Infrastructure, policy and regulatory interventions to increase physical activity to prevent cardiovascular diseases and diabetes: a systematic review
Bibliographic record
Abstract
BACKGROUND: Noncommunicable diseases are major contributors to morbidity and mortality worldwide. Modifying the risk factors for these conditions, such as physical inactivity, is thus essential. Addressing the context or circumstances in which physical activity occurs may promote physical activity at a population level. We assessed the effects of infrastructure, policy or regulatory interventions for increasing physical activity. METHODS: We searched PubMed, Embase and clinicaltrials.gov to identify randomised controlled trials (RCTs), controlled before-after (CBAs) studies, and interrupted time series (ITS) studies assessing population-level infrastructure or policy and regulatory interventions to increase physical activity. We were interested in the effects of these interventions on physical activity, body weight and related measures, blood pressure, and CVD and type 2 diabetes morbidity and mortality, and on other secondary outcomes. Screening and data extraction was done in duplicate, with risk of bias was using an adapted Cochrane risk of bias tool. Due to high levels of heterogeneity, we synthesised the evidence based on effect direction. RESULTS: We included 33 studies, mostly conducted in high-income countries. Of these, 13 assessed infrastructure changes to green or other spaces to promote physical activity and 18 infrastructure changes to promote active transport. The effects of identified interventions on physical activity, body weight and blood pressure varied across studies (very low certainty evidence); thus, we remain very uncertain about the effects of these interventions. Two studies assessed the effects of policy and regulatory interventions; one provided free access to physical activity facilities and showed that it may have beneficial effects on physical activity (low certainty evidence). The other provided free bus travel for youth, with intervention effects varying across studies (very low certainty evidence). CONCLUSIONS: Evidence from 33 studies assessing infrastructure, policy and regulatory interventions for increasing physical activity showed varying results. The certainty of the evidence was mostly very low, due to study designs included and inconsistent findings between studies. Despite this drawback, the evidence indicates that providing access to physical activity facilities may be beneficial; however this finding is based on only one study. Implementation of these interventions requires full consideration of contextual factors, especially in low resource settings. TRIAL REGISTRATION: PROSPERO 2018 CRD42018093429.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".