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Record W2947102776 · doi:10.1123/japa.2018-0361

Replacing Sedentary Time With Light or Moderate–Vigorous Physical Activity Across Levels of Frailty

2019· article· en· W2947102776 on OpenAlexaff
Judith Godin, Joanna M. Blodgett, Kenneth Rockwood, Olga Theou

Bibliographic record

VenueJournal of Aging and Physical Activity · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsNova Scotia Health Authority
Fundersnot available
KeywordsBody mass indexMedicineMarital statusPhysical activityGerontologySedentary behaviorSedentary lifestyleDemographyScreen timeNational Health and Nutrition Examination SurveyPhysical therapyEnvironmental healthPopulationInternal medicine

Abstract

fetched live from OpenAlex

We sought to examine how much sedentary times needs to be replaced by light or moderate-vigorous physical activity in order to reduce frailty and protect against mortality. We built isotemporal substitution models to assess the theoretical effect of replacing sedentary behavior with and equal amount of light or moderate-vigorous activity on frailty and mortality in community-based adults aged 50 years and older. Controlling for age, sex, body mass index, marital status, race, education, employment status, and National Health and Nutrition Examination Study cycle, replacing one hour of sedentary time with moderate-vigorous or light physical activity daily was associated with a lower Frailty Index. For mortality, results varied based on frailty level. Replacing sedentary behavior with moderate-vigorous exercise was associated with lower mortality risk in vulnerable individuals, however, replacing sedentary behavior with light activity was associated with lower mortality risk in frailer individuals.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.318
Teacher spread0.291 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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