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Abstract 4139275: Degree of rurality moderates the association of sedentary behavior with cognitive function in patients with cardiac diseases

2024· article· en· W4404363991 on OpenAlexaboutno aff
Chin‐Yen Lin, Jia Wu, Geunyeong Cha, Martha Biddle, Misook L. Chung, Frances J. Feltner, Mary Kay Rayens, Debra K. Moser

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRuralityAssociation (psychology)CognitionSedentary behaviorCardiac function curveInternal medicineCardiologyGerontologyRural areaObesityPsychiatryHeart failurePathology

Abstract

fetched live from OpenAlex

Background: Whether there are disparities in cognitive function in individuals living in rural areas compared to urban areas is unknown. Some investigators have found that rural residents have better cognitive function, while others report the opposite. Sedentary behavior is a major risk factor for cognitive health in patients with cardiac diseases; however, little is known about the impact of sedentary behavior on cognitive function at different degrees of rurality. Purpose: The aim of the study was to determine whether degree of rurality moderates the relationship between sedentary lifestyle and cognitive function among rural patients with coronary heart disease or heart failure. Methods: This study includes 135 coronary heart disease or heart failure patients residing in Appalachia (aged 59 ± 12 years, 53% female). Sedentary behavior was measured by the average daily sedentary time (in minutes) using accelerometry (ActiGraph). Cognitive function was assessed using the Montreal Cognitive Assessment-Blind. Rurality was determined by Rural-Urban Commuting Area (RUCA) codes. Participants were categorized into two groups based on degree of rurality: 1) 89 participants were included in a less rural group (RUCA codes 2−3 within Appalachia and RUCA codes 4−6 for micropolitan areas < 50,000 population); and 2) 46 participants were included in a more rural group (RUCA codes 7−10 for small towns < 9,999 population). Data were analyzed using the PROCESS macro in SPSS to test the proposed moderating effect controlling for age, gender, and depressive symptoms. Results: The time spent in sedentary behavior ranged from 3.2 to 13.3 hours per day, with an average of 7.9 ± 2.1 and a median of 7.8 hours per day. Sedentary behavior predicted cognitive function (B = −0.006, p = 0.012), but this relationship was moderated by rurality group (coefficient of rurality group*secondary behavior interaction term = 0.006, p = 0.029). Patients living in more rural areas had significantly worse cognitive function if they were sedentary for longer periods (p = 0.012), but this relationship was not observed in those living in less rural areas (p = 0.986). Conclusions: Although being sedentary is associated with worse cognitive function, degree of rurality significantly interacts with sedentary behavior in this association. Testing the impact of promoting physical activity on cognitive function is warranted in this population, particularly for those living in highly rural areas.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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
Published2024
Admission routes1
Has abstractyes

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