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Coronary artery disease is the main cause of death and loss of disability-adjusted life years worldwide. Information and communication technology has become an important part of health care systems, including the innovative cardiac rehabilitation services through mobile phone and mobile health (mHealth) interventions.
In this study, we aimed to determine the effectiveness of different kinds of mHealth programs in changing lifestyle behavior, promoting adherence to treatment, and controlling modifiable cardiovascular risk factors and psychosocial outcomes in patients who have experienced a coronary event.
A systematic review of the literature was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A thorough search of the following biomedical databases was conducted: PubMed, Embase, Web of Science, SciELO, CINAHL, Scopus, The Clinical Trial, and Cochrane. Articles that were randomized clinical trials that involved an intervention consisting of an mHealth program using a mobile app in patients after a coronary event were included. The articles analyzed some of the following variables as outcome variables: changes in lifestyle behavior, cardiovascular risk factors, and anthropometric and psychosocial variables. A meta-analysis of the variables studied was performed with the Cochrane tool. The risk of bias was assessed using the Cochrane Collaboration tool; the quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation tool; and heterogeneity was measured using the
A total of 23 articles were included in the review, and 20 (87%) were included in the meta-analysis, with a total sample size of 4535 patients. Exercise capacity measured using the 6-minute walk test (mean difference=21.64, 95% CI 12.72-30.55;
mHealth technology has a positive effect on patients who have experienced a coronary event in terms of their exercise capacity, physical activity, adherence to medication, and physical and mental quality of life, as well as readmissions for all causes and cardiovascular causes.
PROSPERO (International Prospective Register of Systematic Reviews) CRD42022299931; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=299931
Cardiovascular diseases (CVDs) are the main cause of death worldwide [
Among CVDs, coronary artery disease (CAD) is the main cause of death and loss of disability-adjusted life years worldwide [
The secondary prevention of CAD is considered essential at present [
Thanks to the advances in medicine and technology, hospital stays after myocardial infarction have been shortened in recent years, meaning that health care professionals have fewer opportunities to inform patients about their disease during their admission [
Information and communication technology is becoming an increasingly important part of health care systems, including the innovative cardiac rehabilitation (CR) services through mobile phone and mobile health (mHealth) interventions [
Early secondary preventive care patients recently discharged after acute coronary syndrome was shown to promote adherence to drug treatment and facilitate the control of changes in cardiovascular risk factors. However, because of the COVID-19 pandemic, it is likely that the uptake and availability of secondary prevention strategies have been affected, as CR programs may have been suspended or patients avoided or could not go to health centers [
Despite the exponential growth in and availability of smartphone technology to provide a new tool to optimize the secondary prevention of heart diseases, no systematic reviews have been published that focus exclusively on the effectiveness of mHealth involving mobile apps as a way of providing digital health and its secondary prevention components to patients who have experienced a coronary event. Thus, the aim of this review was to determine the effectiveness of the different means of providing mHealth programs in changing lifestyle behavior, promoting adherence to treatment, and controlling modifiable cardiovascular risk factors and psychosocial outcomes.
A systematic review of the literature was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [
Studies were included if they complied with the inclusion criteria, namely randomized controlled trials (RCTs) in which an intervention had been performed consisting of a telehealth or mHealth program by means of a mobile app in patients with coronary heart disease and included the following outcome variables: change in lifestyle behavior (diet, physical exercise, and treatment adherence) and control of cardiovascular risk factors (tobacco, blood sugar, systolic blood pressure [SBP], diastolic blood pressure [DBP], total cholesterol, low-density lipoprotein [LDL] cholesterol, and high-density lipoprotein [HDL] cholesterol); anthropometric variables (waist circumference and BMI); and psychosocial variables (anxiety, depression, and stress).
Studies that used SMS text messages without an app or web portal and included participants who had experienced a stroke or another CVD were excluded.
