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Record W3214808051 · doi:10.3390/obesities1030015

Changes in Adherence to the 24-Hour Movement Guidelines and Overweight and Obesity among Children in Northeastern Japan: A Longitudinal Study before and during the COVID-19 Pandemic

2021· article· en· W3214808051 on OpenAlexaboutno aff
Hyunshik Kim, Jiameng Ma, Junghoon Kim, Daolin Xu, Sunkyoung Lee

Bibliographic record

VenueObesities · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightPandemicObesityMedicineBody mass indexCoronavirus disease 2019 (COVID-19)AnthropometryChildhood obesityDemographyScreen timeGerontologyEnvironmental healthPediatricsInternal medicine

Abstract

fetched live from OpenAlex

There are few studies comparing adherence to Canadian 24-hour Movement Guidelines (24-h MG) before and during the COVID-19 pandemic and exploring the pandemic’s effect on childhood obesity. This survey-based 2-year study investigated changes in obesity and adherence to the 24-h MG in children before and during the COVID-19 pandemic. Data were collected at two points in time: pre-COVID-19 (May 2019; T1; n = 247) and during-COVID-19 (May 2021; T2; n = 171). Participants were healthy elementary school children aged between 6–12 years in northeastern Japan. The questionnaire comprised items on physical activity, screen time, sleep duration, adherence to the 24-h MG, and anthropometric and demographic characteristics. Among all participants, a statistically significant difference (p < 0.001) between the average body mass index at T1 (M = 16.06 kg/m2, SD = 2.08 kg/m2) and T2 (M = 18.01 kg/m2, SD = 3.21 kg/m2) was observed, where 17.8% were overweight and obese at T1 and 24% at T2, and 10.9% adhered to all 24 h MG at T1 and 4.1% at T2. To prevent obesity in children during the COVID-19 pandemic, environmental changes should be evaluated and appropriate preventive measures taken, including pro-community health programs that encourage parent-children outdoor activities.

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 score0.269
Threshold uncertainty score0.779

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 science0.0000.001
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 head0.046
GPT teacher head0.309
Teacher spread0.263 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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