Text messages as a tool to improve cardiovascular disease risk factors control: A Systematic Review and Meta-Analysis

Text messages as a tool to improve cardiovascular disease risk factors control: A Systematic Review and Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Text messages as a tool to improve cardiovascular disease risk factors control: A Systematic Review and Meta-Analysis Ernesto Calderon Martinez, Stephin Zachariah Saji, Jonathan Victor Salazar Ore, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5112776/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted 13 You are reading this latest preprint version Abstract Background Cardiovascular diseases (CVDs) are the leading cause of global mortality, claiming 17.9 million lives annually. Major behavioral risk factors include unhealthy diet, physical inactivity, tobacco use, and excessive alcohol consumption. Text messaging interventions can potentially improve individual risk factors and encourage healthy habits. They have been shown to manage risk factors and disease progression. This systematic review and meta-analysis aimed to evaluate the efficacy of text messaging interventions for the primary prevention of CVD risk factors. Methods This review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. Searches were conducted on PubMed, MEDLINE, Cochrane, Scopus, Web of Science, Embase, and CINAHL using MeSH and free-text terms related to cardiovascular disease and text messaging interventions on 18/03/2024. Results Out of 6142 identified articles, 22 studies met the inclusion criteria. The meta-analysis revealed that text messaging interventions significantly improved medication adherence, with a pooled effect size of Mean Difference (MD) of 0.61 (95%CI: 0.37 to 0.85; p < 0.0001, I² = 0.0%). They also significantly reduced diastolic blood pressure by MD of -2.66 (95% CI: -4.62 to -0.70, I² = 85%, p = 0.007) and systolic blood pressure by MD of -6.11 (95% CI: -10.25 to -1.97, I² = 96%, p = 0.003). However, no significant improvements were observed in BMI, LDL, HDL, total cholesterol, or HbA1c levels. Conclusion Text messaging interventions effectively improve medication adherence and help in the reduction of blood pressure, making them a promising tool for CVD risk control. However, their impact on other cardiovascular risk factors is limited, indicating the need for further research to explore long-term effects and personalized interventions for diverse populations. Integrating these digital tools into healthcare strategies could enhance CVD prevention efforts and improve cardiovascular risk factors control outcomes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular diseases (CVDs) remain the leading cause of global mortality, claiming 17.9 million lives annually( 1 ). They encompass a spectrum of heart and blood vessel disorders, with heart attacks and strokes alone responsible for over 80% of these deaths occurring prematurely in individuals under 70( 1 ). Major behavioral risk factors contributing to CVDs include an unhealthy diet, physical inactivity, tobacco use, and excessive alcohol consumption. These behaviors have been related to elevated blood pressure, glucose levels, lipids, and obesity, all of which significantly increase susceptibility to heart attack, stroke, and related complications( 1 , 2 ). CVD incidence is growing among younger adults( 3 , 4 ). This could be explained by the rising rates of uncontrolled CVD risk factors, such as obesity, smoking, exercise, and medication adherence, among others( 5 ). Therefore, there is a need for novel strategies to control CVD risk factors. Recent research has focused on using text messages for health purposes, which send information in near-real time to thousands of people as recipients of standardized, bulk messages or personalized or tailored messages( 6 ). Text Messaging intervention strategy has been found to work in risk factor control and progression of diseases, including diabetes, resulting in a low-cost and effective tool for subjects with impaired glucose( 7 ). In the CVD context, although drug interventions are cost-effective in managing and reducing the risk of recurrent cardiovascular events, medication adherence remains suboptimal. As a scalable and cost-effective tool, mobile phone text messaging presents an opportunity to convey health information, deliver electronic reminders, and encourage behavior change( 8 ). Text messaging intervention could successfully improve individual risk factors and promote healthy habits. Previous studies have proven the efficacy of controlling these risk factors with variability among the results among the different risk factors ( 9 – 12 ). Despite there are previous systematic reviews that address text messages and other ways of mobile health (health), it is mandatory to include a quantitative study to measure the potentially significant effect of text messages as a promising tool to control CVD risk factors, to correctly measure the effect that this intervention has into the risk factors and identify the most effective way to use and reach a consensus of it is used. This systematic review and meta-analysis aimed to evaluate the efficacy of text messaging as a strategy to control CVD risk factors. Methods The present study employed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020( 13 ) guidelines to ensure a comprehensive and systematic approach to our review. The protocol for this review has been registered on PROSPERO databases under the following available ID: CRD42024529187. CRITERIA FOR CONSIDERING STUDIES IN THIS REVIEW TYPES OF PARTICIPANTS This study has set specific participant selection criteria, including all participants over 18 years. Exclusion criteria are studies assessing multiple modalities and studies involving teenagers and child populations. TYPES OF INTERVENTION The intervention to be included in this analysis should be a text message focusing on controlling cardiovascular disease risk factors in the patients via text messaging service. The control group doesn't use the text message service. Studies assessing other modalities (websites, email, apps, video, web portal, phone calls) without a specific group of text messages and control group were excluded. OUTCOMES The outcomes to be measured include studies that report the effectiveness of texting messages for cardiovascular risk factor control and exclude studies that do not report relevant outcomes related to this topic. The effectiveness will be measured with mean difference (MD); in cases where these values are not reported, they will be calculated manually using the information provided by the author. This review will specifically focus on the impact of text messages on improving CVD-related factors, such as smoking cessation, HbA1c levels, diastolic blood pressure (DBP), systolic blood pressure (BP), cholesterol levels, LDL levels, HDL levels, medication adherence, and exercise. TYPES OF STUDY For our research, we systematically reviewed relevant studies published from 2000 to 2023, available in English; we meticulously screened and analyzed randomized clinical trials (RCTs) to reduce bias and prove cause-and-effect relations between interventions and results. We excluded case series, cohort, case-control trials, cross-sectional, dissertations, book chapters, protocol articles, reviews, news articles, conference abstracts, letters to the editor, editorials, comment publications, systematic reviews, and meta-analyses. Furthermore, we excluded studies that did not clearly describe their operationalizations, were duplicated, and could not obtain the necessary data or receive a response from the original author via email. Searching methods We perform our search on PubMed, MEDLINE, Cochrane, Scopus, Web of Science, Embase, and CINAHL, using Medical Subject Headings (MeSH) terms and free text terms such as: ‘’cardiovascular disease’’, ‘’ Text messaging’’, ‘’Prevention’’, ‘’Cellular phone’’, ‘’Risk reduction behavior’’ on 18/03/2024. The specific search per database can be found in the supplementary material (Supplementary Table 1–6). We adhered to a PRISMA( 13 ) flowchart to guide the systematic review article selection process, resulting in a uniform dataset and enhancing the accuracy and reliability of our findings. Selection of studies Following an initial screening based on the title and abstract, two reviewers (AK, SM) independently selected trials for inclusion in this review using predetermined inclusion and exclusion criteria. This search was performed using Rayyan( 8 ) to extract relevant data, and duplicates were filtered. Keywords were employed to highlight inclusion and exclusion criteria-related words on Rayyan (Supplementary material). Consensus and consultation with a third reviewer (JVSO) resolved any disagreement about including the studies. Subsequently, a full-text analysis was conducted with two reviewers (LS, VA) independently selected trials for inclusion in the review using predetermined inclusion and exclusion criteria. Consensus and consultation with a third review author (SZS) resolved disagreements about including studies. Assessment of risk of bias in included studies We evaluated the data using the criteria outlined in the Cochrane Handbook ( 14 ). We applied the Cochrane RoB 2.0 tool( 10 ) for randomized controlled trials (RCTs) to assess the quality of studies included in the systematic review. Two independent reviewers (AK and JVSO) evaluated the risk of bias in each study, considering the specific criteria and guidelines provided by the respective tools. Any reviewer discrepancies have been resolved through discussion with a third, blinded reviewer (ECM). Statistical Analysis We will only perform a meta-analysis when two or more studies report an outcome according to the Cochrane Handbook using R Software version 3.6.0 to calculate the effect size( 15 ). Effect sizes were presented as mean differences with 95% confidence intervals (CI). The random-effects model was used for pooling analysis to compensate for the heterogeneity of study statistics. In this regard, I2 ≥ 50% and ≥ 75%( 16 ) indicated substantial and considerable heterogeneity, study removal method to the sub-analysis to assess whether any individual study exerted particular influence on the overall effect size, p‐values < 0.05 were considered statistically significant. Results Across the database, we identified a total of 6142 possible articles. After a thorough examination, 17 duplicate articles were removed before screening. 6125 articles were reviewed during the screening process, and 6084 were excluded. From the remaining, 41 articles were sought for retrieval, but four reports were not retrieved. Of the 37 reports assessed for eligibility, 15 were excluded due to the wrong study design. Ultimately, 22 studies were included in the final review process. This process is summarized in the PRISMA Flow Chart Fig. 1 . Study characteristics The study characteristics encompass a comprehensive evaluation of text messaging interventions as a tool for cardiovascular risk factor control, including cholesterol levels, medication adherence, physical activity, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking, and HbA1c. The research was conducted across a diverse range of countries such as the United Kingdom (12%), Canada (5%), Pakistan (5%), Iran (10%), Australia (22%), China (10%), New Zealand (10%), Nepal (5%), Italy (5%), the United States (10%), Brazil (5%), and Turkey (5%), indicating a broad geographic representation. Each study utilized different types of SMS interventions, ranging from one-way to two-way messaging, with varying frequencies (daily, weekly, etc.) and durations (ranging from weeks to a year). These interventions were generally automated and targeted at improving lifestyle behaviors, enhancing medication adherence, or providing motivational and educational support. Most studies (61%) reported a positive impact of these interventions on cardiovascular risk factors control, with significant improvements in medication adherence (34% of studies), increased physical activity (28% of studies), and notable reductions in SBP and DBP (51% of studies). Studies reported varied outcomes, often focusing on blood pressure, physical activity, and medication adherence. Regarding SBP, Tam H (2023) in China and Bhandari B (2022) in Nepal observed reductions, while Bobrow K (2014) in South Africa found only a small reduction in SBP control. Chow C (2015) reported significant SBP, LDL-C levels, and BMI reductions in Australia. However, Kes D (2021) in