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The Relationship of Actigraph Accelerometer Cut-Points for Estimating Physical Activity With Selected Health Outcomes

2012· article· en· W1985798893 on OpenAlexaff
Paul D. Loprinzi, Hyo Lee, Bradley J. Cardinal, Carlos J. Crespo, Ross E. Andersen, Ellen Smit

Bibliographic record

VenueResearch Quarterly for Exercise and Sport · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysical activityCut-pointGerontologyPhysical therapyMedicinePsychologyStatistics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the influence of child and adult cut-points on physical activity (PA) intensity, the prevalence of meeting PA guidelines, and association with selected health outcomes. Participants (6,578 adults > or = 18 years, and 3,174 children and adolescents < or = 17 years) from the National Health and Nutrition Examination Survey 2003-06 (Centers for Disease Control and Prevention, 2006) wore an accelerometer for 7 days. PA intensity was estimated with 5 child-derived and 12 adult-derived cut-points. For all, the cut-point influenced PA intensity and the prevalence of meeting PA guidelines. Similarly, cut-point selection influenced the relationship between physical activity and various health outcomes. Future research should further enhance meaningful cut-points relevant to populations with diverse health and age profiles.

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)0.008
metaresearch head score (Gemma)0.053
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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.053
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.090
GPT teacher head0.409
Teacher spread0.320 · 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

Citations75
Published2012
Admission routes1
Has abstractyes

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