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Record W2761491620 · doi:10.1037/hea0000560

Health behavior changes in adolescence and young adulthood: Implications for cardiometabolic risk.

2017· article· en· W2761491620 on OpenAlexafffund
Megan E. Ames, Bonnie J. Leadbeater, Stuart MacDonald

Bibliographic record

VenueHealth Psychology · 2017
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsPsycINFOYoung adultLatent growth modelingDevelopmental psychologyPsychologyAdolescent healthGerontologyAdult developmentPhysical activityMedicineDemographyMEDLINEPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: Adolescence and young adulthood produce developmentally salient and contextual challenges for health behavior choices. The present study examines how changes in physical activity, nutrition, and sleep duration before and after high school graduation influence cardiometabolic risk (CMR) in adulthood (at ages 22-29). METHOD: Youth (N = 662; Time 1 ages 12-18; 48% male) were followed biannually across 10 years. Piecewise latent growth curve modeling was used to assess how changes in physical activity, nutrition, and sleep duration before and after high school influence CMR in young adulthood, accounting for baseline levels of each health behavior. Sex differences in associations were examined. RESULTS: Higher initial (baseline) levels of physical activity and nutrition predicted lower CMR. Increases in physical activity and nutrition before and after high school also contributed to lower CMR. When examined simultaneously, initial levels of physical activity and sleep duration (for female participants only) and increases in nutrition had independent effects on CMR. CONCLUSIONS: Prevention approaches that take into account the salient developmental and contextual differences in adolescence and young adulthood may improve efforts to prevent CMR. (PsycINFO Database Record

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 score0.124
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.068
GPT teacher head0.452
Teacher spread0.384 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

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