Turkey observed lower BP levels, while Golshahi J (2015) in Iran and Chow C (2022) in Australia reported no significant effects on BP or lifestyle changes. Medication adherence was a key outcome in multiple studies. Kamal A (2015) in Pakistan, Khonsari S (2014) in Malaysia, Dale L (2015b) in New Zealand, and Kes D (2021) in Turkey all reported improved adherence. In contrast, studies by Chow C (2022) in Australia and Santo K (2018) found no significant difference in adherence. Physical activity improvements were noted by Dale L. (2015a) in New Zealand, Foccardi G. (2021) in Italy, and Klimis H (2021) in Australia, with sustained fitness gains observed by Duscha B. (2018) in the USA. Song Y (2019) reported that exercise tolerance in China has improved. Other notable outcomes include improved medication self-efficacy, as found by Park L (2014) in the USA, while Wald D (2014) in the UK highlighted enhanced medication adherence in patients on cardiovascular treatments. Kiselev A (2012) in Russia found improved health outcomes, whereas Golshahi J (2015) in Iran saw no significant impact on lifestyle changes or hypertension control. Smoking outcomes were found in 4 studies. Chow C (2015) and Kiselev A. (2012) found a significant smoking reduction in the intervention group in Australia and Russia. In contrast, Santo K (2018) and Chow C (2022), both in Australia, have not found significant differences between interventions and the control group. All findings are summarized in Table 1 ( 9 , 11 , 12 , 17 – 31 ). Risk of bias assessment This risk of bias assessment utilized Cochrane's Risk of Bias 2.0 tool for randomized control trials to evaluate the quality or risk of bias in the included studies. A risk of bias traffic light plot was created using the ROBVIS tool. As summarized in the figure, twelve articles (55%) indicated some concerns, while the remaining ten (45%) demonstrated a low risk of bias. The overall results are depicted in three colors: green for low risk, yellow for some concerns, and red for high risk. Notably, none of the articles (0%) fell into the high-risk category. This information is presented in Fig. 2 . Meta-analysis results Medication adherence (Morisky score) The random-effects model was utilized to synthesize the MD across the studies, considering the variability within and between studies. The analysis included two studies, encompassing 323 observations, with 161 in the experimental and 162 in the control group. The pooled effect size under the random-effects model was MD 0.61 (95%CI: 0.37 to 0.85; p < 0.0001, I² = 0.0%), indicating a statistically significant improvement in medication adherence measured by the Morisky score in the experimental group compared to the control group (Fig. 3 ). Publication Bias Due to the limited number of studies (k = 2), it was impossible to assess publication bias using Egger's test or funnel plot. Subgroup and Sensitivity Analysis Subgroup analysis was deemed inappropriate given the small number of studies and the low heterogeneity (I² = 0.0%). Similarly, sensitivity analyses were not conducted, including leave-one-out analyses and analyses excluding potential outliers. The minimal number of studies and the absence of heterogeneity rendered these analyses impractical and unlikely to provide additional insights. Diastolic Blood Pressure The random-effects model was utilized to synthesize the MD across ten studies, considering the variability within and between studies. The analysis included 2269 observations, with 1127 in the experimental group and 1142 in the control group. The pooled effect size under the random-effects model was − 2.66 (95% CI: -4.62 to -0.70, I² = 85%, p = 0.007), indicating a statistically significant reduction in diastolic blood pressure (DBP) in the experimental group compared to the control group. The prediction interval ranged from − 9.72 to 4.39, reflecting the potential variability in true effect sizes across different settings (Fig. 3 ). Publication Bias The funnel plot appeared asymmetric, suggesting potential publication bias or other small-study effects. To further investigate, Egger's test for funnel plot asymmetry was conducted. The test result was not significant (t = -0.34, df = 8, p = 0.74), indicating no evidence of publication bias according to this test. The discrepancies between the funnel plot and Egger's test results might be explained by factors other than publication bias, such as true heterogeneity or differences in study quality (Fig. 4 ). Subgroup and Sensitivity Analysis Subgroup analyses were performed to explore potential sources of heterogeneity. Significant variability in the effectiveness of interventions was observed across different subgroups. The difference was insignificant for the "Risk of Bias” (p = 0.77) effect. Country-wise, effect sizes varied widely, with Australia showing a substantial reduction in effect size ( MD -2.31) and significant between-group differences (p < 0.0001). Similarly, the SMS type and frequency demonstrated variability, with significant differences in effect sizes between subgroups (p < 0.0001) being the two messages per week (MD -7.05) and reminder type of message (MD − 6.68) subgroups. Such subgroups showed a major effect. Notably, the source of the message (automated system versus nurse-led) also influenced the results (p < 0.0001), with nurse-led interventions showing a more substantial effect (MD -7.05). Sensitivity analyses, including leave-one-out analysis, did not identify any specific study influencing the overall results, showing the robustness of the findings (Supplementary Table 7–8). Systolic blood pressure The random-effects model was utilized to synthesize the SMD across eleven studies, considering the variability within and between studies. The analysis included 3183 observations, with 1584 in the experimental group and 1599 in the control group. The pooled effect size under the random-effects model was − 6.11 (95% CI: -10.25 to -1.97, I² = 96%, p = 0.003), indicating a statistically significant reduction in systolic blood pressure (SBP) in the experimental group compared to the control group. The prediction interval ranged from − 22.00 to 9.77, reflecting the potential variability in true effect sizes across different settings (Fig. 3 ). Publication Bias The funnel plot appeared asymmetric, suggesting potential publication bias or other small-study effects. To further investigate, Egger's test for funnel plot asymmetry was conducted. The test result was not significant (t = -1.08, df = 9, p = 0.31), indicating no evidence of publication bias according to this test. The discrepancies between the funnel plot and Egger's test results might be explained by factors other than publication bias, such as true heterogeneity or differences in study quality (Fig. 4 ). Subgroup and Sensitivity Analysis Subgroup analyses were performed to explore potential sources of heterogeneity. Significant variability in the effectiveness of interventions was observed across different subgroups. The country subgroup analysis revealed substantial negative effect sizes with a marked reduction effect size on Russia (MD -21.6), with significant between-group differences (p < 0.0001). Additionally, the frequency of the SMS subgroup showed notable effect sizes for "Daily" (MD -21.60), with significant differences (p < 0.0001). The source of the message also influenced the results, with nurse-led interventions (MD -13.16) showing a significant effect (p < 0.0001). The reminders and education type of messages showed a reduction in SBP (MD -12.04) with significant effect differences between groups (p < 0.0001). Sensitivity analyses, including leave-one-out analysis, did identify Kilselev A, 2012 influencing the overall outcomes. The pooled effect size after excluding this study remained robust, with a MD -4.49 (95% CI: -7.42 to -1.55, p = 0.002, I2 = 87.4%) (Supplementary Table 9–10). LDL The random-effects model was utilized to synthesize the SMD across four studies, considering the variability within and between studies. The analysis included 2503 observations, with 1253 in the experimental group and 1250 in the control group. The pooled effect size under the random-effects model was − 1.85 (95% CI: -4.91 to 1.20, I² = 59.4%, p = 0.23), indicating no statistically significant reduction in the outcome of interest in the experimental group compared to the control group. The prediction interval ranged from − 13.83 to 10.12, reflecting the potential variability in true effect sizes across different settings (Fig. 3 ). Publication Bias Due to the limited number of studies (k = 4), it was not possible to assess publication bias using Egger's test or funnel plot. Subgroup and Sensitivity Analysis Subgroup analyses revealed that only the "Type of SMS" subgroup had a significant effect on "Lifestyle text messages" with an MD effect size of -5.00 (95% CI: -9.23 to − 0.76). Other subgroups, including "Risk of Bias," "Country," "Type of Transmission," "Frequency of SMS," and "Who Sends the Message," did not show significant effects, indicating no substantial differences within these categories. Sensitivity analyses, including leave-one-out analysis, identified Chow C, 2015, Klimis H, 2021, and Chow C 2022 as outliers. A previous analysis without the identified studies was not possible due to the lower number of remaining studies (Supplementary Table 11–12). HDL The random-effects model was utilized to synthesize the SMD across three studies, considering the variability within and between studies. The analysis included 1045 observations, with 518 in the experimental group and 527 in the control group. The pooled effect size under the random-effects model was − 1.19 (95% CI: --2.50 to 0.11, I² = 0.0%, p = 0.07). The prediction interval ranged from − 9.68 to 7.29, reflecting the potential variability in true effect sizes across different settings (Fig. 3 ). Publication Bias Due to the limited number of studies (k = 3), it was not possible to conduct Egger's test and funnel plot to further assess publication bias. Subgroup and Sensitivity Analysis Subgroup and sensitivity analyses were not conducted due to the low number of studies and low heterogeneity (I² = 0.0%) Total cholesterol The random-effects model was utilized to synthesize the SMD across five studies, considering the variability within and between studies. The analysis included 2715 observations, with 1358 in the experimental group and 1357 in the control group. The pooled effect size under the random-effects model was − 4.33 (95% CI: -10.20 to 1.53, I² = 90%, p = 0.14). The prediction interval ranged from − 24.94 to 16.27, reflecting the potential variability in true effect sizes across different settings ( Fig. 3 ). Publication Bias Due to the limited number of studies (k = 5), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig. 4 ). Subgroup and Sensitivity Analysis The subgroups analysis, including ‘’Type of SMS", "Risk of Bias," "Country," "Type of Transmission," "Frequency of SMS," and "Who Sends the Message," did not show significant effects, indicating no substantial differences within these categories. Sensitivity analyses, including leave-one-out analysis, identified Klimis H, 2021, as an influential study. Excluding this study led to a revised analysis with four remaining studies involving 2469 observations (1234 in the experimental group and 1235 in the control group). The pooled effect size under the random-effects model was MD -7.06 (95% CI: -10.61 to -3.51, I² = 0%, p < 0.0001), indicating a statistically significant reduction in total cholesterol in the experimental group compared to the control group. (Supplementary Table 13–14). BMI The random-effects model was utilized to synthesize the SMD across five studies, considering the variability within and between studies. The analysis included 2715 observations, with 1358 in the experimental group and 1357 in the control group. The pooled effect size under the random-effects model was MD -0.17 (95% CI: -1.12 to 0.77, I² = 95%, p = 0.71). The prediction interval ranged from − 3.65 to 3.30, reflecting the potential variability in true effect sizes across different settings (Fig. 3 ). Publication Bias Due to the limited number of studies (k = 5), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig. 4 ). Subgroup and Sensitivity Analysis Subgroup analyses revealed mixed outcomes. For the "Risk of Bias" subgroup, the effect size for studies with some risk of bias was MD 2.20, indicating an increase and significant effect, significatively different between groups (p = 0.004). Similarly, in