The impact of physical activity and an additional behavioural risk factor on cardiovascular disease, cancer and all-cause mortality: a systematic review
Bibliographic record
Abstract
BACKGROUND: Regular physical activity improves overall health, and has the capacity to reduce risk of chronic diseases and death. However, better understanding of the relationship between multiple lifestyle risk behaviours and disease outcomes is pertinent for prioritising public health messaging. The aim of this systematic review is to examine the association between physical inactivity in combination with additional lifestyle risk behaviours (smoking, alcohol, diet, or sedentary behaviour) for cardiovascular disease, cancer, and all-cause mortality. METHODS: We searched Ovid Medline, EMBASE, and the Cochrane Register from 1 January 2010 to 12 December 2017, for longitudinal observational studies of adults (18+ years) in the general population with a publication date of 2010 onwards and no language restriction. Main exposure variables had to include a physical activity measure plus at least one other lifestyle risk factor. In total, 25,639 studies were identified. Titles, abstracts and full-text articles of potentially relevant papers were screened for eligibility. Data was extracted and quality assessment was completed using a modified Newcastle-Ottawa Scale (NOS). RESULTS: Across the 25 eligible studies, those participants who reported being physically active combined with achieving other health behaviour goals compared to those who were categorised as physically inactive and did not achieve other positive lifestyle goals, were at least half as likely to experience an incident cardiovascular disease (CVD) event, die from CVD, or die from any cause. These findings were consistent across participant age, sex, and study length of follow-up, and even after excluding lower quality studies. We also observed a similar trend among the few studies which were restricted to cancer outcomes. Most studies did not consider epidemiological challenges that may bias findings, such as residual confounding, reverse causality by pre-existing disease, and measurement error from self-report data. CONCLUSIONS: High levels of physical activity in combination with other positive lifestyle choices is associated with better health outcomes. Applying new approaches to studying the complex relationships between multiple behavioural risk factors, including physical activity, should be a priority.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".