MétaCan
Menu
Back to cohort
Record W3119345023 · doi:10.1024/1662-9647/a000255

Daily Physical Activity in Older Age

2021· article· en· W3119345023 on OpenAlexaffabout
Lea O. Wilhelm, Theresa Pauly, Maureen C. Ashe, Christiane A. Hoppmann

Bibliographic record

VenueGeroPsych · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysical activityAffect (linguistics)Fear of fallingPsychologyGerontologyFalling (accident)Activities of daily livingTraitOlder peopleMultilevel modelDemographyMedicinePoison controlInjury preventionPhysical medicine and rehabilitationPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

Abstract. Affective barriers like negative affect (time-varying subjective state) or fear of falling (person-trait) may reduce daily physical activity among older adults. A group of 123 community-dwelling older adults ( M age = 71.83, range = 64–85, 63% women) from Canada participated in a 10-day time-sampling study. We used accelerometer-assessed physical activity, assessing negative affect three times per day and fear of falling once prior to the 10-day period. Using multilevel models, we noted considerable variability in physical activity between days (activity counts: 47%; steps: 55%). We found time-varying negative associations between daily physical activity and daily negative affect. Fear of falling was not related to daily physical activity. Findings point to the merit of examining time-varying differences in subjective experiences when looking for physical activity barriers in older age.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.367
Teacher spread0.315 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueGeroPsychSame topicPhysical Activity and HealthFrench-language works237,207