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Record W4297338575 · doi:10.1038/s41398-022-02191-7

Physical activity partly mediates the association between cognitive function and depressive symptoms

2022· article· en· W4297338575 on OpenAlexfundno aff
Zsófia Csajbók, Stefan Sieber, Stéphane Cullati, Pavla Čermáková, Boris Cheval

Bibliographic record

VenueTranslational Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNational Institute on AgingMax-Planck-GesellschaftMinisterstvo Zdravotnictví Ceské RepublikyBanting Research FoundationNatural Sciences and Engineering Research Council of CanadaEuropean CommissionUniverzita Karlova v PrazeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAssociation (psychology)Depressive symptomsCognitionClinical psychologyPsychologySchizophrenia (object-oriented programming)Depression (economics)Function (biology)PsychiatryMedicinePsychotherapistGeneticsBiology

Abstract

fetched live from OpenAlex

Cognitive function, physical activity, and depressive symptoms are intertwined in later life. Yet, the nature of the relationship between these three variables is unclear. Here, we aimed to determine which of physical activity or cognitive function mediated this relationship. We used large-scale longitudinal data from 51,191 adults 50 years of age or older (mean: 64.8 years, 54.7% women) from the Survey of Health, Ageing and Retirement in Europe (SHARE). Results of the longitudinal mediation analyses combined with autoregressive cross-lagged panel models showed that the model with physical activity as a mediator better fitted the data than the model with cognitive function as a mediator. Moreover, the mediating effect of physical activity was 8-9% of the total effect of cognitive function on depressive symptoms. Our findings suggest that higher cognitive resources favor the engagement in physical activity, which contributes to reduced depressive symptoms.

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)0.000
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 score0.016
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.309
Teacher spread0.284 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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