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Review Paper
Effectiveness of mobile health interventions to improve medication adherence for patients with cardiovascular disease: a systematic review and meta-analysis
Yeoungsuk Song1orcid, Seurk Park2orcid, Yuyoung Lee1orcid, Sohye Lee3orcid

DOI: https://doi.org/10.4040/jkan.26016
Published online: May 22, 2026

1College of Nursing, Kyungpook National University, Daegu, South Korea

2School of Nursing, Gyeongkuk National University, Andong, South Korea

3Loewenberg College of Nursing, University of Memphis, Memphis, TN, USA

Corresponding author: Seurk Park School of Nursing, Gyeongkuk National University, 1375 Gyeongdong-ro, Andong 36729, South Korea E-mail: ps@gknu.ac.kr
• Received: February 4, 2026   • Revised: April 19, 2026   • Accepted: April 22, 2026

© 2026 Korean Society of Nursing Science

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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  • Purpose
    This study systematically reviewed and synthesized the evidence on the effectiveness of mobile health (mHealth) interventions in improving medication adherence among patients with cardiovascular disease.
  • Methods
    This systematic review included randomized controlled trials that evaluated the effects of mHealth interventions on medication adherence among patients with cardiovascular disease. PubMed, the Cochrane Library, CINAHL, and Embase were searched for peer-reviewed studies and grey literature published in English between January 1, 2013, and July 31, 2025. The Cochrane Risk of Bias 2 (RoB 2) tool was used to assess the risk of bias in the included studies. R software ver. 4.5.2 was used to perform the meta-analysis.
  • Results
    Fifty-two studies were included in the systematic review, of which 20 were included in the meta-analysis. The pooled analysis demonstrated a significant improvement in medication adherence among patients with cardiovascular disease receiving mHealth interventions, with a moderate to large effect size (Hedges’ g=0.72; 95% confidence interval, 0.20–1.25; p<.001), despite substantial heterogeneity (I2=97%). However, a considerable proportion of the included studies were assessed as having a high risk of bias, which may limit the internal validity of the findings. Subgroup analyses indicated that the effects of mHealth interventions on medication adherence did not differ significantly according to intervention type, duration, or outcome measurement tools.
  • Conclusion
    mHealth interventions appear to be effective in improving medication adherence among patients with cardiovascular disease. However, these findings should be interpreted with caution because of the high risk of bias and substantial heterogeneity among the included studies. Future research should explore the use of emerging technologies, such as artificial intelligence and virtual reality, to address medication non-adherence (PROSPERO registration number: CRD42023450502).
  • Key words: Cardiovascular diseases; Medication adherence; Meta-analysis; Mobile applications; Systematic review
In South Korea, cardiovascular disease (CVD) is the second leading cause of death [1]. Globally, CVD affects almost half a billion people worldwide, and reports indicate that more than 18 million people have died from CVD [2]. Recent statistics indicate a decline in CVD-related mortality owing to improved healthcare technology and prevention, screening, treatment, and rehabilitation methods [3]. However, CVD continues to result in high burdens not only at the individual level, such as poor quality of life, caregiver burden, high medical expenses, and premature death [4-6], but also at the societal level, such as increased healthcare costs and health disparity or equity issues [7-9]. There has been an urgent need to properly manage CVD and prevent CVD-related adverse events; however the one major barrier is poor medication adherence.
Notably, medication adherence continues to undermine optimal CVD prevention and management despite the availability of effective pharmacologic therapies to manage CVD. Medication adherence refers to the “voluntary cooperation of the patient in taking drugs or medicine as prescribed, including timing, dosage, and frequency” [10]. Unfortunately, poor medication adherence remains a substantial challenge for patients and healthcare providers [11]. Over half of the medications used to treat chronic diseases are not taken as prescribed [11], and medication adherence remains a significant challenge in the management of CVD.
Previous studies have identified several factors that contribute to poor medication adherence and treatment compliance, including sociodemographic factors, fear, cost, psychiatric symptoms, lack of trust, and healthcare system issues [12-15]. A growing literature stream indicates that medication adherence and treatment compliance can improve symptom management and prognosis and prevent complications from CVD, such as cardiac events and death [16,17]. Thus, effective interventions to improve medication adherence for patients with CVD must be developed and tested [18].
Given the challenges of medication adherence, mobile health (mHealth) interventions have been continuously developed and tested to improve treatment compliance and medication adherence in patients with chronic diseases [19-21]. The term “mHealth” refers to the utilization of mobile and wireless technologies to deliver health services and improve health outcomes, healthcare services, and health research [22]. With the accessibility of high-speed internet and availability of various hardware (e.g., wireless devices such as mobile phones, smart watches, and tablets) and software (e.g., mobile applications and artificial intelligence) technologies, technology-based mHealth interventions have been developed and tested to educate and support patients with chronic diseases. However, the rapid evolution of mobile technologies and the increasing number of related studies have created the need for an updated synthesis of the evidence. Thus, reviews of the most up-to-date research on how mHealth interventions can improve medication adherence in patients with CVD are warranted.
Recently, several systematic reviews and meta-analyses have examined the effectiveness of mHealth interventions for patients with various types of CVD, such as hypertension [23] and heart failure [24], as well as for CVD prevention [25,26]. Additionally, a prior systematic review and meta-analysis evaluated the effectiveness of mHealth interventions in improving medication adherence among patients with CVD [27]. However, it addressed a broad range of outcomes, such as self-management, patient knowledge, self-care behavior, and symptom management, rather than focusing specifically on medication adherence. Moreover, several new randomized controlled trials (RCTs) have been published in recent years. Therefore, this review provides updated evidence by synthesizing recent RCTs and specifically focuses on the effectiveness of mHealth interventions for improving medication adherence in patients with CVD.
This systematic review and meta-analysis examined the effectiveness of mHealth interventions in improving medication adherence in patients with CVD. The following research questions were explored: (1) What are the characteristics of mHealth intervention studies? (2) What are the effects of mHealth interventions on medication adherence? (3) What are the characteristics and key features of mHealth interventions?
