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Record W6996751498

Strength and Endurance Exercises Combined Effects on Cognitive Function in Older Adults.

2020· article· en· W6996751498 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Showcase Research, Scholarship, & Creative Works (University of Lynchburg) · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsCardiorespiratory fitnessCognitionEndurance trainingPhysical fitnessCardiovascular fitnessPhysical exerciseVO2 maxGrip strength
DOInot available

Abstract

fetched live from OpenAlex

Aging is associated with a decline in exercise capacity and cardiovascular fitness which combined has been shown to negatively impact cognitive function in older adults. Endurance exercise is a common strategy employed to promote gains in both exercise capacity and cardiorespiratory fitness and is associated with an increase in cognitive function in older adults. There is a limited understanding of the effects of combined endurance and strength exercise on exercise capacity and cognitive function in older adults. Therefore, the purpose of this study is to determine the effects of a six-week community-based exercise program that consists of strength and endurance exercises on exercise capacity, cardiorespiratory fitness, and cognitive function in independent older adults. Participants were enrolled in the University of Lynchburg Active Aging Program which consisted of weekly goals of 150-minutes of endurance exercise and two days of strength training exercises that targeted the major muscle groups. Exercise capacity and cardiorespiratory fitness were assessed through the 6-Minute Walk Test. The Montreal Cognitive Assessment measured cognitive function before and after the 6-week exercise program.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.048
GPT teacher head0.308
Teacher spread0.260 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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