Increased arterial stiffness has been associated with an increased risk of developing cardiovascular diseases and all-cause mortality. Pulse wave velocity (PWV) is an innovative and affordable measurement of arterial stiffness which may be an accessible tool to estimate mortality risk; however, no meta-analysis has estimated its predictive performance for cardiovascular and all-cause mortality. Moreover, reference values for PWV have only been established by consensus for healthy populations. The aim of this review was to estimate PWV and especially carotid femoral PWV performance predicting cardiovascular and all-cause mortality as well as comparing the resulting cfPWV thresholds with already established values in order to increase its validity. Original studies measuring PWV thresholds and its association with cardiovascular and all-cause mortality were systematically searched. The DerSimonian and Laird method was used to compute pooled estimates of diagnostic odds ratio (dOR), and overall test performances were summarized in hierarchical summary receiver operating characteristic curves (HSROC). Six studies were included in the meta-analysis. The pooled dOR values for the predictive performance of cfPWV were 11.23 (95 % CI, 7.29–1.29) for cardiovascular mortality and 6.52 (95% CI, 4.03–10.55) for all-cause mortality. The area under the HSROC curve for cfPWV was 0.75 (95% CI, 0.69–0.81) for cardiovascular mortality and 0.78 (95% CI, 0.74–0.83) for all-cause mortality, where the closest cut-off point to the summary point was 10.7 and 11.5, respectively. This systematic review and meta-analysis demonstrates that cfPWV is a useful and accurate cardiovascular mortality predictor and that its previously estimated reference values for estimating risk may be used in high-risk populations.

Background: Physical activity and lifestyle interventions, such as a healthy diet, have been proven to be effective approaches to manage metabolic syndrome. However, these interventions require great commitment from patients and clinicians owing to their economic costs, time consumption, and lack of immediate results. Objective: The aim of this systematic review and meta-analysis was to analyze the effect of mobile-based health interventions for reducing cardiometabolic risk through the promotion of physical activity and healthy lifestyle behaviors. Methods: PubMed, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, and SPORTdiscus databases were searched for experimental studies evaluating cardiometabolic risk indicators among individuals with metabolic syndrome who were included in technology-assisted physical activity and lifestyle interventions. Effect sizes, pooled mean changes, and their respective 95% CIs were calculated using the DerSimonian and Laird method. Outcomes included the following clinical and biochemical parameters: body composition (waist circumference [WC] and BMI), blood pressure (systolic blood pressure [SBP] and diastolic blood pressure [DBP]), glucose tolerance (fasting plasma glucose [FPG] and glycated hemoglobin A1c [HbA1c]), and lipid profile (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], and triglycerides). Results: A total of nine studies were included in the meta-analysis. Owing to the scarcity of studies, only pooled mean pre-post changes in the intervention groups were estimated. Significant mean changes were observed for BMI (−1.70 kg/m2, 95% CI −3.20 to −0.20; effect size: −0.46; P=.03), WC (−5.77 cm, 95% CI −9.76 to −1.77; effect size: −0.54; P=.005), SBP (−7.33 mmHg, 95% CI −13.25 to −1.42; effect size: −0.43; P=.02), DBP (−3.90 mmHg, 95% CI −7.70 to −0.11; effect size: −0.44; P=.04), FPG (−3.65 mg/dL, 95% CI −4.79 to −2.51; effect size: −0.39; P<.001), and HDL-C (4.19 mg/dL, 95% CI 2.43-5.95; effect size: 0.23; P<.001). Conclusions: Overall, mobile-based health interventions aimed at promoting physical activity and healthy lifestyle changes had a strong positive effect on cardiometabolic risk indicators among individuals with metabolic syndrome. Nevertheless, further research is required to compare this approach with usual care in order to support the incorporation of these technologies in health systems. Trial Registration: PROSPERO CRD42019125461; https://tinyurl.com/y3t4wog4.

