Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with a significant portion of these deaths considered premature in individuals under 70. In developed nations, public health policies increasingly emphasize strengthening primary care, identifying high-risk individuals, and implementing preventative strategies. This focus stems from the understanding that while some risk markers (age, sex, genetics) are unmodifiable, risk factors (RFs) – such as smoking, obesity, sedentary lifestyle, diabetes, and hypertension – are modifiable and contribute significantly to CVD morbidity and mortality. The Framingham study identified these six primary RFs, highlighting the increased risk of CVD and death among individuals with any combination of these factors. Accurate cardiovascular risk (CVR) assessment is crucial, often employing models like the REGICOR tables (derived from the Framingham equation) to stratify risk based on the presence of RFs. Individuals with a CVR exceeding 20% are classified as high-risk. However, reaching at-risk populations faces challenges due to economic, geographic, and access limitations. Telemedicine, utilizing information and telecommunication technologies, offers a potential solution by delivering clinical, administrative, and educational services remotely, addressing these barriers. Telemedicine interventions, encompassing teleconsultation, tele-education, telemonitoring, and even telesurgery, are gaining popularity. Clinical guidelines recommend multidisciplinary interventions addressing multiple RFs simultaneously, and telemedicine provides a technologically advanced method for implementing these strategies. These interventions aim to promote a cardio-healthy lifestyle through health education and pharmacological control of hypertension, hypercholesterolemia, diabetes, etc. While developed countries are increasingly adopting telemedicine and e-health for chronic disease management, the effectiveness of these approaches in mitigating CVD risk remains uncertain. This systematic review and meta-analysis aims to determine the effectiveness of telemedicine-based interventions in reducing cardiovascular RFs.

The introduction adequately summarizes existing literature on CVD epidemiology, risk factors, and the potential of telemedicine. It cites key studies such as the Framingham Heart Study and mentions the use of risk assessment tools like REGICOR tables. The authors highlight the challenges of reaching at-risk populations and the growing use of telemedicine as a solution. A gap in the literature is identified: the unclear effectiveness of telemedicine in mitigating CVD risk, justifying the need for this systematic review and meta-analysis.

This systematic review and meta-analysis followed PRISMA 2020 guidelines and a registered PROSPERO protocol (CRD42022365395), incorporating recommendations from the Cochrane Manual. A comprehensive literature search across PubMed, Scopus, Cinhal, and Web of Science databases was conducted between September and October 2022. The search strategy used a combination of keywords related to telemedicine, e-health, and various cardiovascular risk factors (hypertension, obesity, diabetes, etc.), targeting studies published between 2017 and 2022. Inclusion criteria focused on randomized controlled trials (RCTs) involving the general population, excluding ethnic minorities and individuals with health problems unrelated to the study's defined cardiovascular RFs. Rayyan, a virtual tool, assisted in identifying and removing duplicate articles. Two independent reviewers assessed article eligibility, with a third reviewer resolving any discrepancies. Data extraction focused on variables measuring CVRFs: glycosylated hemoglobin (HbA1c), blood pressure, weight, and moderate-to-vigorous physical activity (MVPA). Additional data points included author, publication year, country, population characteristics, intervention and control group characteristics, telemedicine type, and follow-up time. Methodological quality was assessed using the PEDro scale, categorizing studies as Poor (0–3), Fair (4–5), Good (6–8), and Excellent (>9). A random-effects model meta-analysis was performed for mean and standard deviation of changes over time in the observed variables, using 95% confidence intervals (CIs) where SD was unavailable. Funnel plots were used to assess publication bias.

The initial database search yielded 763 articles, reduced to 28 eligible RCTs after screening and duplicate removal. These 28 studies encompassed a total of 5460 participants aged 18–75 years, each possessing at least one of the target RFs (diabetes, hypertension, overweight/obesity, or sedentary lifestyle). Studies were conducted across various countries (US, Australia, UK, Canada, Spain, Malaysia, Belgium, Iran, South Korea, Germany, Turkey, France, China, and Taiwan). Intervention durations ranged from one month to two years. **Diabetes:** Thirteen studies evaluated telemedicine's impact on diabetes; four showed statistically significant HbA1c reductions. Meta-analysis revealed a small but significant mean effect size (g = −0.432, p < 0.001) for HbA1c reduction. Subgroup analysis excluding studies without blinded participants showed a moderate effect (g = −0.538, p = 0.019). **Hypertension:** Six studies assessed telemedicine's effect on blood pressure; four reported statistically significant reductions. Meta-analysis showed moderate and significant effects on systolic (g = −0.775, p < 0.001) and small but significant effects on diastolic (g = −0.447, p < 0.001) blood pressure. Subgroup analysis excluding studies without blinded participants showed a moderate effect on systolic blood pressure (g = −0.733, p = 0.006). **Overweight/Obesity:** Seven studies evaluated telemedicine's impact on body weight; five showed statistically significant reductions. Meta-analysis revealed a moderate and significant mean effect size (g = −0.628, p < 0.001). Subgroup analyses excluding studies without blinded participants or with inadequate follow-up revealed moderate and large effect sizes, respectively (g = −0.728, p = 0.002; g = −0.957, p = 0.001). **Sedentarism:** Two studies evaluated telemedicine's impact on sedentary behavior but showed no statistically significant findings. Meta-analysis was not feasible due to data presentation limitations.

This review and meta-analysis demonstrate the clinical relevance of telemedicine interventions for managing cardiovascular risk factors, despite some heterogeneity in study results and protocols. While statistically significant effects vary across RFs, a consistent trend of improvement is observed. The improvement in risk factor control, particularly in weight reduction (overweight/obesity studies) and HbA1c reduction (diabetes studies), suggests that telemedicine enhances treatment adherence, offering consistent reminders, clarification of doubts, and continued contact with healthcare professionals, overcoming geographical limitations. Telemedicine's accessibility and cost-effectiveness are highlighted, especially for populations with limited access to healthcare, potentially contributing to reduced healthcare costs and improved equity of care. The findings emphasize the importance of using evidence-based telemedicine interventions validated through rigorous clinical trials. Although several studies found no statistically significant results in specific outcomes (sedentary behavior), there's notable evidence of telemedicine's overall positive impact on multiple risk factors, potentially stemming from improved adherence to health guidelines.

Although telemedicine's effects on cardiovascular risk factors show some heterogeneity, its clinical significance is clear. Telemedicine interventions improve long-term risk factor management and body composition, enhancing treatment adherence. Standardized protocols and evidence-based telemedicine practices are essential. Telemedicine also improves access to healthcare and reduces barriers, especially valuable in resource-limited settings.

The review's limitations include the heterogeneity of telemedicine intervention protocols across studies, hindering reproducibility. Publication bias is also a possibility, as only published articles were included. Future research should focus on developing standardized, widely applicable telemedicine interventions for cardiovascular risk reduction and address potential biases.