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Record W4210281763 · doi:10.47062/1190.0302.05

OBESITY, EATING BEHAVIOR AND PHYSICAL ACTIVITY DURING COVID-19 LOCKDOWN: A STUDY OF INDIAN TEENAGERS

2022· article· en· W4210281763 on OpenAlexaff
Madiha Khan, Maneeza Khan, Sabiha Khan, Gausal A. Khan

Bibliographic record

VenueInternational Journal of Environment and Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsHeritage College
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Physical activityObesity2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineVirologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Lockdowns measures including closure of educational institutions due to COVID-19 may affect youth's activity patterns, psychological stress and obesity status. This is the first kind of study in India on the basis of a large number of teenage subjects from the COVID-19 Impact on Lifestyle Changes. Through an online questionnaire, 144 participants from high schools, aged 10-19 years, voluntarily reported their lifestyles and weight, basal metabolic rate (BMI) status in between 19th July to 12th August 2020 (before and after lockdown). Our data suggest that teenagers having prevalence of significant weight (49.6±15.5 to 52.1±15.2 Kgs p <0.001) and BMI (21.0± 1.2 to 22.7±.1.7 p<0.01) gain. Data further showed that this weight gain was for 71.1 % teenagers and BMI 67.2% subjects. Also, significant decreases were seen in the frequency of engaging in active physical activity, and leisure-time walking, while significant increases were observed in the average sedentary time during weekdays and weekends. Our findings would serve as important evidence for making strategies to counteract or reverse the lockdown effects on youths' 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)0.001
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 score0.005
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.392
Teacher spread0.341 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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