1. Design
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [28]. The study protocol was registered in PROSPERO (CRD42023450502).
2. Inclusion and exclusion criteria
Eligibility criteria were employed according to the Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) framework. The detailed inclusion criteria were (1) P‒patients with CVD aged 18 and above, regardless of gender, race, or type of CVD; (2) I‒mHealth interventions used in hospital or healthcare settings; (3) C‒comparisons including usual or standard care, no intervention, wait-list controls, or active comparator conditions involving alternative interventions; (4) O‒medication adherence; and (5) S‒RCTs. The date range included studies published between 2013 and 2025. This period is appropriate for observing recent trends because of the rapid changes in healthcare technology over a 10-year span [29]. Studies published in English were included. The exclusion criteria were (1) studies including patients with stroke, cerebrovascular disease, or hyperlipidemia or those involving adolescents; (2) studies that did not include an mHealth intervention; (3) studies that did not report medication adherence outcomes; (4) quasi-experimental or non-experimental studies, reviews, or reports; and (5) studies in languages other than English.
3. Search strategy and outcomes
The PubMed, Cochrane Library, CINAHL, and Embase databases were searched using medical subject headings (MeSH) terms and relevant keywords on July 31, 2025. Detailed search strategies for each database are summarized in Supplementary Appendix 1.
The studies were reviewed to identify duplicates to remove using reference management software, EndNote ver. 20 (Clarivate) [30]. Two researchers (S.P. and S.L.) independently screened the titles and abstracts. If discrepancies occurred, two other researchers (Y.S. and Y.L.) were consulted until a consensus was reached. Full-text papers were read in their entirety, and the reference lists of studies that met the eligibility criteria were checked.
4. Data extraction and quality appraisal
Two reviewers trained in systematic review and meta-analysis with relevant research experience independently extracted data manually from the included studies using a standard data extraction form (author, year, country, study design, participant, sample size, intervention type, duration, comparator, and outcome measurement tools). Disagreements were resolved through discussion or referral to a third author. Extracted data were first entered into an intermediate software program (Microsoft Excel for Windows; Microsoft Corp.) and subsequently imported into R ver. 4.5.2 (The R Foundation for Statistical Computing) for analysis.
The quality of the RCTs included in the review was assessed using version 2 of the Cochrane Risk of Bias (RoB 2) tool (Cochrane) [31]. This tool evaluates five domains: randomization process (D1), deviations from intended interventions (D2), missing outcome data (D3), measurement of the outcome (D4), and selection of the reported result (D5). Each domain was rated as low risk (+), some concerns (!), or high risk (–), contributing to an overall risk of bias judgment. Two researchers independently assessed the risk of bias and resolved any discrepancies in ratings through group discussion.
5. Synthesis
Statistical analyses were conducted using R ver. 4.5.2. The results of the individual studies were calculated as the standardized mean difference (SMD) with 95% confidence intervals (CIs) for the data synthesis of continuous variables. For analytical purposes, subgroup analyses were conducted according to intervention type, intervention duration, and outcome measurement tools. Statistical significance was set at p<.05.
Statistical heterogeneity was assessed using chi-square (χ2) and I2 tests indicating the following values: 0%–40% (not important), 30%–60% (moderate), 50%–90% (substantial), and 75%–100% (considerable) [32]. A random effects model was used to account for variability in intervention type, intervention duration, and outcome measurement tools across the included studies. A funnel plot for potential publication bias was drawn, and the Egger test was used to verify the results.
6. Ethical considerations
As this study was based exclusively on previously published data and did not involve human participants or identifiable personal information, institutional review board approval was not required.
1. Study selection
Figure 1 presents the PRISMA flowchart of the detailed study selection process. The initial database searches yielded 4,368 records. After removing 1,688 duplicates, 2,680 titles and abstracts were screened. This screening process excluded 2,352 records that did not meet the inclusion criteria. The full texts of the remaining 328 articles were assessed for eligibility, and 252 were excluded: 118 included ineligible populations, 22 did not meet the intervention criteria, 59 had irrelevant outcomes, 14 used an inappropriate study design, and 39 were excluded for other reasons. Among the 76 reports assessed for eligibility, 24 were further excluded due to interventions not involving mobile devices. Ultimately, 52 studies met all the inclusion criteria and were included in the qualitative synthesis of this systematic review (Supplementary Appendix 2). Of these, 32 were excluded from the meta-analysis because of insufficient statistical information, such as means and standard deviations. Consequently, 20 studies were included in this meta-analysis.
2. Risk of bias
Among the 52 included studies, 7 (13.5%) were rated as low risk, 18 (34.6%) had some concerns, and 27 (51.9%) were judged as high risk (Figure 2) [33-84]. Thus, most studies demonstrated either methodological weaknesses or concerns in at least one key domain. Domain-specific findings showed that, for D1, most studies (n=41; 78.9%) were considered low risk, with two studies (3.8%) judged as high risk and 9 (17.3%) classified as having some concerns. For D2, only 25 studies (48.1%) were low risk, whereas 23 (44.2%) had some concerns, and 4 (7.7%) were high risk. For D3, 42 studies (80.8%) were low risk, 2 (3.8%) had some concerns, and 8 (15.4%) were high risk. For D4, only 19 studies (36.5%) were low risk, with 16 (30.8%) showing some concerns and 17 (32.7%) rated as high risk. Finally, for D5, 30 studies (57.7%) were low risk, 10 (19.2%) showed some concerns, and 12 (23.1%) were high risk.
3. Study characteristics
Table 1 presents a descriptive summary of the included studies, and Table 2 outlines their key characteristics [33-84]. The included studies were published in English between 2015 and 2025. Fifteen studies (28.8%) were published between 2015 and 2019, whereas the majority (n=37; 71.2%) were published in 2020 or later, indicating a substantial recent increase in research on mHealth interventions for medication adherence. Studies were conducted across multiple countries, with the largest proportion being conducted in China (23.1%), followed by the United States (19.2%), Australia (7.7%), and South Korea (5.8%). The remaining studies (44.2%) were conducted in other countries. Most studies employed an individual RCTs design (n=43; 82.7%). Pilot RCTs accounted for four studies (n=4; 7.7%), while five studies (9.6%) employed other randomized designs, including cluster, crossover, and mixed-methods RCTs.

