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Record W7005176899

Physical activity participant: engagement toward cognitive and physical function among rural veteran’s athletes / Siti Maizatul Akmal Mahbur and Wahidah Tumijan

2024· article· en· W7005176899 on OpenAlexaboutno aff

Bibliographic record

VenueUiTM Institutional Repositories (Universiti Teknologi MARA) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityCognitionAthletesTest (biology)Physical activity levelPhysical fitnessPhysical exercise
DOInot available

Abstract

fetched live from OpenAlex

Engaging in physical activity is essential for preserving and enhancing cognitive and physical abilities, particularly for rural veteran athletes. Nevertheless, there is a dearth of research documenting the outcomes specifically for this age group. The study aimed to determine the relationship between physical activity, cognitive function, and physical function among rural veteran athletes. A cross-sectional correlational design was used. Data was collected using the Montreal Cognitive Assessment (MoCA), Sports Performance Physical Battery Test – Chair Stand Test (SPPB), Physical Activity Readiness Questionnaire for Everyone (PAR-Q+), and Community Health Activities Model Program for Seniors (CHAMPS). There is a significant moderately positive relationship (r = 0.460, p = 0.009) between physical activity for all activities and cognitive performance. Physical function showed a non-significant correlation (r = 0.346, p = 0.057) with physical activity for all activities. In conclusion, rural veteran athletes engage in a substantial amount of physical activity, meeting or exceeding recommended guidelines. There is a positive relationship between physical activity levels and cognitive function, indicating that higher physical activity is associated with better mental health. The relationship between physical activity and physical function is weak, suggesting that while physical activity.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.001
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 head0.086
GPT teacher head0.392
Teacher spread0.306 · 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.

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
Published2024
Admission routes1
Has abstractyes

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