MétaCan
Menu
Back to cohort

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

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.080
Threshold uncertainty score0.436

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 studies0.0010.000
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 head0.090
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