the "Country" subgroup, studies from New Zealand had an increase and significant effect size. For the "Type of SMS" subgroup, "Comprehensive cardiac rehab" showed an increase and significant effect size of MD 2.20, while "Lifestyle text messages" showed a significant reduction effect size of MD -1.30. None of the frequency categories or sender subgroups showed a statistically significant difference. Sensitivity analyses, including leave-one-out analysis, identified Chow C, 2015, as an influential study. Excluding this study led to a revised analysis with four remaining studies involving 2005 observations (1006 in the experimental group and 999 in the control group). The pooled effect size under the random-effects model was 0.17 (95% CI: -0.14 to 0.49, I² = 88.2%, p = 0.28), indicating no significant change in BMI. The test of heterogeneity was significant (Q ( 3 ) = 25.34, p < 0.0001), suggesting substantial variability between the study results even after excluding the influential study (Supplementary Table 15–16). HbA1c The random-effects model was utilized to synthesize the SMD across two studies, considering the variability within and between studies. The analysis included 589 observations, with 293 in the experimental group and 296 in the control group. The pooled effect size under the random-effects model was MD 0.12 (95% CI: -0.04 to 0.30, I² = 78%, p = 0.14), indicating no statistically significant change in HbA1c levels in the experimental group compared to the control group (Fig. 3 ). Publication Bias Due to the limited number of studies (k = 2), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig. 4 ). Subgroup and Sensitivity Analysis Subgroup and sensitivity analyses were not performed due to the small number of studies and high heterogeneity (I² = 99.1%). These limitations preclude reliable subgroup differentiation and sensitivity assessment. Discussion In light of the growing interest in digital interventions, which offer easy access and cost-effective solutions for health promotion, our systematic review and meta-analysis, which synthesizes data from 22 articles, aimed to measure the efficacy of text messaging as a tool for CVD risk factors control. As digital interventions become increasingly prevalent, they provide a promising approach to delivering health behavior change at a low cost and broad reach. By focusing on outcomes such as smoking cessation, HbA1c levels, diastolic and systolic blood pressure, cholesterol levels, and medication adherence, we have sought to address the gap in the literature concerning the impact of text messaging on CVD risk factors. While the selected publications had varied results, our systematic analysis saw a positive effect on cardiovascular risk factors control, using text messaging interventions compared to no intervention. Of the selected publications (61%) saw a positive effect on cardiovascular risk factor control using text messaging interventions, while the remaining (39%) saw no significant difference. Most of the selected articles in our review demonstrated a low risk of bias, with 55% of studies meeting this criterion while 45% exhibiting some concern regarding the risk of bias. The clinical inference drawn from these research findings suggests that integrating text messaging interventions into cardiovascular risk factors control and management could offer tangible benefits for patients. With most publications demonstrating a positive effect, healthcare providers may consider incorporating such interventions to complement existing treatments. However, it's essential for clinicians to critically evaluate the quality of evidence, considering the risk of bias in some studies. Therefore, while integrating text messaging could enhance cardiovascular risk factors control strategies, clinicians should exercise caution when applying these findings and remain vigilant for emerging evidence to ensure the interventions are used effectively and appropriately. The findings from our analysis highlight the significant impact of text messaging interventions on various cardiovascular risk factors control outcomes. Firstly, the random-effects model revealed a substantial and statistically significant reduction in SBP with an MD pooled effect size of -6.11 (95% CI: -10.25 to -1.93, p = 0.003, ² = 96% ). This indicates that text messaging interventions are effective in lowering SBP. In addition, the analysis of ten studies related to DBP revealed an MD pooled effect size of -2.66 (95% CI: -4.62 to -0.70, p = 0.007, I² = 85%), signifying a significant reduction in DBP due to text messaging interventions. However, the high heterogeneity indicates variability in the effect across different studies, highlighting the need to consider context and individual study characteristics carefully. For example, the subgroups for SBD and DBP showed that most frequent text messages, reminders and educational messages, and nurse-led messages have a bigger impact on reducing the variable. We need to point out that the risk of CVD increased steadily with progressively higher levels of baseline SBP and DBP, above a usual SBP and DBP of 115 and 75 mmHg, respectively( 32 ). According to the guidelines outlined in the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure( 33 ), it is apparent that managing both systolic and diastolic blood pressure is essential for decreasing the likelihood of cardiovascular events. The report underscores the importance of integrating text messages into CVD risk factors control by addressing diastolic blood pressure to reduce the occurrence of cardiovascular diseases such as coronary artery disease, heart failure, stroke, and peripheral vascular disease. In comparison to our study, a meta-analysis of six studies( 10 ) reported a significant reduction in DBP with an MD of -6.11 mm Hg (p < 0.01) with text messaging intervention, which aligns with our findings. This study also found no significant reduction in SBP with text messaging, contrasting with our results, where we found a statistically significant result; this difference is attributed to the lower sample size that the mentioned study has in the included articles they have and the most extensive ample size and range of studies we are analyzing. The variability in DBP outcomes across studies, reflected in the high heterogeneity, underscores the need for context-specific considerations when interpreting these results, as DBP reduction could be clinically not significant, while SBP reduction may be considerable. These results are important when compared with other interventions, such as the Dietary Approaches to Stop Hypertension (DASH) diet or sodium intake reduction, which, in previous guidelines such as American Heart Association (AHA)( 34 , 35 ) has been pointed out that can reduce 11 mmgHG and 5 to 6 mmHg respectively, making text messages a powerful tool to help to manage SBP as a cardiovascular risk factor. Regarding medication adherence, we demonstrated an MD pooled effect size of 0.61 (95%CI: 0.37 to 0.85; p < 0.0001, I² = 0.0%). This significant positive effect indicates that text messaging interventions notably improve medication adherence, as measured by the Morisky score. The low heterogeneity suggests that the impact on medication adherence is relatively consistent across the included studies. This finding is consistent with a previous study that reported a moderate effect size (SMD = 0.65, p = 0.01) in older adults with hypertension, suggesting that text messaging effectively improves adherence across different populations, but in our analysis, we have demonstrated that this effect is maintained trough our studies with a heterogeneity of I2 = 0% compared to the last analysis that showed a heterogeneity of I2 = 85% that raised concerns about the effect ( 10 ). This reveals that while text messaging interventions generally show positive effects on SBP, DBP, and medication adherence, the magnitude and consistency of these effects can vary, this can be due to several facts, as we demonstrated in our subgroups analysis of different variables being different according to the content of the message, the timing of the message, and the sender of the message. The variability in outcomes across studies emphasizes the need for personalized interventions for specific populations and settings and calls for further research to refine and validate this digital tool for more consistent and effective results. The findings from our meta-analysis indicate no significant improvement in body mass index (BMI), LDL cholesterol, HDL cholesterol, total cholesterol, or HBA1C levels due to text messaging interventions. This contrasts with some previous research which reported significant changes in these metrics. For instance, a previously reported meta-analysis( 36 ) found that text messaging interventions had a positive impact on HbA1c levels, resulting in significant reductions in HbA1C MD -0.38 (95%CI − 0.53 to − 0.23, p-value < 0.001). Another meta-analysis( 37 ) reported significant improvement in cholesterol levels (SMD= -0.26; 95% CI -0.4 to -0.12; P = < 0.01), we need to point out that despite our initial analysis revealed no significant reduction, in our sensitivity analysis after excluding Klimis H, 2021, as an influential study we find a similar effect with a MD -7.06 (95% CI: -10.61 to -3.51, I² = 0%, p < 0.0001). Another meta-analysis( 38 ) observed a moderate and statistically significant effect on reducing BMI (− 0.43 kg/m², 95% CI: -0.63 to -0.23, I 2 62%, p = 0.001), this effective intervention could be attached to the inclusion of studies that add mobile health interventions and not only text messages as intervention. While another meta-analysis( 39 ) more focused on only text messages found no statistically significant reduction in BMI (SMD = -3.61; 95% CI = -9.48 to 2.26; P = 0.23). Additionally, this study found the change in HDL cholesterol and LDL cholesterol levels were statistically non-significant LDL (SMD = -1.81 to 95% CI = -4.80 to 1.18; p = 0.24), HDL (SMD = -1.15 to 95% CI = -2.83 to 0.54; P = .18). This suggests that while text messaging interventions can improve certain cardiovascular outcomes, such as blood pressure and medication adherence, their effects on weight management, HBA1C, and lipid profiles might be less pronounced. In general, our analysis is consistent with previous findings of LDL, HDL, and cholesterol levels, while contrast for HbA1c and BMI; this could be due to the differences of the included studies as we only include text messages as intervention and not all the variety of mobile health interventions, being a pure meta-analysis that measures the effect of text message alone on this variables. Other previous studies have shown significant weight, BMI, waist circumference, and HBA1C level reductions due to text messaging interventions, though changes in lipid levels were not consistently significant. These discrepancies emphasize the need for tailored digital interventions for individual risk profiles and study-specific contexts. Future research should aim to integrate a comprehensive set of outcome measures to better evaluate the impact of text messaging interventions on cardiovascular risk factors control. Our analysis demonstrated that text messaging interventions significantly reduced SBP and DBP, crucial cardiovascular health indicators. In addition to improving blood pressure, our study found a significant positive effect on medication adherence. This suggests that text messaging interventions notably enhance adherence to prescribed medication regimens, which aligns with previous research highlighting the role of digital tools in improving patient compliance. Our results indicate that text messaging interventions can improve CVD risk factors. For this reason, physicians should consider incorporating text messaging into patient management and customizing messages to meet individual needs and health goals. This approach offers continuous support, helps patients stay engaged, and allows for progress monitoring and timely treatment adjustments. However, physicians must critically assess the evidence, accounting for variability and potential biases. Limitations and future research Acknowledging the limitations of our study is crucial for contextualizing our findings. The high heterogeneity observed in some outcomes, such as DBP and SBP, warrants cautious interpretation and the need for context-specific considerations when interpreting these results. Additionally, the methodological quality of the four included studies had some concerns about the risk of