesity over four years among pre-school Kuwait children, and to examine their association with breakfast skipping (BF), sugary and sweetened beverage (SSB) consumption, and screen time. Children aged 2–5 years (n = 5304) were selected from 2016 to 2019 national surveys. Overweight and obesity were defined according to the World Health Organization references. The children’s mothers were asked about the BF of their children the day of the survey, their frequency of SSB consumption, and their weekly screen time use. Logistic regression was used to identify the risk factors associated with overweight/obesity. No significant decline (p values ≥ 0.12) was found for both overweight and obesity. Contrastingly, BF skipping, SSB consumption, and screen time declined (p < 0.0001). The BF skippers were found to have a 31% lower risk of being overweight. Daily TV watching, for 2–3 h, increases the odds of obesity by 5.6-fold. Our findings are encouraging regarding the decline in risky behaviours over time. However, more effort should be made both at the micro- and macro-level for a sustainable reduction in overweight and obesity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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 teacher head, 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

Citations13
Published2021
Admission routes1
Has abstractyes

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