1) Participant characteristics

Among the included studies, 23 (44.2%) focused on patients with hypertension, followed by coronary heart disease (n=8; 15.4%), atrial fibrillation (n=6; 11.5%), and acute coronary syndrome (n=4; 7.7%). Patients with coronary artery disease and heart failure were each represented in the included studies (n=3; 5.8% for each). Five studies (9.6%) included mixed or general CVD populations, comprising general CVD as well as combined conditions such as acute coronary syndrome, heart failure or mechanical valve replacement. Regarding sample size, most studies enrolled between 100 and 299 participants (n=20; 38.4%), followed by fewer than 100 participants (n=16; 30.8%), and 300–999 participants (n=12; 23.1%). Only a small proportion of studies included 1,000 or more participants (n=4; 7.7%).

2) Intervention characteristics

Intervention types were categorized as text message-based, smartphone application-based, and multicomponent mHealth interventions. Smartphone application-based interventions were the most frequent (n=22; 42.3%), followed by text message-based interventions (n=20; 38.5%) and multicomponent mHealth interventions (n=10; 19.2%). Intervention duration ranged from 3 to 52 weeks and was categorized as ≤12 weeks (n=25; 48.1%), 13–24 weeks (n=12; 23.1%), and ≥25 weeks (n=15; 28.8%).

3) Comparator

Most studies compared mHealth interventions with usual or standard care (n=41; 78.8%), while seven studies (13.5%) employed active comparators involving alternative digital interventions, such as simplified app-based monitoring, low-frequency administrative text messages, or interactive technology alone. Four studies (7.7%) included a no-intervention control group.