Two researchers independently examined the identified articles using the search strategy described in the
A total of 23 studies were included in the systematic review, of which 20 (87%) were included in the meta-analysis. The Cochrane Review Manager (RevMan 5.4; The Cochrane Collaboration) software was used for the statistical analysis. Differences in the effects of mHealth interventions and standard health care were examined by means of the inverse variance method. The difference of means was used as the statistic to analyze the effect, and the standardized difference of means was used when variables with different measurement scales were compared, and a 95% CI was given for each effect size. Risk difference was assessed for the qualitative variables. To test the hypothesis, the
The search provided a total of 1773 articles that were distributed among the following databases: Web of Science (n=598, 33.73%); PubMed (n=299, 16.86%); Scopus (n=168, 9.48%); SciELO (n=69, 3.89%); CINAHL (n=319, 17.99%); Cochrane (n=172, 9.7%); and The Clinical Trial (n=148, 8.35%). A total of 23.18% (411/1773) of articles were identified as duplicates and hence removed. First, the titles and then the abstracts were checked using the inclusion and exclusion criteria. Eventually, 8.35% (148/1773) of articles were selected for the whole text to be read, of which 15.5% (23/148) were chosen for the review and 13.5% (20/148) for the meta-analysis.
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of the study selection process. CVD: cardiovascular disease.
All the articles that were selected to be part of the review were RCTs, with a total of 4535 patients. Among the 24 variables analyzed in the RCTs, 54% (13/24) presented evidence of high or moderate quality and 46% (11/24) of variables provided low or very low quality of evidence. The follow-up duration of the intervention ranged from 2 to 26 months, with the most frequent duration being 6 months. The age of the patients in these clinical trials ranged from 55 to 66 years and 81.32% (3688/4535) of the patients were male.
In the included studies, the control group received “usual health care” or “standard medical care” after the coronary event. In general, the interventions were conducted by a multidisciplinary team of nurses, cardiologists, physiotherapists, nutritionists, specialists in sports medicine, and exercise physiologists.
The patient dropout rate across the studies did not exceed 20%, except in the trial by Skobel et al [
The most commonly studied variables were SBP, DBP, and lifestyle, whereas the least frequently analyzed were c-reactive protein, which was studied only by 2 authors [
The risk of bias in the RCTs included in this review is summarized in
Owing to the nature of these RCTs (N=23), the participants, and sometimes the medical professionals, could not be blinded; therefore, all the trials were considered to present a high risk of concealment bias. The researcher assessing the results was not blinded in 17% (4/23) of studies [
Regarding attrition bias, 22% (5/23) of the trials were considered high risk because of incomplete results data [
All the trials included in the review were classified as having a low risk of reporting bias because of the following reasons: the trials had study protocols that were readily available; the results studied were previously specified; or if the study protocol was not available, it was clear that all the expected results were included.
Finally, regarding other possible risks of bias, all the studies were classified as low risk, as the patients who participated in the trials provided their written, informed consent to participate in the study. All the RCTs were approved by the ethics committee of the institution where the trial was conducted, and their ethics approval statements were included in the texts.
In summary, most of the trials were assessed as having moderate risk, as it was not possible to blind all the participants because of the nature of these RCTs.
Risk of bias summary: authors’ judgments about each risk of bias item for each included trial.
Risk of bias graph: authors’ judgments about each risk of bias item presented as percentages across all included trials.
A total of 39% (9/23) of the trials provided data on the plasma concentrations of total cholesterol and LDLs from a total of 1211 participants. Santo et al [
The meta-analysis of the included trials did not show significant differences in total cholesterol (
A total of 57% (13/23) of studies with high heterogeneity (
A total of 39% (9/23) of trials with high heterogeneity (
A total of 13% (3/23) of studies with high heterogeneity (
Heart rate (
A total of 17% (4/23) of homogeneous studies (
Another outcome measure of exercise capacity was the peak oxygen consumption, studied in 8 trials with high heterogeneity (
Forest plots for changes in the 6-minute walk test. IV: instrumental variable; mHealth: mobile health.
A total of 17% (4/23) of studies with high heterogeneity (
Forest plots for changes in physical exercise. IV: instrumental variable; mHealth: mobile health.
