Summary of findings

The impact of lifestyle-based interventions on absolute cardiovascular disease risk

Should lifestyle-based interventions vs. usual care be used to mitigate absolute CVD risk in adults?

Bibliography: Kariuki, J. K., Imes, C. C., Engberg, S. J, Scott, P., Klem, M.L., Yamnia, C. I. The impact of lifestyle-based interventions on absolute cardiovascular disease risk

Outcomes	Anticipated absolute effects* (95% CI)	№ of participants (studies)	Certainty of the evidence (GRADE)
	Standardized mean difference with lifestyle-based interventions
Absolute CVD risk score assessed with validated risk assessment algorithms Scale from: 0 to 100 follow up: range 12 weeks to 18 months	SMD 0.39 SD lower (0.74 lower to 0.03 lower)	3605 (15 RCTs)	⨁⨁⨁O Moderatea
Absolute CVD risk score assessed with validated risk assessment algorithms Scale from: 0 to 100 follow up: range 3 weeks to 16 years	SMD 0.39 SD lower (0.60 lower to 0.17 lower)	1885 (14 Quasi-experimental studies)	⨁⨁OO Lowb,c

*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). CI: Confidence interval; SMD: Standardized mean difference

GRADE Working Group grades of evidence High certainty: We are very confident that the true effect lies close to that of the estimate of the effect Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different Low certainty: Our confidence in the effect estimate is limited: True effect may be substantially different from the estimate the effect Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect Explanations The uncertainty index was 96, mainly due to the use of different risk assessment tools and varying study design in pooled analyses. We mitigated the risk by employing random effect models in the meta-analysis. The quasi-experimental design increases the likelihood of bias in the implementation of study protocols and in outcome measurements. The uncertainty index was 88, mainly due to the use of different risk assessment tools and varying study design in pooled analyses. We mitigated the risk by employing random effect models in the meta-analysis.