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Record W4302144763 · doi:10.17615/wvtz-hq87

Lifestyle habits and obesity progression in overweight and obese American young adults: Lessons for promoting cardiometabolic health: Lifestyle habits and obesity progression

2020· article· en· W4302144763 on OpenAlexaboutno aff
Margeaux K. Akazawa, EunSeok Cha, Hannah M. Lerner, Colleen R. Dawkins, Sandra B. Dunbar, Guillermo E. Umpierrez, Kevin H. Kim

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsObesityOverweightMedicineGerontologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Obesity among young adults is a growing problem in the United States and is related to unhealthy lifestyle habits such as high caloric intake and inadequate exercise. Accurate assessment of lifestyle habits across obesity stages is important for informing age-specific intervention strategies to prevent and reduce obesity progression. Using a modified version of the Edmonton Obesity Staging System (mEOSS), a new scale for defining obesity risk and predicting obesity morbidity and mortality, this cross sectional study assessed prevalence of mEOSS in 105 overweight/obese young adults and compared young adults’ lifestyle habits across the mEOSS stages. Descriptive statistics, chi-square tests, and one-way analyses of variance (ANOVA) were performed. About 80% of participants (n=83) fell into the mEOSS-2 group and had obesity-related chronic disorders such as diabetes, hypertension and/or dyslipidemia. There were significant differences in dietary quality and dietary patterns across the mEOSS stages. Findings highlighted the significance of obesity prevention and early treatment for overweight and obese young adults to prevent/stop obesity progression.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.368
Teacher spread0.332 · 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.

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
Published2020
Admission routes1
Has abstractyes

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