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Record W4405415539 · doi:10.1177/08982643241307757

Associations of Muscle-Strengthening Activity and Cognitive Function in Community-Dwelling Middle-Aged and Older Adults

2024· article· en· W4405415539 on OpenAlexaboutno aff
Yuzi Zhang, Laura F. DeFina, David Léonard, Baojiang Chen, Emily T. Hébert, Carolyn E. Barlow, Andjelka Pavlovic, Harold W. Kohl

Bibliographic record

VenueJournal of Aging and Health · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionGerontologyCross-sectional studyLogistic regressionMedicineOdds ratioOddsPhysical therapyLongitudinal studyPsychologyCognitive impairmentDemographyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

ObjectiveTo determine the associations between muscle-strengthening activity (MSA) and cognitive function among middle-aged and older adults.MethodsThis cross-sectional study included 2973 participants aged ≥55 in the Cooper Center Longitudinal Study. Participants self-reported leisure-time physical activity. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). The associations of reported MSA frequency (0-1 vs. ≥2 sessions/week) and volume (zero, low: ≤250, medium: 250-420, high: >420 MET minutes/week) with mild cognitive impairment (MCI, defined as MoCA<26) and MoCA total score were examined using logistic and linear regression.ResultsIndividuals who participated in ≥2 MSA sessions/week had a significantly higher MoCA total score. Participants with medium MSA volume were significantly associated with lower odds of being classified as MCI and associated with a higher MoCA total score than those with zero volume.ConclusionsEngaging in MSA is associated with cognitive health among middle-aged and older adults independent of aerobic exercise.

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 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)0.002
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 score0.510
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.373
Teacher spread0.285 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

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