4) Outcome measurement tools

Medication adherence was assessed using various outcome measurement tools across the included studies. Morisky-based adherence instruments were the most frequently used, accounting for 22 studies (42.3%), with the Morisky Medication Adherence Scale-8 (MMAS-8) being the predominant tool, followed by other Morisky-based measures such as the Morisky Medication Adherence Scale (MMAS), Medication Adherence Questionnaire (MAQ), and Morisky-Green-Levine Scale. Five studies (9.6%) used the Hill-Bone Compliance Scale (HBCS). Other self-reported adherence measures, such as the Adherence to Refills and Medications Scale-14, Medication Adherence Report Scale (MARS)/MARS-5, Brief Medication Questionnaire, Brilique Adherence Questionnaire, Rief Adherence Index, Voils Medication Non-adherence Extent Scale, self-designed questionnaires, and self-reported medication adherence were used in 16 studies (30.8%). Objective adherence indicators, including pill count, proportion of days covered, and electronic medication event monitoring system were used in 10 studies (17.3%). In studies that employed multiple adherence measures, each study was classified into a single adherence measurement category to avoid double counting in the analysis.
4. Meta-analysis of study outcomes

1) Intervention effectiveness: medication adherence

Figure 3 presents the results of the 20 studies included in the meta-analysis of medication adherence interventions [34,35,38,39,41,44,49,52-54,58,62,67,68,70,76,77,79,80,84]. The results showed that mHealth interventions had a significant moderate-to-large effect on medication adherence (Hedges’ g=0.72; 95% CI, 0.20–1.25), with a significant difference between the experimental and control groups (Z=2.71, p<.001). The results indicated a significant improvement in favor of the intervention, with high heterogeneity (I2=96.9%).

2) Subgroup analysis

Subgroup analyses were conducted based on intervention type, duration, and outcome measurement tools (Supplementary Table 1).

(1) Intervention type

A subgroup analysis by intervention type was conducted with 14 studies [34,35,38,39,41,44,49,62,68,76,77,79,80,84]. Text message-based interventions showed a small but significant effect on medication adherence (SMD=0.38; 95% CI, 0.05–0.70; p=.020) [35,38,39,84], while smartphone application-based interventions demonstrated a significantly larger effect (SMD=0.86; 95% CI, 0.27–1.45; p=.004) [34,41,44,49,68,77,79]. Multicomponent mHealth interventions showed a large point estimate but did not reach significance (SMD=1.41; 95% CI, –1.07 to 3.90; p=.270) [62,76,80]. However, the test for subgroup differences indicated no significant differences in effect sizes across intervention types (Q=2.49, degrees of freedom [df]=2, p=.290).

(2) Intervention duration

A subgroup analysis by intervention duration was conducted with 14 studies [34,35,38,39,49,52,58,62,67,68,76,79,80,84]. Interventions lasting 12 weeks showed a small but statistically significant improvement in medication adherence (SMD=0.28; 95% CI, 0.04–0.51; p<.001) [34,35,39,52,58,67,68,76,79,84]. In contrast, interventions lasting 24 weeks (SMD=11.09; 95% CI, –10.52 to 32.70; p=.314) [49,62] or 52 weeks (SMD=1.87; 95% CI, –1.81 to 5.55; p=.318) [38,80] demonstrated larger point estimates but did not reach significance owing to wide CIs. The test for subgroup differences in effect sizes across intervention duration was not statistically significant (Q=1.67, df=2, p=.434).