bias in the randomization process. In comparison, twelve studies had some concerns about selecting the reported result. Also, there is no possible way to differentiate what is the medications the patients were taking, as this was not reported in the pooled studies. Furthermore, the limited number of studies made performing subgroup and sensitivity analysis on some parameters impossible, precluding reliable subgroup differentiation and sensitivity assessment. Also, the sample sizes of some RCTs included were small (seven of the studies had 100 or fewer participants). Smoking outcomes were found in 4 studies. Chow C (2015) and Kiselev A. (2012) found a significant smoking reduction in the intervention group in Australia and Russia. In contrast, from Australia, Santo K (2018) and Chow C (2022) have not found significant differences between interventions and the control group. The limited number of studies underscores the need for additional research and reports to validate our findings, explore potential moderators of intervention effectiveness, and address methodological challenges. For instance, smoking as an impact CVD risk factor showed outcomes only in 4 studies. Future research should aim to evaluate more with nurse text messages and obtain more information on lifestyle changes, including physical activity, smoking cessation, healthier diets, body mass index reduction, and patient medications. This will allow us to evaluate the impact of new technologies in reducing other risk factors associated with cardiovascular disease. Moreover, no studies assessed the effects of text messaging beyond the 12-month follow-up. Studies with follow-ups longer than 12 months could help understand the long-term effect that text messaging has on preventing cardiovascular disease risk factors. Conclusion This systematic review and meta-analysis demonstrate that text messaging interventions can significantly improve cardiovascular risk factors control by medication adherence and reduce systolic and diastolic blood pressure, which is critical for cardiovascular health, indicating its potential as an effective tool in CVD prevention. Despite these positive outcomes, the high heterogeneity observed in blood pressure measures and the lack of significant effects on other cardiovascular risk factors, such as BMI, LDL, HDL, total cholesterol, and HbA1c, underscore the variability in intervention effectiveness across different settings and populations. This suggests that while text messaging can enhance certain health behaviors, its impact on broader cardiovascular risk factors control metrics might be limited. Nevertheless, integrating text messaging interventions into existing healthcare strategies offers a promising, cost-effective approach to improving medication adherence and blood pressure control, vital for preventing cardiovascular events. Future research should focus on long-term effects, comprehensive lifestyle changes, and individualizing interventions to enhance their efficacy across diverse populations. Addressing these gaps will be crucial for refining digital health technologies and optimizing their role in CVD prevention. Abbreviations CVDs Cardiovascular Diseases PRISMA Preferred Reporting Items for Systematic Review and Meta-Analysis RCTs Randomized Clinical Trials DBP Diastolic Blood Pressure SBP Systolic Blood Pressure BMI Body Mass Index LDL Low-Density Lipoprotein HDL High-Density Lipoprotein HbA1c Hemoglobin A1c MeSH Medical Subject Headings CI Confidence Interval SMD Standardized Mean Difference k Number of Studies Declarations Ethics Approval and Consent to Participate This study did not require ethical approval in accordance with local guidelines, as it is a meta-analysis of previously published data. No new individual patient data were collected or analyzed; therefore, patient consent was not necessary for this research. All data utilized in this analysis were sourced from studies that had obtained the requisite ethical approvals and informed consent from participants. Consent for publication The authors consent for publication of all tables, figures and contents that were generated through author’s original analysis of data included in meta-analysis. Availability of data and materials All data generated or analyzed during this study are included in this published article Competing Interests The authors have no conflict of interest or competing interests to declare. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Ernesto Calderon Martinez : Writing – review & editing, Writing – original draft, Visualization, Project administration, Conceptualization, Formal analysis. Stephin Zachariah Saji : Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing. Jonathan Victor Salazar Ore : Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing. Ajay Kumar : Data curation, Writing – review & editing, Investigation, Validation. Sutirtha Mohanty : Data curation, Writing – review & editing, Investigation. Viridiana Yumiko Nakamura Ramírez : Methodology, Writing – review & editing, Investigation . Ahmad Hammoud : Methodology, Writing – review & editing, Investigation, Validation Leen Nasser Shaban : Writing – review & editing, Investigation. Vaidarshi Abbagoni : Writing – review & editing, Investigation, Validation . Camila Sanchez Cruz : Writing – review & editing, Investigation. Acknowledgements Not applicable References Cardiovascular. diseases [Internet]. [cited 2024 Mar 17]. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 Pietrzak E, Cotea C, Pullman S. Primary and secondary prevention of cardiovascular disease: Is there a place for internet-based interventions? 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Heart Surg Forum [Internet]. 2020 Jan 23 [cited 2024 Sep 1];23(1):E18–24. https://pubmed.ncbi.nlm.nih.gov/32118537/ Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.IncludedStudies.xlsx Table 1. Included Studies in Systematic Review: Intervention Outcomes and key points Caption: MI - Myocardial Infarction, SMS- Short Message Service, SBP -Systolic Blood Pressure, DBP – Diastolic Blood Pressure, LDL-C – Low-Density Lipoprotein- cholesterol, BMI- Body Mass Index, CVRF- Cardiovascular Risk Factor, BP- Blood Pressure, mHealth- mobile health, VO2 – maximal oxygen consumption, CR- Cardiac Rehabilitation SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 02 Dec, 2024 Reviewers agreed at journal 01 Dec, 2024 Reviews received at journal 30 Nov, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviews received at journal 14 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers invited by journal 28 Oct, 2024 Editor assigned by journal 16 Oct, 2024 Editor invited by journal 30 Sep, 2024 Submission checks completed at journal 25 Sep, 2024 First submitted to journal 25 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5112776","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":382651882,"identity":"1809a011-8893-413b-9371-4bf016e16abe","order_by":0,"name":"Ernesto Calderon Martinez","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":true,"prefix":"","firstName":"Ernesto","middleName":"Calderon","lastName":"Martinez","suffix":""},{"id":382651883,"identity":"0f5797b7-68fc-4eef-aba4-95a32d504d50","order_by":1,"name":"Stephin Zachariah Saji","email":"","orcid":"","institution":"Our Lady of Fatima University","correspondingAuthor":false,"prefix":"","firstName":"Stephin","middleName":"Zachariah","lastName":"Saji","suffix":""},{"id":382651884,"identity":"a2bcfea8-615b-452c-88e9-0ec853bfce14","order_by":2,"name":"Jonathan Victor Salazar Ore","email":"","orcid":"","institution":"Universidad de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"Victor Salazar","lastName":"Ore","suffix":""},{"id":382651885,"identity":"9529c110-fddc-48cc-aed0-be7c62faa91f","order_by":3,"name":"Ajay Kumar","email":"","orcid":"","institution":"Isra University Faculty of Medicine and Allied Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"","lastName":"Kumar","suffix":""},{"id":382651886,"identity":"426f6af8-4618-4bc5-b4e3-ee2a4f443706","order_by":4,"name":"Sutirtha Mohanty","email":"","orcid":"","institution":"Kerala University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sutirtha","middleName":"","lastName":"Mohanty","suffix":""},{"id":382651887,"identity":"694feff6-b818-4c52-9ecb-164d160ec9a2","order_by":5,"name":"Viridiana Yumiko Nakamura Ramírez","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"Viridiana","middleName":"Yumiko Nakamura","lastName":"Ramírez","suffix":""},{"id":382651888,"identity":"2017a949-e9f6-4233-a4b8-c107413d8888","order_by":6,"name":"Ahmad Hammoud","email":"","orcid":"","institution":"Ilia State University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Hammoud","suffix":""},{"id":382651889,"identity":"5c611a03-657f-4796-99c3-cb2e57f6e2f9","order_by":7,"name":"Leen Nasser Shaban","email":"","orcid":"","institution":"Ilia State University","correspondingAuthor":false,"prefix":"","firstName":"Leen","middleName":"Nasser","lastName":"Shaban","suffix":""},{"id":382651890,"identity":"710f9541-dbee-4060-b63d-cf04e8af8a65","order_by":8,"name":"Vaidarshi Abbagoni","email":"","orcid":"","institution":"St. Vincent Medical Center, Frank H. Netter Quinnipiac University","correspondingAuthor":false,"prefix":"","firstName":"Vaidarshi","middleName":"","lastName":"Abbagoni","suffix":""},{"id":382651891,"identity":"96a610ca-dff5-4a57-888e-d73c5f77cf26","order_by":9,"name":"Camila Sanchez Cruz","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"Camila","middleName":"Sanchez","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2024-09-18 23:42:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5112776/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5112776/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21818-0","type":"published","date":"2025-04-04T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71564497,"identity":"d3b319d7-5893-40fb-98c6-9bddfaa653b3","added_by":"auto","created_at":"2024-12-16 17:23:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow Diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption:\u003c/strong\u003e \u003cem\u003e\u0026nbsp;\u003c/em\u003ePrisma Flow Diagram delineates the systematic process of identifying and screening studies across multiple databases, culminating in selecting 22 studies.\u003c/p\u003e","description":"","filename":"Fig1.PRISMAFlowDiagram.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/0dba196b3e1536592c95e0a5.jpg"},{"id":71563158,"identity":"591e5d98-156d-4808-810d-e50841ccb8fa","added_by":"auto","created_at":"2024-12-16 17:15:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk of Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption:\u003c/strong\u003e \u0026nbsp;Light Plot showing twelve articles (55%) indicated some concerns, while the remaining ten articles (45%) demonstrated a low risk of bias.\u003c/p\u003e","description":"","filename":"Fig2.RiskofBias.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/48a8cea37289e1701161ab5b.jpg"},{"id":71563160,"identity":"ebe045fa-bd96-4cef-9022-a286c0bdd85d","added_by":"auto","created_at":"2024-12-16 17:15:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":291320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-Analysis Forest Plots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption:\u003c/strong\u003e Forest plot of studies showing 95% CI and overall effect size for: (A) Medication adherence (Morisky score), (B) Diastolic blood pressure (DBP), (C) Systolic blood pressure (SBP), (D) LDL levels, (E) HDL levels, (F) Total cholesterol, (G) BMI, and (H) HbA1c.\u003c/p\u003e","description":"","filename":"Fig3.MetaAnalysisForestPlots.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/331307cbb7732ca6aea5fe35.jpg"},{"id":71563157,"identity":"c291dba7-d379-411c-b2c9-8f8d88fece11","added_by":"auto","created_at":"2024-12-16 17:15:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunnel Plots for Publication Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption:\u003c/strong\u003e Funnel plot analysis for assessing publication bias in: (A) Diastolic blood pressure (DBP), (B) Systolic blood pressure (SBP).\u003c/p\u003e","description":"","filename":"Fig4.FunnelPlots.