Does cognitive function affect functional capacity and perceived fatigue severity after exercise in patients with coronary artery disease?
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Successful execution of exercise-based cardiac rehabilitation programs, an important branch of physiotherapy in individuals with coronary artery disease (CAD), depends on adequate cognitive abilities. It has been identified that coronary microvascular dysfunction, marked by reduced coronary flow reserve, is associated with impaired cerebral blood flow, affecting haemodynamic and cognitive performance. This study aimed to investigate how cognitive function influences functional capacity and differences in fatigue perception in CAD patients. METHODS: Fifty CAD patients, with an average age of 59.40 ± 6.58 years, were evaluated for comorbidities (Charlson comorbidity index), number of CAD risk factors (hypertension, diabetes mellitus, dyslipidaemia, smoking, and physical inactivity), cognitive performance (Montreal cognitive assessment scale [MoCA]), functional capacity (incremental shuttle walk test [ISWT]), exercise-induced fatigue (Modified Bourg Scale), and physical activity (PA) levels (international physical activity questionnaire-short form). RESULTS: Analyses focused on the links between MoCA scores and CRF, ISWT outcomes, and differences in fatigue perception. Findings revealed a strong positive link between MoCA scores and ISWT performance (r = 0.83, p < 0.001), and a strong inverse relationship between CRF and MoCA scores (r = -0.95, p < 0.001). In addition, MoCA score was positively correlated with differences in fatigue perception (r = 0.88, p < 0.001). CONCLUSION: These results highlight the critical role of cognitive function in determining functional capacity and managing fatigue in CAD patients. They also suggest that cognitive interventions may be a potential adjunctive approach in physiotherapy programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".