Footsteps to Wellness: A Systematic Review and Meta-Analysis of Walking Pace and Coronary Artery Disease Event
Bibliographic record
Abstract
Coronary artery disease (CAD) poses a global health challenge, necessitating effective preventive strategies. Despite the pivotal role of physical activity in cardiovascular health, many fall short of recommended guidelines for daily physical activity. Simple and accessible, walking presents an opportunity, with increased pace emerging as a potential strategy for reducing the risk of cardiovascular diseases. Thus, we aimed to elucidate the potential association between walking pace and the risk of CAD events in adults without a prior history of CAD through a systematic review. We searched PubMed, Scopus, Web of Science, and ScienceDirect without publication date restrictions to identify prospective cohorts that analyzed walking pace and adult CAD events. The literature search conducted from April 02, 2023, to August 21, 2023, identified a total of four studies (six cohorts) for meta-analysis using random-effects models. The Newcastle-Ottawa Scale was used to assess study quality, and data extraction involved two independent reviewers. The analysis calculated overall relative risks (RRs) and 95% confidence intervals (CIs) for those with the quickest walking paces compared to those with the slowest walking paces. A funnel plot analysis for publication bias and subgroup analysis were also conducted. Results from the meta-analysis involving 160,519 participants and 3,351 CAD events demonstrated a 46% decreased risk for those walking at the quickest pace (pooled RR = 0.54, 95% CI = 0.45-0.66). No significant heterogeneity was observed. In conclusion, walking pace emerges as a significant risk factor for CAD events in adults without a prior history of CAD. It serves as a potential screening tool to identify individuals at higher risk. Promoting a faster walking pace as a daily activity may effectively mitigate the burden of CAD.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".