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/58be86d7ed360d928aa21c56.jpg"},{"id":80082069,"identity":"dc93633a-bfbf-48b3-aaf8-4a253754d4e4","added_by":"auto","created_at":"2025-04-07 16:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2247896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/274fba4a-c0a9-4067-bcdf-10f855b6438c.pdf"},{"id":71564492,"identity":"38ed404d-6468-4e38-8713-6dca1503f8cf","added_by":"auto","created_at":"2024-12-16 17:23:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. Included Studies in Systematic Review: Intervention Outcomes and key points\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption: \u003c/strong\u003eMI - Myocardial Infarction, SMS- Short Message Service, SBP -Systolic Blood Pressure, DBP – Diastolic Blood Pressure, LDL-C – Low-Density Lipoprotein- cholesterol, BMI- Body Mass Index, CVRF- Cardiovascular Risk Factor, BP- Blood Pressure, mHealth- mobile health, VO2 – maximal oxygen consumption, CR- Cardiac Rehabilitation\u003c/p\u003e","description":"","filename":"Table1.IncludedStudies.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/2b28544765159976b67eee2e.xlsx"},{"id":71564493,"identity":"31cbaa74-9a28-4caf-99b4-291ccbc592a8","added_by":"auto","created_at":"2024-12-16 17:23:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":53915,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5112776/v1/a68d75224735e7a299ddfe68.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Text messages as a tool to improve cardiovascular disease risk factors control: A Systematic Review and Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) remain the leading cause of global mortality, claiming 17.9\u0026nbsp;million lives annually(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). They encompass a spectrum of heart and blood vessel disorders, with heart attacks and strokes alone responsible for over 80% of these deaths occurring prematurely in individuals under 70(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Major behavioral risk factors contributing to CVDs include an unhealthy diet, physical inactivity, tobacco use, and excessive alcohol consumption. These behaviors have been related to elevated blood pressure, glucose levels, lipids, and obesity, all of which significantly increase susceptibility to heart attack, stroke, and related complications(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCVD incidence is growing among younger adults(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This could be explained by the rising rates of uncontrolled CVD risk factors, such as obesity, smoking, exercise, and medication adherence, among others(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, there is a need for novel strategies to control CVD risk factors.\u003c/p\u003e \u003cp\u003eRecent research has focused on using text messages for health purposes, which send information in near-real time to thousands of people as recipients of standardized, bulk messages or personalized or tailored messages(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Text Messaging intervention strategy has been found to work in risk factor control and progression of diseases, including diabetes, resulting in a low-cost and effective tool for subjects with impaired glucose(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In the CVD context, although drug interventions are cost-effective in managing and reducing the risk of recurrent cardiovascular events, medication adherence remains suboptimal. As a scalable and cost-effective tool, mobile phone text messaging presents an opportunity to convey health information, deliver electronic reminders, and encourage behavior change(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Text messaging intervention could successfully improve individual risk factors and promote healthy habits. Previous studies have proven the efficacy of controlling these risk factors with variability among the results among the different risk factors (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite there are previous systematic reviews that address text messages and other ways of mobile health (health), it is mandatory to include a quantitative study to measure the potentially significant effect of text messages as a promising tool to control CVD risk factors, to correctly measure the effect that this intervention has into the risk factors and identify the most effective way to use and reach a consensus of it is used.\u003c/p\u003e \u003cp\u003eThis systematic review and meta-analysis aimed to evaluate the efficacy of text messaging as a strategy to control CVD risk factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present study employed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) guidelines to ensure a comprehensive and systematic approach to our review. The protocol for this review has been registered on PROSPERO databases under the following available ID: CRD42024529187.\u003c/p\u003e\u003cp\u003e \u003cb\u003eCRITERIA FOR CONSIDERING STUDIES IN THIS REVIEW\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eTYPES OF PARTICIPANTS\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThis study has set specific participant selection criteria, including all participants over 18 years. Exclusion criteria are studies assessing multiple modalities and studies involving teenagers and child populations.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTYPES OF INTERVENTION\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe intervention to be included in this analysis should be a text message focusing on controlling cardiovascular disease risk factors in the patients via text messaging service. The control group doesn't use the text message service. Studies assessing other modalities (websites, email, apps, video, web portal, phone calls) without a specific group of text messages and control group were excluded.\u003c/p\u003e\u003cp\u003e \u003cb\u003eOUTCOMES\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe outcomes to be measured include studies that report the effectiveness of texting messages for cardiovascular risk factor control and exclude studies that do not report relevant outcomes related to this topic. The effectiveness will be measured with mean difference (MD); in cases where these values are not reported, they will be calculated manually using the information provided by the author. This review will specifically focus on the impact of text messages on improving CVD-related factors, such as smoking cessation, HbA1c levels, diastolic blood pressure (DBP), systolic blood pressure (BP), cholesterol levels, LDL levels, HDL levels, medication adherence, and exercise.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTYPES OF STUDY\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFor our research, we systematically reviewed relevant studies published from 2000 to 2023, available in English; we meticulously screened and analyzed randomized clinical trials (RCTs) to reduce bias and prove cause-and-effect relations between interventions and results. We excluded case series, cohort, case-control trials, cross-sectional, dissertations, book chapters, protocol articles, reviews, news articles, conference abstracts, letters to the editor, editorials, comment publications, systematic reviews, and meta-analyses. Furthermore, we excluded studies that did not clearly describe their operationalizations, were duplicated, and could not obtain the necessary data or receive a response from the original author via email.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSearching methods\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe perform our search on PubMed, MEDLINE, Cochrane, Scopus, Web of Science, Embase, and CINAHL, using Medical Subject Headings (MeSH) terms and free text terms such as: ‘’cardiovascular disease’’, ‘’ Text messaging’’, ‘’Prevention’’, ‘’Cellular phone’’, ‘’Risk reduction behavior’’ on 18/03/2024. The specific search per database can be found in the supplementary material (Supplementary Table\u0026nbsp;1–6). We adhered to a PRISMA(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) flowchart to guide the systematic review article selection process, resulting in a uniform dataset and enhancing the accuracy and reliability of our findings.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSelection of studies\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFollowing an initial screening based on the title and abstract, two reviewers (AK, SM) independently selected trials for inclusion in this review using predetermined inclusion and exclusion criteria. This search was performed using Rayyan(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) to extract relevant data, and duplicates were filtered. Keywords were employed to highlight inclusion and exclusion criteria-related words on Rayyan (Supplementary material). Consensus and consultation with a third reviewer (JVSO) resolved any disagreement about including the studies. Subsequently, a full-text analysis was conducted with two reviewers (LS, VA) independently selected trials for inclusion in the review using predetermined inclusion and exclusion criteria. Consensus and consultation with a third review author (SZS) resolved disagreements about including studies.\u003c/p\u003e\u003cp\u003e \u003cb\u003eAssessment of risk of bias in included studies\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe evaluated the data using the criteria outlined in the Cochrane Handbook (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We applied the Cochrane RoB 2.0 tool(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) for randomized controlled trials (RCTs) to assess the quality of studies included in the systematic review. Two independent reviewers (AK and JVSO) evaluated the risk of bias in each study, considering the specific criteria and guidelines provided by the respective tools. Any reviewer discrepancies have been resolved through discussion with a third, blinded reviewer (ECM).\u003c/p\u003e\u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe will only perform a meta-analysis when two or more studies report an outcome according to the Cochrane Handbook using R Software version 3.6.0 to calculate the effect size(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Effect sizes were presented as mean differences with 95% confidence intervals (CI). The random-effects model was used for pooling analysis to compensate for the heterogeneity of study statistics. In this regard, I2 ≥ 50% and ≥ 75%(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) indicated substantial and considerable heterogeneity, study removal method to the sub-analysis to assess whether any individual study exerted particular influence on the overall effect size, p‐values \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAcross the database, we identified a total of 6142 possible articles. After a thorough examination, 17 duplicate articles were removed before screening. 6125 articles were reviewed during the screening process, and 6084 were excluded. From the remaining, 41 articles were sought for retrieval, but four reports were not retrieved. Of the 37 reports assessed for eligibility, 15 were excluded due to the wrong study design. Ultimately, 22 studies were included in the final review process. This process is summarized in the PRISMA Flow Chart Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStudy characteristics\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe study characteristics encompass a comprehensive evaluation of text messaging interventions as a tool for cardiovascular risk factor control, including cholesterol levels, medication adherence, physical activity, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking, and HbA1c. The research was conducted across a diverse range of countries such as the United Kingdom (12%), Canada (5%), Pakistan (5%), Iran (10%), Australia (22%), China (10%), New Zealand (10%), Nepal (5%), Italy (5%), the United States (10%), Brazil (5%), and Turkey (5%), indicating a broad geographic representation. Each study utilized different types of SMS interventions, ranging from one-way to two-way messaging, with varying frequencies (daily, weekly, etc.) and durations (ranging from weeks to a year). These interventions were generally automated and targeted at improving lifestyle behaviors, enhancing medication adherence, or providing motivational and educational support.