Health-related quality of life was studied in 39% (9/23) of RCTs with moderate heterogeneity (
The physical and mental dimensions of quality of life were analyzed in 22% (5/23) of studies, with a sample of 620 patients. The following validated questionnaires were used in these trials: 12-Item Short Form Health Survey, 36-Item Short Form Health Survey, Health-Related Quality of Life, and World Health Organization Quality of Life: Brief Version.
In both the physical (
Forest plots for changes in quality of life (physical dimension or physical health). IV: instrumental variable; mHealth: mobile health.
Forest plots for changes in quality of life (mental dimension or mental health). IV: instrumental variable; mHealth: mobile health.
Depression was analyzed in 22% (5/23) of trials with moderate heterogeneity (
Three authors studied adherence to medication using the 8-item Morisky Medication Adherence Scale and a self-reported questionnaire in a survey of 507 patients (
Forest plots for changes in adherence to medication. IV: instrumental variable; mHealth: mobile health.
A total of 13% (3/23) of studies analyzed the difference in mortality between the groups. In the meta-analysis, with a sample of 2010 patients, no significant differences in all-cause mortality (
Regarding the rehospitalizations of patients during the study period in each RCT, the meta-analysis showed that rehospitalizations for both all causes (
Furthermore, a sensitivity analysis was performed by excluding each study sequentially to determine the influence of any single study on the robustness of the results, revealing no substantial difference in the overall effect for the 6-MWT, quality of life, physical activity, and rehospitalizations (
Forest plots for changes in rehospitalizations for all causes. IV: instrumental variable; mHealth: mobile health.
Forest plots for changes in rehospitalizations for cardiovascular causes. IV: instrumental variable; mHealth: mobile health.
This study presents an assessment of evidence from RCTs that compared the effects of mHealth and standard interventions on lifestyle, adherence to treatment, and changes in cardiovascular risk factors after a coronary event. This meta-analysis provides evidence of the favorable effects of mHealth interventions on the variables analyzed. The fact that the studies chosen for this review were published in recent years is proof of the growing interest in mHealth interventions as a resource aimed at improving the secondary prevention of CAD.
Keeping blood lipids and blood pressure under control are very important objectives in the secondary prevention of CVDs. A meta-analysis conducted by Gencer et al [
The increased prevalence of obesity has become an important public health concern worldwide. Total and abdominal adiposity during adolescence is associated with atherosclerosis in adulthood and insulin resistance [
A high blood glucose level is also an important risk factor leading to the onset and development of CAD. A recent meta-analysis concluded that prediabetes is associated with a greater risk of all-cause mortality and CVD in the general population and in patients with atherosclerotic CVD [
Regarding the number of people who had stopped smoking at the end of the intervention, the percentage was high in both groups (standard care and mHealth), but the results were not statistically significant. These findings are similar to those reported in the meta-analyses by Akinosun et al [
Physical inactivity is independently associated with 12.2% of the global burden of acute myocardial infarction [
Many patients do not comply with lifestyle recommendations or do not take their medication as prescribed after a cardiovascular event. Adherence to treatment by patients who are prescribed cardiovascular medication is estimated to be approximately 51% a year after a myocardial infarction [
CAD is one of the main causes of disability and loss of health-related quality of life among patients with this disease [
Several studies have reported that anxiety and depression are also independent risk factors for cardiovascular morbidity and mortality [
In our meta-analysis, we did not observe differences between the intervention and usual care groups in terms of mortality, but we did find a reduction in hospitalizations for all causes and for cardiovascular causes with the digital intervention. However, these variables were not included in other meta-analyses, which makes their comparison difficult.
This review found that although the usability, viability, and acceptance of mHealth tools for modifying cardiovascular risk factors and lifestyle were included as variables in a few studies, they were highly valued by the patients. A study by Johnston et al [
The heterogeneity of some of the variables studied was high, possibly because of the small sample sizes; different monitoring durations of the RCTs; differences in the age of the participants; and different settings in which the mHealth programs took place (hospital, home, or outpatient clinics).