(3) Outcome measurement tools

A subgroup analysis by outcome measurement tools was conducted with 13 studies [34,35,38,39,41,49,53,54,62,68,76,79,84]. Studies using the HBCS (SMD=0.81; 95% CI, 0.29–1.32; p=.002) [34,39,41,76], MAQ (SMD=0.54; 95% CI, 0.24–0.84; p<.001) [54,55], and MMAS-8 (SMD=0.64; 95% CI, 0.18–1.10; p=.006) [35,38,49,62,68,79,84] demonstrated significant improvements in medication adherence. However, the test for subgroup differences indicated no significant differences in effect sizes across the outcome measurement tools (Q=0.78, df=2, p=.680).
5. Publication bias
A funnel plot was used to examine the potential publication bias among the 20 studies included in the meta-analysis (Supplementary Figure 1). Visual inspection of the funnel plot suggested asymmetry around the central dotted line. Egger’s regression test further indicated that this asymmetry was not statistically significant (bias=5.74; t=2.05, df=18, p=.055), suggesting no evidence of publication bias.
This systematic review and meta-analysis examined the effects of mHealth interventions on medication adherence in patients with CVD. Of the 52 included studies, 71.2% were published after 2020. The studies were primarily conducted in China and the United States, with additional studies conducted across several other countries. This trend suggests that CVD remains a leading cause of mortality nationally and globally, prompting active efforts to reduce its burden through various interventions. Furthermore, advances in mHealth technologies and artificial intelligence have led to a marked increase in mobile-based interventions since 2020 [83]. Similarly, South Korea has made efforts to improve medication adherence through a range of mobile-based interventions.
In the review of studies by mHealth intervention type, smartphone applications were the most frequently used, followed by text message-based interventions. Smartphone applications provided more comprehensive and sustained support by offering structured medication information, automated reminders, calendar integration, and motivational features such as rewards and social connectivity [85]. In contrast, text messages supported immediate behavior initiation and habit formation by reducing forgetfulness, supporting adherence during routine disruptions, and reinforcing motivation through simple, concise reminders and social support [85]. Overall, text messaging was effective in terms of simplicity and immediacy, whereas smartphone applications facilitated personalized and long-term adherence support.
The most frequently used outcome measurement tool for medication adherence in this review was the MAAS-8. This is consistent with the MAAS-8 being one of the most widely used tools to assess patient medication adherence, with its reliability and validity confirmed for managing chronic conditions [86], such as hypertension and diabetes [87]. This measure includes eight self-reported items [86], which places a minimal burden on patients. The following three adherence levels were estimated: 0 to <6 (low), 6 to <8 (medium), and ≥8 (high). In particular, this measure could be used to identify patients at risk of non-adherence [86]. Furthermore, this scale can be integrated into an electronic health record system [87], thus helping to detect patients at high risk for non-adherence in outpatient or community health nursing care settings.
This meta-analysis of 20 studies demonstrated that mHealth interventions can significantly improve medication adherence in patients with CVD, with a moderate-to-large effect size (Hedges’ g=0.72). The effect size of eHealth interventions, including those incorporating electronic health records, meta-analysis on medication adherence was 0.38 in renal transplant recipients [88], and 0.41 for patients with asthma [89]. Otherwise, that of mHealth was 0.90, with a large effect size in patients with CVD [27]. Overall, the relatively larger effects observed for mHealth interventions may be explained by their ability to deliver real-time, context-sensitive support independent of location, which is particularly advantageous for promoting sustained medication adherence. However, substantial heterogeneity was observed in this study, and subgroup analyses were conducted to examine potential sources related to intervention characteristics and medication adherence measurement tools. This heterogeneity may be attributable to differences in intervention characteristics, patient populations, and outcome measurement approaches across the included studies.
Although subgroup differences by intervention type were not significant, smartphone application-based interventions demonstrated the largest point estimate (SMD=0.86) in intervention types. This finding suggests that smartphone-based interventions may have particular advantages in supporting medication adherence, although these differences should be interpreted with caution. Smartphone applications are generally perceived as user-friendly, easy to use, and helpful [27], and can facilitate individualized, one-on-one communication while delivering personalized education tailored to patients’ clinical conditions [49]. For example, integrated features, such as automated medication reminders and scheduled international normalized ratio (INR) monitoring alerts, enabled timely warfarin intake and appropriate dose adjustment. Thus, these application-based functions likely enhanced medication adherence and INR compliance [49]. Although younger patients demonstrated greater willingness to use smartphones, older patients were reluctant to engage in telemedicine [88]. Therefore, the implementation of age-friendly, simplified interfaces, voice-guided prompts, and enlarged font sizes has been recommended to maximize the effectiveness of smartphone-based applications [88].