\u003c/p\u003e\u003cp\u003eMost studies (61%) reported a positive impact of these interventions on cardiovascular risk factors control, with significant improvements in medication adherence (34% of studies), increased physical activity (28% of studies), and notable reductions in SBP and DBP (51% of studies). Studies reported varied outcomes, often focusing on blood pressure, physical activity, and medication adherence. Regarding SBP, Tam H (2023) in China and Bhandari B (2022) in Nepal observed reductions, while Bobrow K (2014) in South Africa found only a small reduction in SBP control. Chow C (2015) reported significant SBP, LDL-C levels, and BMI reductions in Australia. However, Kes D (2021) in Turkey observed lower BP levels, while Golshahi J (2015) in Iran and Chow C (2022) in Australia reported no significant effects on BP or lifestyle changes. Medication adherence was a key outcome in multiple studies. Kamal A (2015) in Pakistan, Khonsari S (2014) in Malaysia, Dale L (2015b) in New Zealand, and Kes D (2021) in Turkey all reported improved adherence. In contrast, studies by Chow C (2022) in Australia and Santo K (2018) found no significant difference in adherence.\u003c/p\u003e\u003cp\u003ePhysical activity improvements were noted by Dale L. (2015a) in New Zealand, Foccardi G. (2021) in Italy, and Klimis H (2021) in Australia, with sustained fitness gains observed by Duscha B. (2018) in the USA. Song Y (2019) reported that exercise tolerance in China has improved. Other notable outcomes include improved medication self-efficacy, as found by Park L (2014) in the USA, while Wald D (2014) in the UK highlighted enhanced medication adherence in patients on cardiovascular treatments. Kiselev A (2012) in Russia found improved health outcomes, whereas Golshahi J (2015) in Iran saw no significant impact on lifestyle changes or hypertension control. Smoking outcomes were found in 4 studies. Chow C (2015) and Kiselev A. (2012) found a significant smoking reduction in the intervention group in Australia and Russia. In contrast, Santo K (2018) and Chow C (2022), both in Australia, have not found significant differences between interventions and the control group. All findings are summarized in Table\u0026nbsp;1 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eRisk of bias assessment\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThis risk of bias assessment utilized Cochrane's Risk of Bias 2.0 tool for randomized control trials to evaluate the quality or risk of bias in the included studies. A risk of bias traffic light plot was created using the ROBVIS tool. As summarized in the figure, twelve articles (55%) indicated some concerns, while the remaining ten (45%) demonstrated a low risk of bias. The overall results are depicted in three colors: green for low risk, yellow for some concerns, and red for high risk. Notably, none of the articles (0%) fell into the high-risk category. This information is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMeta-analysis results\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eMedication adherence (Morisky score)\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the MD across the studies, considering the variability within and between studies. The analysis included two studies, encompassing 323 observations, with 161 in the experimental and 162 in the control group. The pooled effect size under the random-effects model was MD 0.61 (95%CI: 0.37 to 0.85; p \u0026lt; 0.0001, I² = 0.0%), indicating a statistically significant improvement in medication adherence measured by the Morisky score in the experimental group compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 2), it was impossible to assess publication bias using Egger's test or funnel plot.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup analysis was deemed inappropriate given the small number of studies and the low heterogeneity (I² = 0.0%). Similarly, sensitivity analyses were not conducted, including leave-one-out analyses and analyses excluding potential outliers. The minimal number of studies and the absence of heterogeneity rendered these analyses impractical and unlikely to provide additional insights.\u003c/p\u003e\u003cp\u003e \u003cb\u003eDiastolic Blood Pressure\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the MD across ten studies, considering the variability within and between studies. The analysis included 2269 observations, with 1127 in the experimental group and 1142 in the control group. The pooled effect size under the random-effects model was − 2.66 (95% CI: -4.62 to -0.70, I² = 85%, p = 0.007), indicating a statistically significant reduction in diastolic blood pressure (DBP) in the experimental group compared to the control group. The prediction interval ranged from − 9.72 to 4.39, reflecting the potential variability in true effect sizes across different settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe funnel plot appeared asymmetric, suggesting potential publication bias or other small-study effects. To further investigate, Egger's test for funnel plot asymmetry was conducted. The test result was not significant (t = -0.34, df = 8, p = 0.74), indicating no evidence of publication bias according to this test. The discrepancies between the funnel plot and Egger's test results might be explained by factors other than publication bias, such as true heterogeneity or differences in study quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup analyses were performed to explore potential sources of heterogeneity. Significant variability in the effectiveness of interventions was observed across different subgroups. The difference was insignificant for the \"Risk of Bias” (p = 0.77) effect. Country-wise, effect sizes varied widely, with Australia showing a substantial reduction in effect size ( MD -2.31) and significant between-group differences (p \u0026lt; 0.0001). Similarly, the SMS type and frequency demonstrated variability, with significant differences in effect sizes between subgroups (p \u0026lt; 0.0001) being the two messages per week (MD -7.05) and reminder type of message (MD − 6.68) subgroups. Such subgroups showed a major effect. Notably, the source of the message (automated system versus nurse-led) also influenced the results (p \u0026lt; 0.0001), with nurse-led interventions showing a more substantial effect (MD -7.05). Sensitivity analyses, including leave-one-out analysis, did not identify any specific study influencing the overall results, showing the robustness of the findings (Supplementary Table\u0026nbsp;7–8).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSystolic blood pressure\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across eleven studies, considering the variability within and between studies. The analysis included 3183 observations, with 1584 in the experimental group and 1599 in the control group. The pooled effect size under the random-effects model was − 6.11 (95% CI: -10.25 to -1.97, I² = 96%, p = 0.003), indicating a statistically significant reduction in systolic blood pressure (SBP) in the experimental group compared to the control group. The prediction interval ranged from − 22.00 to 9.77, reflecting the potential variability in true effect sizes across different settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe funnel plot appeared asymmetric, suggesting potential publication bias or other small-study effects. To further investigate, Egger's test for funnel plot asymmetry was conducted. The test result was not significant (t = -1.08, df = 9, p = 0.31), indicating no evidence of publication bias according to this test. The discrepancies between the funnel plot and Egger's test results might be explained by factors other than publication bias, such as true heterogeneity or differences in study quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup analyses were performed to explore potential sources of heterogeneity. Significant variability in the effectiveness of interventions was observed across different subgroups. The country subgroup analysis revealed substantial negative effect sizes with a marked reduction effect size on Russia (MD -21.6), with significant between-group differences (p \u0026lt; 0.0001). Additionally, the frequency of the SMS subgroup showed notable effect sizes for \"Daily\" (MD -21.60), with significant differences (p \u0026lt; 0.0001). The source of the message also influenced the results, with nurse-led interventions (MD -13.16) showing a significant effect (p \u0026lt; 0.0001). The reminders and education type of messages showed a reduction in SBP (MD -12.04) with significant effect differences between groups (p \u0026lt; 0.0001). Sensitivity analyses, including leave-one-out analysis, did identify Kilselev A, 2012 influencing the overall outcomes. The pooled effect size after excluding this study remained robust, with a MD -4.49 (95% CI: -7.42 to -1.55, p = 0.002, I2 = 87.4%) (Supplementary Table\u0026nbsp;9–10).\u003c/p\u003e\u003cp\u003e \u003cb\u003eLDL\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across four studies, considering the variability within and between studies. The analysis included 2503 observations, with 1253 in the experimental group and 1250 in the control group. The pooled effect size under the random-effects model was − 1.85 (95% CI: -4.91 to 1.20, I² = 59.4%, p = 0.23), indicating no statistically significant reduction in the outcome of interest in the experimental group compared to the control group. The prediction interval ranged from − 13.83 to 10.12, reflecting the potential variability in true effect sizes across different settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 4), it was not possible to assess publication bias using Egger's test or funnel plot.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup analyses revealed that only the \"Type of SMS\" subgroup had a significant effect on \"Lifestyle text messages\" with an MD effect size of -5.00 (95% CI: -9.23 to − 0.76). Other subgroups, including \"Risk of Bias,\" \"Country,\" \"Type of Transmission,\" \"Frequency of SMS,\" and \"Who Sends the Message,\" did not show significant effects, indicating no substantial differences within these categories. Sensitivity analyses, including leave-one-out analysis, identified Chow C, 2015, Klimis H, 2021, and Chow C 2022 as outliers. A previous analysis without the identified studies was not possible due to the lower number of remaining studies (Supplementary Table\u0026nbsp;11–12).\u003c/p\u003e\u003cp\u003e \u003cb\u003eHDL\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across three studies, considering the variability within and between studies. The analysis included 1045 observations, with 518 in the experimental group and 527 in the control group. The pooled effect size under the random-effects model was − 1.19 (95% CI: --2.50 to 0.11, I² = 0.0%, p = 0.07). The prediction interval ranged from − 9.68 to 7.29, reflecting the potential variability in true effect sizes across different settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 3), it was not possible to conduct Egger's test and funnel plot to further assess publication bias.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup and sensitivity analyses were not conducted due to the low number of studies and low heterogeneity (I² = 0.0%)\u003c/p\u003e\u003cp\u003e \u003cb\u003eTotal cholesterol\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across five studies, considering the variability within and between studies. The analysis included 2715 observations, with 1358 in the experimental group and 1357 in the control group. The pooled effect size under the random-effects model was − 4.33 (95% CI: -10.20 to 1.53, I² = 90%, p = 0.14). The prediction interval ranged from − 24.94 to 16.27, reflecting the potential variability in true effect sizes across different settings \u003cem\u003e(\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 5), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe subgroups analysis, including ‘’Type of SMS\", \"Risk of Bias,\" \"Country,\" \"Type of Transmission,\" \"Frequency of SMS,\" and \"Who Sends the Message,\" did not show significant effects, indicating no substantial differences within these categories. Sensitivity analyses, including leave-one-out analysis, identified Klimis H, 2021, as an influential study. Excluding this study led to a revised analysis with four remaining studies involving 2469 observations (1234 in the experimental group and 1235 in the control group). The pooled effect size under the random-effects model was MD -7.06 (95% CI: -10.61 to -3.51, I² = 0%, p \u0026lt; 0.0001), indicating a statistically significant reduction in total cholesterol in the experimental group compared to the control group. (Supplementary Table\u0026nbsp;13–14).