[Web-based interventions targeting cardiovascular risk factors in older people; a systematic review and meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether web-based interventions for cardiovascular risk factor management reduce the risk of cardiovascular disease in older people. DESIGN: Systematic review and meta-analysis. METHOD: Embase, Medline, Cochrane Library and CINAHL were systematically searched from January 1995 to 3 November 2014. We included all randomised controlled trials for web-based interventions targeting cardiovascular risk factors in populations with a mean age of 50 and older. The outcome measures were cardiovascular risk factors (blood pressure, HbA1c, LDL cholesterol, weight, smoking status and physical activity) and the incidence of cardiovascular disease. We used random-effects models to pool the results of the studies. RESULTS: A total of 57 studies (19,862 participants) fulfilled eligibility criteria, and 47 of these were suitable for meta-analysis. We found a significant reduction in systolic blood pressure (-2.66 mmHg, 95% CI -3.81 to -1.52), diastolic blood pressure (-1.26 mmHg, 95% CI -1.92 to -0.60), HbA1c level (-0.13%, 95% CI -0.22 to -0.05), LDL cholesterol level (-0.06 mmol/l, 95% CI -0.10 to -0.01), weight (-1.34 kg, 95% CI -1.91 to -0.77), and an increase in physical activity (standardized mean difference 0.25, 95% CI 0.10-0.39) in the intervention group when compared with the control group. Treatment effects were more pronounced in studies of short duration (< 12 months) and when combining the web-based intervention with human support by a health care professional. No difference in the incidence of cardiovascular disease was found between groups. CONCLUSION: Web-based interventions have a beneficial effect on the cardiovascular risk profile, but this effect is modest and declines with time. Currently, there is insufficient evidence that this can prevent cardiovascular disease. A focus on long-term effects, effect-sustainability and clinical endpoints is recommended for future studies.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".