Degree of rurality moderates the association of sedentary time with cognitive function in rural patients with cardiac diseases and depressive symptoms
Bibliographic record
Abstract
BACKGROUND: Prolonged sedentary time has been linked to impaired cognitive outcomes. However, the impact of sedentary time on cognitive function at different degrees of rurality is not yet well understood in patients with cardiac diseases and depressive symptoms. PURPOSE: To determine whether degree of rurality moderates the relationship between sedentary time and cognitive function. METHODS: This study includes 135 coronary heart disease or heart failure patients, primarily residing in rural Kentucky, including Appalachian areas, United States. Sedentary time 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. Patients were categorized into two groups by rurality: (1) 89 patients in a less rural group (RUCA codes 4-6); and (2) 46 patients in a more rural group (RUCA codes 7-10). Data were collected May 2021-September 2022 and analyzed using the Hayes PROCESS macro in SPSS. RESULTS: Sedentary time predicted cognitive function (B = -0.006, p = 0.019), and this relationship was moderated by rurality (interaction term = 0.006, p = 0.022). Patients living in more rural areas had significantly worse cognitive function when sedentary for longer periods (p = 0.019); specifically, every 100-min increase in sedentary time was associated with a 0.6-point decrease in cognitive function score. However, this relationship was not observed in those living in less rural areas (p = 0.658). CONCLUSIONS: Testing the impact of interventions aimed at reducing sedentary time on cognitive function is warranted in this population, particularly for those living in highly rural areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".