In general, systematic reviews include interventions with different technologies such as mobile phones, websites, and software apps, but they do not usually compare these technologies with each other. However, the meta-analysis by Xu et al [
Cell phones are considered efficient digital devices and have been the most widely studied because of their affordability and ease of use; however, smartphones may have advantages because of additional interaction features.
Regarding the limitations of our study, it is worth mentioning aspects such as the fact that the participants in the included RCTs enrolled voluntarily by signing an informed consent form, which probably introduced a selection bias, as these patients might have been more motivated to adhere to secondary prevention than others who chose not to participate. Another limitation could be that to participate in the mHealth programs, the patients had to have a mobile phone or a tablet with an internet connection, which could suggest that the participants were younger. However, this limitation seems to be of little importance because nowadays, >75% of the world population has a mobile telephone with internet access and >57% of homes have an internet connection. In Europe, these figures are even higher, reaching 99% and 86%, respectively [
Another consideration is that the trials used nonvalidated self-reported questionnaires to analyze some objectives, resulting in the generalizability and coherence of the studies being variable. More studies are required to examine the long-term impact of smartphone-based interventions on people who have experienced a coronary event with regard to heart-related mortality and hospital admissions, as these are important measures of the success of secondary prevention strategies.
A strength of our meta-analysis is that RCTs with very similar interventions were selected, involving the use of an app or web portal and programs based on SMS text messages or reminders and telephone calls were excluded. As a result, the interventions analyzed used the newest and most up-to-date technology. To the best of our knowledge, this is the first meta-analysis to group these interventions based on mobile apps for secondary prevention exclusively in patients with CAD after a coronary event, not with risk of CVD. Another strength is the inclusion of many kinds of behavioral, metabolic, and psychosocial variables, providing a broad view of the results being obtained with mHealth technology. All the studies included in this review and meta-analysis were RCTs that are the key to scientific evidence in clinical research. In addition, these clinical trials were conducted in a wide variety of countries in Europe, America, Asia, and Oceania.
Future trials should include larger sample sizes, less-studied variables such as quality of life or readmissions; long-term follow-up; comprehensive explanation of the intervention (frequency, length, and intensity); cost-effectiveness analysis; usability; application of emerging technologies; apps adapted to the age and clinical situation of patients (comorbidity and immobility); and software and hardware improvements such as larger interface fonts or accessible and understandable programs. All these aspects will improve the quality of the trials and help identify the characteristics of the most effective mHealth interventions.
mHealth technology has a positive effect on patients who have undergone a coronary event in terms of their exercise capacity, performance of physical exercise, adherence to medication, physical and mental quality of life, and hospital readmissions for all causes and cardiovascular causes. More research is required with long-term follow-ups and cost analyses to determine the clinical importance of these findings and to promote their generalization, implementation, and feasibility. A promising future for mHealth technology will be based on the development of apps that are user-friendly and personalized and include motivation and feedback strategies.
Complete search strategy.
Grading of Recommendations, Assessment, Development and Evaluations summary of findings: mobile health versus standard care.
Funnel plots for outcome variables.
Characteristics of included studies.
Forest plots for changes in blood lipids.
Forest plots for changes in systolic blood pressure, diastolic blood pressure, BMI, and waist circumference.
Forest plots for changes in glycated hemoglobin, glucose, heart rate, and smoking cessation.
Forest plots for changes in oxygen consumption peak.
Forest plots for changes in anxiety, depression, and quality of life.
Forest plot for changes in mortality.
Sensitivity analysis.
6-minute walk test
coronary artery disease
cardiac rehabilitation
cardiovascular disease
diastolic blood pressure
high-density lipoprotein
low-density lipoprotein
mobile health
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
International Prospective Register of Systematic Reviews
randomized controlled trial
systolic blood pressure
This project is supported by a grant to fund Research and Innovation in Biomedical and Health Sciences within the framework of the Integrated Territorial Initiative 2014-2020 for the province of Cadiz. Reference code: PI-0014-20219. The project was cofinanced with 80% funds from the Andalusian European Regional Development Fund Operational Program 2014-2020 and was also funded by Ministry of Health and Families.
None declared.