Subgroup analysis by intervention duration did not reveal any significant differences. Among the examined durations, only 12-week interventions demonstrated a small but significant improvement in medication adherence. In contrast, longer duration interventions, including 24- and 52-week programs, did not show significant effects and were characterized by wide CIs, indicating heterogeneity and imprecision in the effect estimates. These findings suggest that extending intervention duration does not necessarily lead to greater effectiveness and that longer interventions may be more susceptible to variability in adherence trajectories and potential declines in intervention fidelity over time [38,62]. Overall, short-term interventions of approximately 3 months may be sufficient to achieve modest improvements in medication adherence among patients with CVD [34,35,39], although the durability of these effects warrants further investigation.
Subgroup differences across the outcome measurement tools were not significant. However, the HBCS demonstrated the largest point estimate, which may be explained by its disease-specific design for hypertensive populations and the inclusion of multiple self-management domains, such as medication use, dietary sodium restriction, and appointment-keeping [90], rather than reflecting genuine differences in intervention effectiveness. In contrast, the MMAS-8 is a generic adherence measure applicable across a range of chronic conditions [35,91] that focuses primarily on medication-taking behaviors. Future research on mHealth for patients with CVD should adopt common, well-validated adherence measures or report sufficient detail to enable stratified analyses by measurement instrument. Furthermore, combining self-report tools with objective indicators would enhance the comparability and robustness of the intervention effect estimates.
Overall RoB 2 assessment indicated substantial methodological limitations in mHealth intervention studies for cardiovascular medication adherence, with only a small proportion of studies rated as having a low risk of bias. Therefore, the findings of this study should be interpreted with caution, particularly considering the potential impact of methodological limitations on the validity of the results. Although randomization procedures were generally well conducted, major sources of bias were related to deviations from intended interventions, outcome measurement, and selective reporting, which may compromise internal validity. These findings highlight the need for future mHealth research to strengthen intervention fidelity and adopt standardized and well-validated outcome measures, including objective adherence metrics and appropriate blinding [92]. Furthermore, strict adherence to transparent outcome reporting and preregistration standards is essential to enhance the credibility and reproducibility of study results in research on cardiovascular medication adherence [93]. Systematic attention to the RoB 2 domains will promote higher methodological standards and stronger evidence for mHealth interventions in cardiovascular medication adherence.
Regarding publication bias, Egger’s regression test did not indicate significant publication bias. This finding suggests that the pooled estimates are unlikely to be substantially influenced by reporting bias.
This systematic review and meta-analysis had several limitations. First, although only RCTs were included, some were pilot trials with relatively small sample sizes, which may have led to imprecise effect estimates. Second, medication adherence is inherently difficult to measure objectively as a primary outcome. The included studies employed a wide range of medication adherence tools, many of which relied on self-reports, potentially increasing the risk of bias compared with more objective outcomes. This heterogeneity in outcome measurements also limited the number of studies eligible for certain subgroup analyses. Finally, limitations related to the review process should be acknowledged, considering that only English-language publications were included and a limited number of databases were searched. This restriction may have introduced potential selection bias and limited the comprehensiveness of the included studies.
Despite these limitations, this review has several notable strengths. All included studies were RCTs, which is the strongest design for evaluating intervention effectiveness because randomization reduces bias and strengthens causal inference. Furthermore, this review focused on a clearly defined outcome, medication adherence, thereby enhancing the interpretability of the findings. Repeated consensus discussions among four reviewers strengthened the rigor of the risk of bias assessment and enhanced the consistency and credibility of the evaluations. Furthermore, the subgroup analyses provided comprehensive information regarding intervention type, duration, and outcome measurement tools.
This systematic review synthesized evidence from 52 studies and found that mHealth interventions are effective in improving medication adherence in patients with CVD. The findings support the potential role of mHealth technology in improving medication adherence. mHealth education programs to improve medication adherence could be effective in patients with CVD. Furthermore, the findings indicate that healthcare professionals, including staff nurses and nurse practitioners, should adopt more standardized measurement tools, such as the HBCS or MMAS, and complement self-report measures with objective indicators when feasible.
Despite these promising findings, the evidence base remains heterogeneous, underscoring the need for continued high-quality systematic reviews and well-designed trials. Future research should prioritize rigorous study designs and explore emerging technologies, including artificial intelligence and virtual reality-based mHealth interventions, to enhance medication adherence among patients with CVD.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgements

