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Record W6907628723 · doi:10.25384/sage.c.5869849.v1

Prospective Associations between Physical Activity and Memory in the Canadian Longitudinal Study on Aging: Examining Social Determinants

2022· other· en· W6907628723 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLongitudinal studyPhysical activityLongitudinal dataCohort studyCohortSocial determinants of healthSocial supportRegression analysisLinear regressionProspective cohort study

Abstract

fetched live from OpenAlex

ObjectivesTo examine associations between physical activity (PA) and prospectively assessed memory in a cohort of cognitively healthy adults, after accounting for understudied social determinants.MethodsWe used data from the Canadian Longitudinal Study on Aging (CLSA). PA (exposure) and memory (outcome) were assessed using validated measures in 2013–2015 and 2015–2018, respectively. Respondents reported their daily number of hours spent engaging in five different PAs. We conducted multiple imputation and used linear regression (n = 41,394), adjusting for five categories of covariates: demographics, sensory health characteristics, health behaviors, health status, and social determinants (sex/gender, education, income, social support, perceived social standing, race, and sexual orientation).ResultsIn crude models, nearly every intensity and duration of PA was associated with better memory. In fully adjusted models, protective associations were attenuated; however, some associations held: all durations of walking, most durations of light activities, moderate activities for ≥1 hour, and strenuous activities for 1 to <2 hours.DiscussionSome forms of PA may be associated with better memory. The benefits of higher intensity PA may only be realized after social determinants are addressed.

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)0.002
metaresearch head score (Gemma)0.004
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: Dataset · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
GPT teacher head0.423
Teacher spread0.183 · 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
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueSage Journals DataFrench-language works237,207