\u003c/p\u003e\u003cp\u003e \u003cb\u003eBMI\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across five studies, considering the variability within and between studies. The analysis included 2715 observations, with 1358 in the experimental group and 1357 in the control group. The pooled effect size under the random-effects model was MD -0.17 (95% CI: -1.12 to 0.77, I² = 95%, p = 0.71). The prediction interval ranged from − 3.65 to 3.30, reflecting the potential variability in true effect sizes across different settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 5), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup analyses revealed mixed outcomes. For the \"Risk of Bias\" subgroup, the effect size for studies with some risk of bias was MD 2.20, indicating an increase and significant effect, significatively different between groups (p = 0.004). Similarly, in the \"Country\" subgroup, studies from New Zealand had an increase and significant effect size. For the \"Type of SMS\" subgroup, \"Comprehensive cardiac rehab\" showed an increase and significant effect size of MD 2.20, while \"Lifestyle text messages\" showed a significant reduction effect size of MD -1.30. None of the frequency categories or sender subgroups showed a statistically significant difference. Sensitivity analyses, including leave-one-out analysis, identified Chow C, 2015, as an influential study. Excluding this study led to a revised analysis with four remaining studies involving 2005 observations (1006 in the experimental group and 999 in the control group). The pooled effect size under the random-effects model was 0.17 (95% CI: -0.14 to 0.49, I² = 88.2%, p = 0.28), indicating no significant change in BMI. The test of heterogeneity was significant (Q (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) = 25.34, p \u0026lt; 0.0001), suggesting substantial variability between the study results even after excluding the influential study (Supplementary Table\u0026nbsp;15–16).\u003c/p\u003e\u003cp\u003e \u003cb\u003eHbA1c\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe random-effects model was utilized to synthesize the SMD across two studies, considering the variability within and between studies. The analysis included 589 observations, with 293 in the experimental group and 296 in the control group. The pooled effect size under the random-effects model was MD 0.12 (95% CI: -0.04 to 0.30, I² = 78%, p = 0.14), indicating no statistically significant change in HbA1c levels in the experimental group compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePublication Bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDue to the limited number of studies (k = 2), it was not possible to conduct Egger's test and funnel plot to further assess publication bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubgroup and Sensitivity Analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSubgroup and sensitivity analyses were not performed due to the small number of studies and high heterogeneity (I² = 99.1%). These limitations preclude reliable subgroup differentiation and sensitivity assessment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn light of the growing interest in digital interventions, which offer easy access and cost-effective solutions for health promotion, our systematic review and meta-analysis, which synthesizes data from 22 articles, aimed to measure the efficacy of text messaging as a tool for CVD risk factors control. As digital interventions become increasingly prevalent, they provide a promising approach to delivering health behavior change at a low cost and broad reach. By focusing on outcomes such as smoking cessation, HbA1c levels, diastolic and systolic blood pressure, cholesterol levels, and medication adherence, we have sought to address the gap in the literature concerning the impact of text messaging on CVD risk factors.\u003c/p\u003e\u003cp\u003eWhile the selected publications had varied results, our systematic analysis saw a positive effect on cardiovascular risk factors control, using text messaging interventions compared to no intervention. Of the selected publications (61%) saw a positive effect on cardiovascular risk factor control using text messaging interventions, while the remaining (39%) saw no significant difference. Most of the selected articles in our review demonstrated a low risk of bias, with 55% of studies meeting this criterion while 45% exhibiting some concern regarding the risk of bias. The clinical inference drawn from these research findings suggests that integrating text messaging interventions into cardiovascular risk factors control and management could offer tangible benefits for patients. With most publications demonstrating a positive effect, healthcare providers may consider incorporating such interventions to complement existing treatments. However, it's essential for clinicians to critically evaluate the quality of evidence, considering the risk of bias in some studies. Therefore, while integrating text messaging could enhance cardiovascular risk factors control strategies, clinicians should exercise caution when applying these findings and remain vigilant for emerging evidence to ensure the interventions are used effectively and appropriately.\u003c/p\u003e\u003cp\u003eThe findings from our analysis highlight the significant impact of text messaging interventions on various cardiovascular risk factors control outcomes. Firstly, the random-effects model revealed a substantial and statistically significant reduction in SBP with an MD pooled effect size of -6.11 (95% CI: -10.25 to -1.93, p = 0.003, ² = 96% ). This indicates that text messaging interventions are effective in lowering SBP. In addition, the analysis of ten studies related to DBP revealed an MD pooled effect size of -2.66 (95% CI: -4.62 to -0.70, p = 0.007, I² = 85%), signifying a significant reduction in DBP due to text messaging interventions. However, the high heterogeneity indicates variability in the effect across different studies, highlighting the need to consider context and individual study characteristics carefully. For example, the subgroups for SBD and DBP showed that most frequent text messages, reminders and educational messages, and nurse-led messages have a bigger impact on reducing the variable. We need to point out that the risk of CVD increased steadily with progressively higher levels of baseline SBP and DBP, above a usual SBP and DBP of 115 and 75 mmHg, respectively(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). According to the guidelines outlined in the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), it is apparent that managing both systolic and diastolic blood pressure is essential for decreasing the likelihood of cardiovascular events. The report underscores the importance of integrating text messages into CVD risk factors control by addressing diastolic blood pressure to reduce the occurrence of cardiovascular diseases such as coronary artery disease, heart failure, stroke, and peripheral vascular disease.\u003c/p\u003e\u003cp\u003eIn comparison to our study, a meta-analysis of six studies(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) reported a significant reduction in DBP with an MD of -6.11 mm Hg (p \u0026lt; 0.01) with text messaging intervention, which aligns with our findings. This study also found no significant reduction in SBP with text messaging, contrasting with our results, where we found a statistically significant result; this difference is attributed to the lower sample size that the mentioned study has in the included articles they have and the most extensive ample size and range of studies we are analyzing. The variability in DBP outcomes across studies, reflected in the high heterogeneity, underscores the need for context-specific considerations when interpreting these results, as DBP reduction could be clinically not significant, while SBP reduction may be considerable. These results are important when compared with other interventions, such as the Dietary Approaches to Stop Hypertension (DASH) diet or sodium intake reduction, which, in previous guidelines such as American Heart Association (AHA)(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) has been pointed out that can reduce 11 mmgHG and 5 to 6 mmHg respectively, making text messages a powerful tool to help to manage SBP as a cardiovascular risk factor.\u003c/p\u003e\u003cp\u003eRegarding medication adherence, we demonstrated an MD pooled effect size of 0.61 (95%CI: 0.37 to 0.85; p \u0026lt; 0.0001, I² = 0.0%). This significant positive effect indicates that text messaging interventions notably improve medication adherence, as measured by the Morisky score. The low heterogeneity suggests that the impact on medication adherence is relatively consistent across the included studies. This finding is consistent with a previous study that reported a moderate effect size (SMD = 0.65, p = 0.01) in older adults with hypertension, suggesting that text messaging effectively improves adherence across different populations, but in our analysis, we have demonstrated that this effect is maintained trough our studies with a heterogeneity of I2 = 0% compared to the last analysis that showed a heterogeneity of I2 = 85% that raised concerns about the effect (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This reveals that while text messaging interventions generally show positive effects on SBP, DBP, and medication adherence, the magnitude and consistency of these effects can vary, this can be due to several facts, as we demonstrated in our subgroups analysis of different variables being different according to the content of the message, the timing of the message, and the sender of the message. The variability in outcomes across studies emphasizes the need for personalized interventions for specific populations and settings and calls for further research to refine and validate this digital tool for more consistent and effective results. The findings from our meta-analysis indicate no significant improvement in body mass index (BMI), LDL cholesterol, HDL cholesterol, total cholesterol, or HBA1C levels due to text messaging interventions. This contrasts with some previous research which reported significant changes in these metrics. For instance, a previously reported meta-analysis(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) found that text messaging interventions had a positive impact on HbA1c levels, resulting in significant reductions in HbA1C MD -0.38 (95%CI − 0.53 to − 0.23, p-value \u0026lt; 0.001). Another meta-analysis(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) reported significant improvement in cholesterol levels (SMD= -0.26; 95% CI -0.4 to -0.12; P = \u0026lt; 0.01), we need to point out that despite our initial analysis revealed no significant reduction, in our sensitivity analysis after excluding Klimis H, 2021, as an influential study we find a similar effect with a MD -7.06 (95% CI: -10.61 to -3.51, I² = 0%, p \u0026lt; 0.0001). Another meta-analysis(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) observed a moderate and statistically significant effect on reducing BMI (− 0.43 kg/m², 95% CI: -0.63 to -0.23, I\u003csup\u003e2\u003c/sup\u003e 62%, p = 0.001), this effective intervention could be attached to the inclusion of studies that add mobile health interventions and not only text messages as intervention. While another meta-analysis(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) more focused on only text messages found no statistically significant reduction in BMI (SMD = -3.61; 95% CI = -9.48 to 2.26; P = 0.23). Additionally, this study found the change in HDL cholesterol and LDL cholesterol levels were statistically non-significant LDL (SMD = -1.81 to 95% CI = -4.80 to 1.18; p = 0.24), HDL (SMD = -1.15 to 95% CI = -2.83 to 0.54; P = .18).\u003c/p\u003e\u003cp\u003eThis suggests that while text messaging interventions can improve certain cardiovascular outcomes, such as blood pressure and medication adherence, their effects on weight management, HBA1C, and lipid profiles might be less pronounced. In general, our analysis is consistent with previous findings of LDL, HDL, and cholesterol levels, while contrast for HbA1c and BMI; this could be due to the differences of the included studies as we only include text messages as intervention and not all the variety of mobile health interventions, being a pure meta-analysis that measures the effect of text message alone on this variables. Other previous studies have shown significant weight, BMI, waist circumference, and HBA1C level reductions due to text messaging interventions, though changes in lipid levels were not consistently significant. These discrepancies emphasize the need for tailored digital interventions for individual risk profiles and study-specific contexts. Future research should aim to integrate a comprehensive set of outcome measures to better evaluate the impact of text messaging interventions on cardiovascular risk factors control.