None.

Funding

This research was supported by Kyungpook National University Research Fund, 2023.

Data Sharing Statement

Please contact the corresponding author for data availability.

Supplementary Data

Supplementary data to this article can be found online at https://doi.org/10.4040/jkan.26016.

Supplementary Appendix 1. Search strategy to identify relevant studies across databases

jkan-26016-Supplementary-Appendix-1.pdf

Supplementary Appendix 2. Included studies in systematic review

jkan-26016-Supplementary-Appendix-2.pdf

Supplementary Table 1. Results of subgroup analyses by intervention type, intervention duration, and outcome measurement tools

jkan-26016-Supplementary-Table-1.pdf

Supplementary Figure 1. Funnel plot

jkan-26016-Supplementary-Figure-1.pdf

Author Contributions

Conceptualization: YS, SP, SL, YL. Data curation: YS, SP, SL, YL. Final approval of the manuscript: all authors. Formal analysis: YS, SP, SL, YL. Funding acquisition: YS. Investigation: YS. Methodology: YS, SP, SL, YL. Project administration: YS. Resources: SP, SL. Software: SP, SL. Supervision: YS. Validation: YS, SP, SL, YL. Visualization: SP, SL. Writing–original draft: YS, SP, SL, YL. Writing–review & editing: YS, SP, SL, YL.

Fig. 1.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flow diagram of study selection for the systematic review and meta-analysis.
jkan-26016f1.jpg
Fig. 2.
Risk of bias assessment of included studies.
jkan-26016f2.jpg
Fig. 3.
Forest plot of the overall effect size of mHealth interventions on medication adherence in patients with cardiovascular disease. CI, confidence interval; SD, standard deviation; SMD, standardized mean difference.
jkan-26016f3.jpg
Table 1.
Descriptive summary of the included studies (N=52)
Characteristic Category n (%)
Publication year 2015–2019 15 (28.8)
2020–2026 37 (71.2)
Publication country China 12 (23.1)
USA 10 (19.2)
Australia 4 (7.7)
South Korea 3 (5.8)
Other countriesa) 23 (44.2)
Study design RCT 43 (82.7)
Pilot RCT 4 (7.7)
Othersb) 5 (9.6)
Participants Hypertension 23 (44.2)
Coronary heart disease 8 (15.4)
Atrial fibrillation 6 (11.5)
Acute coronary syndrome 4 (7.7)
Coronary artery disease 3 (5.8)
Heart failure 3 (5.8)
Mixed or general CVDc) 5 (9.6)
Sample size <100 16 (30.8)
100–299 20 (38.4)
300–999 12 (23.1)
≥1,000 4 (7.7)
Type of intervention Text message-based interventions 20 (38.5)
Smartphone application-based interventions 22 (42.3)
Multicomponent mHealth interventions 10 (19.2)
Duration of intervention (wk) ≤12 25 (48.1)
13–24 12 (23.1)
≥25 15 (28.8)
Comparator Usual or standard care 41 (78.8)
Active comparator 7 (13.5)
No intervention 4 (7.7)
Outcome measurement tools Morisky-based tools 22 (42.3)
Hill-Bone Compliance Scale 5 (9.6)
Other self-reported scales 16 (30.8)
Objective measures 9 (17.3)

