Association of Physical Activity and Heart Failure with Preserved vs. Reduced Ejection Fraction in the Elderly: The Framingham Heart Study
Bibliographic record
Abstract
AIMS: Reduced physical activity is associated with increased risk of heart failure (HF) in middle-aged individuals. We hypothesized that physical inactivity is also associated with greater HF risk in older individuals, and examined if the association was consistent for HF with preserved ejection fraction (HFPEF) vs. HF with a reduced ejection fraction (HFREF). METHODS AND RESULTS: We evaluated 1142 elderly participants (mean age 76 years) from the Framingham Study without prior myocardial infarction and who attended a routine examination when daily physical activity was assessed systematically with a questionnaire. A composite score, the physical activity index (PAI), was calculated and modelled as tertiles, and related to incidence of HF, HFPEF, and HFREF on follow-up using proportional hazards regression models adjusting for age and sex, and then additionally for standard HF risk factors. Participants with HF and EF <45% vs. ≥45% were categorized as HFREF and HFPEF, respectively. On follow-up (mean 10 years), 250 participants developed HF (108 with HFPEF, 106 with HFREF, 36 with unavailable EF). In age- and sex-adjusted models, the middle and highest PAI tertiles were associated with a 15-56% lower risk of any HF, of HFREF, and of HFPEF, with a graded response across tertiles. In multivariable models, the association of higher PAI with lower risk of any HF and with HFPEF was maintained, whereas the association with HFREF was attenuated. CONCLUSIONS: Our study of an older community-based sample extends to the elderly and to HFPEF previous findings of a protective effect of physical activity on HF risk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".