Protecting the Heart in Motion: The Role of Physical Activity and Cardiorespiratory Fitness in Preventing Sudden Cardiac Death
Bibliographic record
Abstract
Sudden cardiac death (SCD) remains one of the most devastating manifestations of cardiovascular disease. While traditional risk stratification has focused on structural heart disease and electrophysiological markers, growing evidence suggests that modifiable lifestyle factors-particularly physical activity (PA) and cardiorespiratory fitness (CRF)-play a critical role in mitigating the risk of SCD. This narrative review synthesizes evidence on the associations between PA, CRF, and SCD risk. It explores potential biological mechanisms underlying these relationships, identifies key gaps in the literature, and discusses the clinical and public health implications. A substantial body of prospective cohort studies and meta-analyses demonstrates a strong, inverse, and dose-dependent association between both PA and CRF and the risk of SCD. Engaging in ⩾4 hours/week of moderate-to-vigorous PA or achieving CRF levels of ⩾8 to 10 METs is associated with 40% to 50% reductions in SCD risk. CRF also modifies the risk conferred by traditional cardiovascular risk factors such as hypertension, diabetes, and systemic inflammation. Proposed mechanisms include favorable modulation of cardiovascular risk profiles, improved autonomic regulation, anti-arrhythmic and anti-ischemic effects, and enhanced myocardial function. However, evidence gaps persist regarding causal inference (absence of Mendelian randomization studies), optimal PA and CRF thresholds, sex- and age-specific effects, and interactions with other risk factors. PA and CRF are powerful, modifiable predictors of SCD and should be integrated into preventive strategies and routine clinical assessments. Targeted interventions to increase PA and improve CRF, especially among underrepresented and high-risk groups, offer an important opportunity to reduce the burden of SCD globally.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".