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Record W2143912915 · doi:10.1093/aje/kwp255

Declines in Physical Activity and Higher Systolic Blood Pressure in Adolescence

2009· article· en· W2143912915 on OpenAlexaffabout
Katerina Maximova, Jennifer O’Loughlin, Gilles Paradis, James A. Hanley, John Lynch

Bibliographic record

VenueAmerican Journal of Epidemiology · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineBlood pressureWaistAnthropometryBody mass indexDemographyCohortCohort studyPhysical activityInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

The authors examined the potential association between changes in the number of moderate-to-vigorous physical activity (MVPA) sessions per week, adiposity, and systolic blood pressure (SBP) during adolescence. SBP and anthropometric factors were assessed biannually (1999/2000, 2002, and 2004) in a cohort of 1,293 Canadian adolescents aged 12-13 years in 1999. Self-reported 7-day recall data on MVPA sessions >or=5 minutes in duration were collected every 3 months over the 5-year period. Estimates of initial level and rate of decline in number of MVPA sessions per week from individual growth models were used as predictors of SBP in linear regression models. A decline of 1 MVPA session per week with each year of age was associated with 0.29-mm Hg and 0.19-mm Hg higher SBPs in girls and boys, respectively, in early adolescence (ages 12.8-15.1 years) and 0.40-mm Hg and 0.18-mm Hg higher SBPs, respectively, in late adolescence (ages 15.2-17.0 years). The associations were not attenuated by changes in body mass index, waist circumference, or skinfold thickness in girls during late adolescence. Although weaker, associations were evident in boys during late adolescence, as well as in both girls and boys during early adolescence. These results support prevention of declines in MVPA during adolescence to prevent higher blood pressure in youth.

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)0.001
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 score0.035
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.028
GPT teacher head0.346
Teacher spread0.318 · 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

Citations44
Published2009
Admission routes2
Has abstractyes

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