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Record W2902801134 · doi:10.1684/pnv.2018.0734

The benefits of physical activities on cognitive and mental health in healthy and pathological aging

2018· review· en· W2902801134 on OpenAlexaff
Sophie Blanchet, Samy Chikhi, Désirée B. Maltais

Bibliographic record

VenueGériatrie et Psychologie Neuropsychiatrie du Viellissement · 2018
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsCognitionDementiaAffect (linguistics)Cognitive declineEpisodic memoryDepression (economics)DiseasePsychologyExecutive functionsMental healthEffects of sleep deprivation on cognitive performanceGerontologyMedicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Aging is associated with a decreased efficiency of different cognitive functions as well as in the perceptive, physical and physiological changes. The age-related cognitive decline concerns mainly attention, executive control and episodic memory. Some factors such as being physically active protect against the age-related decline. This review will discuss how physical activity can positively affect the cognitive efficiency and mental health of older healthy individuals, and possibly reduces the risk of progression into dementia, and depression. Underlying neurophysiological mechanisms play an important role for improving attention and episodic memory, which are the most sensitive to the effects of aging. We also present recommendations for the management of physical activity for the prevention of cognitive deficits, and the reduction of depressive symptoms in older persons. Given the benefits of physical activity for the prevention of neurodegenerative disease and the improvement of the well-being, it appears to be an important low cost therapeutic approach that should be integrated into clinical practice.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.078
GPT teacher head0.409
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2018
Admission routes1
Has abstractyes

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Same venueGériatrie et Psychologie Neuropsychiatrie du ViellissementSame topicPhysical Activity and HealthFrench-language works237,207