MétaCan
Menu
Back to cohort
Record W2071238509 · doi:10.1038/oby.2009.382

The Utility of Physical Activity in the Management of Global Cardiometabolic Risk

2009· review· en· W2071238509 on OpenAlexaff
Peter M. Janiszewski, Robert Ross

Bibliographic record

VenueObesity · 2009
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsQueen's University
Fundersnot available
KeywordsCardiorespiratory fitnessMedicineDyslipidemiaType 2 diabetesAerobic exerciseObesityPhysical fitnessDiseaseWeight lossPopulationPhysical therapyPhysical activityDiabetes mellitusSedentary lifestyleInternal medicineEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

Major health organizations promote the adoption of a healthy lifestyle, composed of sufficient daily physical activity and a balanced diet for the prevention and management of type 2 diabetes (T2D) and cardiovascular disease risk. In particular, it is recommended that adults accumulate 30 min of moderate-intensity aerobic physical activity on most days of the week. Despite these recommendations, a physically active lifestyle is seldom adopted, and the majority of the North American population remains sedentary. Although the optimal strategy for promoting physical activity in today's environment remains elusive, the evidence for the utility of physical activity in the management of risk factors for T2D and cardiovascular disease is overwhelming. This review examines the influence of aerobic-type physical activity on components of global cardiometabolic risk, that is, the traditional and emerging risk factors for cardiovascular disease and T2D, including visceral obesity, insulin resistance, hypertension, atherogenic dyslipidemia, thrombosis, inflammation, and cardiorespiratory fitness. Where possible, specific consideration is given to the independent effects of an acute bout of physical activity vs. chronic physical activity with weight loss vs. chronic physical activity without weight loss.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.061
GPT teacher head0.395
Teacher spread0.333 · 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 designOther design
Domainnot available
GenreReview

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

Citations70
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueObesitySame topicPhysical Activity and HealthFrench-language works237,207