\u003c/p\u003e\u003cp\u003eOur analysis demonstrated that text messaging interventions significantly reduced SBP and DBP, crucial cardiovascular health indicators. In addition to improving blood pressure, our study found a significant positive effect on medication adherence. This suggests that text messaging interventions notably enhance adherence to prescribed medication regimens, which aligns with previous research highlighting the role of digital tools in improving patient compliance. Our results indicate that text messaging interventions can improve CVD risk factors. For this reason, physicians should consider incorporating text messaging into patient management and customizing messages to meet individual needs and health goals. This approach offers continuous support, helps patients stay engaged, and allows for progress monitoring and timely treatment adjustments. However, physicians must critically assess the evidence, accounting for variability and potential biases.\u003c/p\u003e\u003cp\u003e \u003cb\u003eLimitations and future research\u003c/b\u003e \u003c/p\u003e\u003cp\u003eAcknowledging the limitations of our study is crucial for contextualizing our findings. The high heterogeneity observed in some outcomes, such as DBP and SBP, warrants cautious interpretation and the need for context-specific considerations when interpreting these results. Additionally, the methodological quality of the four included studies had some concerns about the risk of bias in the randomization process. In comparison, twelve studies had some concerns about selecting the reported result. Also, there is no possible way to differentiate what is the medications the patients were taking, as this was not reported in the pooled studies. Furthermore, the limited number of studies made performing subgroup and sensitivity analysis on some parameters impossible, precluding reliable subgroup differentiation and sensitivity assessment. Also, the sample sizes of some RCTs included were small (seven of the studies had 100 or fewer participants).\u003c/p\u003e\u003cp\u003eSmoking outcomes were found in 4 studies. Chow C (2015) and Kiselev A. (2012) found a significant smoking reduction in the intervention group in Australia and Russia. In contrast, from Australia, Santo K (2018) and Chow C (2022) have not found significant differences between interventions and the control group. The limited number of studies underscores the need for additional research and reports to validate our findings, explore potential moderators of intervention effectiveness, and address methodological challenges. For instance, smoking as an impact CVD risk factor showed outcomes only in 4 studies. Future research should aim to evaluate more with nurse text messages and obtain more information on lifestyle changes, including physical activity, smoking cessation, healthier diets, body mass index reduction, and patient medications. This will allow us to evaluate the impact of new technologies in reducing other risk factors associated with cardiovascular disease. Moreover, no studies assessed the effects of text messaging beyond the 12-month follow-up. Studies with follow-ups longer than 12 months could help understand the long-term effect that text messaging has on preventing cardiovascular disease risk factors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis demonstrate that text messaging interventions can significantly improve cardiovascular risk factors control by medication adherence and reduce systolic and diastolic blood pressure, which is critical for cardiovascular health, indicating its potential as an effective tool in CVD prevention. Despite these positive outcomes, the high heterogeneity observed in blood pressure measures and the lack of significant effects on other cardiovascular risk factors, such as BMI, LDL, HDL, total cholesterol, and HbA1c, underscore the variability in intervention effectiveness across different settings and populations. This suggests that while text messaging can enhance certain health behaviors, its impact on broader cardiovascular risk factors control metrics might be limited. Nevertheless, integrating text messaging interventions into existing healthcare strategies offers a promising, cost-effective approach to improving medication adherence and blood pressure control, vital for preventing cardiovascular events. Future research should focus on long-term effects, comprehensive lifestyle changes, and individualizing interventions to enhance their efficacy across diverse populations. Addressing these gaps will be crucial for refining digital health technologies and optimizing their role in CVD prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVDs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePRISMA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic Review and Meta-Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRCTs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Clinical Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHbA1c\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMeSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Subject Headings\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Mean Difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ek\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require ethical approval in accordance with local guidelines, as it is a meta-analysis of previously published data. No new individual patient data were collected or analyzed; therefore, patient consent was not necessary for this research. All data utilized in this analysis were sourced from studies that had obtained the requisite ethical approvals and informed consent from participants.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors consent for publication of all tables, figures and contents that were generated through author’s original analysis of data included in meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data generated or analyzed during this study are included in this published article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest or competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003cbr\u003e\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErnesto Calderon Martinez\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Writing – original draft, Visualization, Project administration, Conceptualization,\u0026nbsp;Formal analysis.\u0026nbsp;\u003cstrong\u003eStephin Zachariah Saji\u003c/strong\u003e: Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eJonathan Victor Salazar Ore\u003c/strong\u003e: Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eAjay Kumar\u003c/strong\u003e:\u0026nbsp;Data curation, Writing – review \u0026amp; editing, Investigation, Validation.\u0026nbsp;\u003cstrong\u003eSutirtha Mohanty\u003c/strong\u003e:\u0026nbsp;Data curation, Writing – review \u0026amp; editing, Investigation.\u0026nbsp;\u003cstrong\u003eViridiana Yumiko Nakamura Ramírez\u003c/strong\u003e:\u0026nbsp;Methodology, Writing – review \u0026amp; editing, Investigation\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAhmad Hammoud\u003c/strong\u003e:\u0026nbsp;Methodology, Writing – review \u0026amp; editing, Investigation, Validation\u0026nbsp;\u003cstrong\u003eLeen Nasser Shaban\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Investigation.\u0026nbsp;\u003cstrong\u003eVaidarshi Abbagoni\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Investigation, Validation\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCamila Sanchez Cruz\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Investigation.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCardiovascular. diseases [Internet]. 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The Impact of Text Message On Self-Management for Coronary Heart Disease: A Meta-Analysis of Randomized Controlled Trials. Heart Surg Forum [Internet]. 2020 Jan 23 [cited 2024 Sep 1];23(1):E18\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/32118537/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/32118537/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5112776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5112776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular diseases (CVDs) are the leading cause of global mortality, claiming 17.9\u0026nbsp;million lives annually. Major behavioral risk factors include unhealthy diet, physical inactivity, tobacco use, and excessive alcohol consumption. Text messaging interventions can potentially improve individual risk factors and encourage healthy habits. They have been shown to manage risk factors and disease progression. This systematic review and meta-analysis aimed to evaluate the efficacy of text messaging interventions for the primary prevention of CVD risk factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. Searches were conducted on PubMed, MEDLINE, Cochrane, Scopus, Web of Science, Embase, and CINAHL using MeSH and free-text terms related to cardiovascular disease and text messaging interventions on 18/03/2024.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of 6142 identified articles, 22 studies met the inclusion criteria. The meta-analysis revealed that text messaging interventions significantly improved medication adherence, with a pooled effect size of Mean Difference (MD) of 0.61 (95%CI: 0.37 to 0.85; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, I\u0026sup2; = 0.0%). They also significantly reduced diastolic blood pressure by MD of -2.66 (95% CI: -4.62 to -0.70, I\u0026sup2; = 85%, p\u0026thinsp;=\u0026thinsp;0.007) and systolic blood pressure by MD of -6.11 (95% CI: -10.25 to -1.97, I\u0026sup2; = 96%, p\u0026thinsp;=\u0026thinsp;0.003). However, no significant improvements were observed in BMI, LDL, HDL, total cholesterol, or HbA1c levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eText messaging interventions effectively improve medication adherence and help in the reduction of blood pressure, making them a promising tool for CVD risk control. However, their impact on other cardiovascular risk factors is limited, indicating the need for further research to explore long-term effects and personalized interventions for diverse populations. Integrating these digital tools into healthcare strategies could enhance CVD prevention efforts and improve cardiovascular risk factors control outcomes.\u003c/p\u003e","manuscriptTitle":"Text messages as a tool to improve cardiovascular disease risk factors control: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 17:15:54","doi":"10.21203/rs.3.rs-5112776/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-02T08:03:28+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"337277039053413431628131067379209118609","date":"2024-12-02T01:01:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T06:55:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170601199228712796878589208311421284234","date":"2024-11-30T06:54:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-15T01:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290480726555948818050468732388490270026","date":"2024-11-14T20:29:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T01:23:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218064972422090987252840689447476266848","date":"2024-10-30T14:37:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-28T06:42:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-16T05:04:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-30T12:16:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-25T23:03:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-09-25T23:02:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b9af4b2-8d87-4111-9fc7-222ba37ff57c","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:02:18+00:00","versionOfRecord":{"articleIdentity":"rs-5112776","link":"https://doi.org/10.1186/s12889-025-21818-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-04-04 15:57:53","publishedOnDateReadable":"April 4th, 2025"},"versionCreatedAt":"2024-12-16 17:15:54","video":"","vorDoi":"10.1186/s12889-025-21818-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21818-0","workflowStages":[]},"version":"v1","identity":"rs-5112776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5112776","identity":"rs-5112776","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}