Aims: One of the most important mechanisms by which smoking contributes to cardiovascular disease is endothelial dysfunction, including arterial stiffness. However, the effects of smoking and smoking cessation on arterial stiffness remain unclear. This meta-analysis aimed to evaluate the effect of smoking and smoking cessation on arterial stiffness in the adult population. Methods and results: Random effects models were used to compute pooled estimates of effect size (ES) and their respective 95% confidence intervals (95% CIs) and %change in pulse wave velocity (PWv) (m/s) for the acute and chronic effect of smoking and smoking cessation, and for the effect of smoking cessation vs. the pooled ES estimate for the effect of smoking cessation vs. maintaining this behaviour. Thirteen studies were included in the meta-analysis. Smoking cessation decreased the PWv (ES −0.52, 95% CI −1.02 to −0.03, 3.5% m/s) compared to those maintaining this behaviour. Pooled estimates of both smoking conventional cigarettes and vaping significantly increased the PWv (ES 0.68, 95% CI 0.39–0.98, 10.0% m/s; and ES 0.37, 95% CI 0.14–0.61, 4.7% m/s, respectively). In addition, smoking cessation was effective in reducing arterial stiffness but only in healthy subjects (ES −0.95, 95% CI −1.85 to −0.05, −6.7% m/s). The chronic effect of smoking showed non-significant results on arterial stiffness. Conclusion: Our results show that arterial stiffness levels decrease after smoking cessation. These findings are of clinical importance, as smoking cessation partially reverses the effects of smoking on arterial stiffness.

Increasingly, health professionals and patients have begun to be involved in eHealth interventions to assist in the self-management of hypertension. Therefore, this study was aimed at comparing the effect of different types of eHealth interventions (phone calls, blood pressure telemonitoring, emails, web-site, smartphone-app, short message service (SMS) and more than two eHealth interventions) on reducing systolic and diastolic blood pressure, increasing adherence to medication treatment, improving physical activity compliance, controlling blood pressure, and improving quality of life (QoL). A systematic search in MEDLINE (via PubMed), EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science databases was conducted to identify experimental studies addressing the effect of eHealth interventions on the self-management of hypertension. Comparative evaluation of the eHealth interventions effect were performed by conducting a standard pairwise meta-analysis and a network meta-analysis for direct and indirect comparisons between eHealth interventions and control/non-intervention. Fifty-one studies were included in the analysis showing a moderate effect size for more than two types of eHealth interventions (-0.46; 95%CI: -0.64, -0.27, p < 0.001 and -0.29; 95%CI: -0.46, -0.13, p < 0.001), phone calls (-0.37; 95%CI: -0.57, -0.17, p < 0.001 and -0.29; 95%CI: -0.52, -0.07, p = 0.011) and smartphone-app (-0.26; 95%CI: -0.50, -0.01, p = 0.040 and -0.40; 95%CI: -0.70, -0.10, p = 0.010) on reducing both systolic and diastolic blood pressure, respectively. Additionally, i) smartphone-app improved medication adherence by 45%; ii) more than two types of eHealth interventions and emails improved physical activity compliance by 18% and 57% respectively; ii) more than two types of eHealth interventions, phone calls, blood pressure telemonitoring, website and SMS improved blood pressure control between 16% and 30%; and iv) blood pressure telemonitoring showed a week effect on QoL CONCLUSIONS: Our study reported eHealth to be a suitable intervention for the self-management of hypertension. Considering our results and the population's accessibility to eHealth devices, eHealth could be a useful and largely scalable tool for the self-management of hypertension. PROSPERO CRD42020187468.

Aim. To assess the incidence trends in type 1 diabetes among children and adolescents across Europe during the period from 1994 to 2022 using a systematic methodology. Materials and Methods. Cross-sectional or follow-up studies reporting population-based incidence rates (IRs) of European children and adolescents diagnosed aged <15 years with type 1 diabetes were included. The Mantel‒Haenszel or DerSimonian and Laird random-effects method was used to compute the pooled IR estimates and their 95% confidence intervals (CIs). Subgroup analyses were conducted by study year, biological sex, age group (0–4, 5−9, and 10–14 years), country, and European regions. Results. A total of 75 studies (219,331 children and adolescents aged 0–14 years) with data from 32 countries were included. Generally, a high overall rate of increase in type 1 diabetes incidence has been shown in most European countries from 1994 to 2022 in both sexes, with an overall increase from 10.85 (95% CI, 9.62–12.07) per 100,000 person-years from 1994 to 2003 to 20.96 (95% CI, 19.26–22.66) per 100,000 person-years from 2013 to 2022. Conclusions. There are substantial between-country differences in the current levels and trends of IR in type 1 diabetes in European children and adolescents. Our data suggest a worrying upward trend in most European countries.