MétaCan
Menu
Back to cohort
Record W2963468116 · doi:10.1371/journal.pone.0220009

Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometabolic biomarkers among children and youth from the United States

2019· article· en· W2963468116 on OpenAlexafffund
Valerie Carson, Mark S. Tremblay, Jean‐Philippe Chaput, Duncan McGregor, Sébastien Chastin

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsWaistMedicineBody mass indexCircumferenceBlood pressureNational Health and Nutrition Examination SurveyInternal medicineScreen timeCross-sectional studyEndocrinologyMetabolic syndromeDemographyObesityPopulationEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Compositional data analysis is one appropriate method for co-dependent data, even when data are collected for a subdivision of the 24-hour period, such as the waking day. Objectives were to use compositional analyses to examine the combined and relative associations of sedentary time (ST), light-intensity physical activity (LPA), moderate-intensity physical activity (MPA), and vigorous-intensity physical activity (VPA) with cardiometabolic biomarkers in a representative sample of children and youth. METHODS: This cross-sectional study included 2544 participants aged 6-17 years from the 2003-2006 United States National Health and Nutrition Examination Survey. ST (<100 counts per minute), LPA (100 counts per minute to <4 METs; Freedson age-specific equation), MPA (4 to <7 METs), and VPA (≥7 METs) were accelerometer-derived. Cardiometabolic biomarkers included waist circumference, body mass index (BMI) z-score, HDL-cholesterol, C-reactive protein, and blood pressure. Triglycerides, glucose, insulin, and LDL-cholesterol were measured in a fasting sub-sample of adolescents (n = 670). Compositional linear regression models were conducted. RESULTS: The composition of ST, LPA, MPA, and VPA was significantly associated with BMI z-score, log waist circumference, systolic and diastolic blood pressure, HDL-cholesterol, and log plasma glucose (variance explained: 1-29%). Relative to the other three behaviors, VPA was negatively associated with BMI z-score (γVPA = -0.206, p = 0.005) and waist circumference (γVPA = -0.03, p = 0.001). Conversely, ST was positively associated with waist circumference (γST = 0.029, p = 0.013). ST and VPA were also positively associated with diastolic blood pressure (γST = 2.700, p = 0.018; γVPA = 1.246, p = 0.038), relative to the other behaviors, whereas negative associations were observed for LPA (γLPA = -2.892, p = 0.026). Finally, VPA was positively associated with HDL-cholesterol, relative to other behaviors (γVPA = 0.058, p<0.001). CONCLUSIONS: The ST and physical activity composition appears important for many aspects of cardiometabolic health in children and youth. Compositions with more time in higher-intensity activities may be better for some aspects of cardiometabolic health.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.261
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations85
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicObesity, Physical Activity, DietFrench-language works237,207