CVD, cardiovascular disease; RCT, randomized controlled trial.

a)Other countries include Jordan, Pakistan, Colombia, Nepal, South Africa, Brazil, Spain, Belgium, Canada, Iran, Taiwan, Palestine, Malaysia, Germany, Chile, and Turkey, as well as one multinational study. b)Others include cluster RCTs, crossover RCTs, and mixed-methods RCTs. c)Mixed or general CVD includes general CVD and combined conditions such as acute coronary syndrome, heart failure, or mechanical valve replacement.

Table 2.
Characteristics of studies included in the systematic review (N=52)
Author (year), country Study design Participant/sample size Intervention type Duration (wk) Comparator Outcome measurement tools
Abel et al. [33] (2023), USA Pilot RCT Black women diagnosed with hypertension (N=77; EG=41, CG=36) Interactive technology-enhanced coaching (wearable monitoring and coaching with integrated data tracking) 36 Active comparator Self-reported MA
Abu-El-Noor et al. [34] (2021), Palestine RCT Patients with hypertension (N=191; EG=97, CG=94) Mobile app with medication reminders, education, and BP self-monitoring 12 Usual care HBCS
Akhu-Zaheya et al. [35] (2017), Jordan RCT Patients with CVD (N=160; EG=52, Placebo=52, CG=56) SMS (adherence, diet, and smoking cessation) vs. general messages (placebo) 12 Usual care MMAS-8
Arshed et al. [36] (2024), Pakistan RCT Patients with hypertension (N=423; EG=214, CG=209) Mobile app-based reminders and education 24 Standard care Pill count
Bae et al. [37] (2021), South Korea RCT Older patients with CHD (N=879; EG=440, CG=439) One-way SMS and website support 24 Standard care MMS
Bermon et al. [38] (2021), Colombia RCT Patients with atherosclerotic CVD (N=930; EG=462, CG=468) SMS (education and behavior change) 52 Active comparator MMAS-8
Bhandari et al. [39] (2022), Nepal Pilot RCT Patients with hypertension (N=200; EG=100, CG=100) SMS (education, reminders, and tailored messages) 12 Standard care HBCS
Bobrow et al. [40] (2016), South Africa RCT Adults with high BP (N=1,372; EG1=457, EG2=458, CG=457) One-way SMS (EG1) vs. interactive SMS (EG2) 52 Usual care PDC
Bozorgi et al. [41] (2021), Colombia RCT Patients with hypertension (N=120; EG=60, CG=60) Mobile app for BP management 8 Usual care HBCS
Buis et al. [42] (2024), USA RCT Patients with uncontrolled hypertension (N=87; EG=44, CG=43) Mobile app with BP monitoring and pedometer 48 Usual care ARMS-14
Canguçu et al. [43] (2024), Brazil Crossover RCT Patients with hypertension (N=155; EG=77, CG=78) Text messages with and without reminders 12 No intervention BMQ
Chandler et al. [44] (2019), USA RCT Hispanic adults with uncontrolled hypertension (N=54; EG=26, CG=28) Smartphone app with medication adherence stops hypertension 36 Active comparator MMAS
Chen et al. [45] (2019), China RCT Patients with CHF (N=767; EG1=252, EG2=255, CG=260) SMS (EG1) vs. structured telephone support (EG2) 26 Usual care Self-reported MA
Chow et al. [46] (2022), Australia RCT Patients with ACS (N=95; EG=52, CG=43) Text message-based cardiac education and support 52 Usual care Self-reported MA
Márquez Contreras et al. [47] (2019), Spain RCT Participant with hypertension (N=154; EG=77, CG=77) Smartphone app for hypertension management 52 Usual care Electronic MEMS
Desteghe et al. [48] (2025), Belgium RCT Patients with AF (N=1,038; EG1=345, EG2=347, CG=346) In-person education (EG1), online education (EG2) 4,12,24,48 (every 24-wk until study end) Standard care Electronic MEMS
Ding et al. [49] (2024), China RCT Patients with mechanical valve replacement (N=84; EG=41, CG=43) Mobile app for self-monitoring, medication reminders, and nurse-supported follow-up care 24 Standard care MMAS-8
Eynan et al. [50] (2024), Canada Pilot study Patients with CHF (N=54; EG1=14, EG2 =13, EG3=12, CG=15) Text messages for education and medication reminders (EG1) vs. coaching (EG2) vs. combined intervention (EG3) 12 Usual care MMAS-8
Fang & Li [51] (2016), China RCT Outpatients with CAD (N=271; EG1=91, EG2=90 EG3=90) SMS reminders and education material via SMS alone (EG1) vs. SMS and micro letter (EG2) vs. phone (EG3) 24 Active comparator MMAS
FarzanehRad et al. [52] (2024), Iran RCT Patients with HF (N=159; EG1=50, EG2=54, CG=55) Tailored text messaging (EG1) vs. pillbox organizers (EG2) 12 Usual care MARS, pill count
Fu et al. [53] (2025), China RCT Patients with CHD (N=60; EG=30, CG=30) Internet-based continuous nursing with multidisciplinary support 24 Standard care MMAS
Ghafouri et al. [54] (2024), Iran RCT Persons with cardiac disease (N=121; EG=58, CG=63) Mobile app with education, risk assessment, and feedback 3 Usual care MAQ
Gong et al [55] (2020), China RCT Patients with hypertension (N=443; EG=225, CG=218) Yan Fu app with self-monitoring, reminders, education, and alerts 24 Usual care MMAS-8
Hsieh et al. [56] (2021), Taiwan RCT Patients with AF (N=231; EG=115, CG=116) Web-based integrated management with education, monitoring, and multidisciplinary support 24 Usual care MARS
Indraratna et al. [57] (2022), Australia Pilot RCT Patients with ACS or HF (N=164; EG=81, CG=83) TeleClinical Care smartphone app with telemonitoring and educational messaging 28 Usual care MGL
Kamal et al. [58] (2018), Pakistan RCT Patients with CAD (N=100; EG=49, CG=51) Interactive voice response-based tailored medication reminders and education 12 Usual care MMAS-8
Kha et al. [59] (2025), Australia RCT Patients with ACS (N=1,379; EG=697, CG=682) SMS (medication adherence and lifestyle change) 48 Standard care Self-reported adherence based on missed days
Khonsari et al. [60] (2015), Malaysia Mixed-methods RCT Patients with ACS (N=62; EG=31, CG=31) Automated web-based system for SMS management 8 Usual care MMAS-8
Khonsari et al. [61] (2020), Iran Mixed-methods RCT Patients with CHD (N=78; EG=39, CG=39) SMS medication reminders 12 Usual care MMAS-8
Kim et al. [62] (2016), USA RCT Patients with hypertension (N=95; EG=52, CG=43) Wireless self-monitoring platform with app support and nurse monitoring 24 Standard care MMAS-8
Krackhardt et al. [63] (2023), Germany RCT Patients with ACS (N=676; EG=342, CG=334) Smartphone app with reminders and motivational messages 4 No intervention BAQ
Lao et al. [64] (2023), China RCT Patients with CHD (N=140; EG=70, CG=70) Mobile health app for cardiac rehabilitation 12 Usual care Pill count
Magnani et al. [65] (2025), USA RCT Patients with AF (N=243; EG=123, CG=120) Smartphone app with a relational agent for education, adherence monitoring, self-care, and heart rate and rhythm monitoring 48 Active comparator PDC
Mehta et al. [66] (2024), USA RCT Patients with hypertension (N=86; EG1=35, EG2=36, CG=15) Bidirectional SMS monitoring 12 Usual care Self-reported MA
Meyer et al. [67] (2025), Germany RCT Patients with hypertension (N=102; EG=52, CG=50) Internet-based cognitive behavioral therapy, lifestyle counseling, and self-monitoring 12 Usual care RAI
Morawski et al. [68] (2018), USA RCT Patients with uncontrolled hypertension (N=411; EG=209, CG=202) Medisafe smartphone app 12 Usual care MMAS-8
Ni et al. [69] (2018), China RCT Patients with CHD (N=50; EG=25, CG=25) Text messages (education via WeChat and medication reminders) 4 Usual care VES
Ni et al. [70] (2022), China RCT Patients with CHD (N=196; EG=103, CG=93) Messaging intervention (reminders and education) 9 Usual care VES
Park et al. [71] (2015), USA RCT Patients with CHD (N=90; EG1=30, EG2=30, CG=30) SMS reminders and education (EG1) vs. SMS (education only) 4 No intervention MMAS-8
Persell et al. [72] (2020), USA RCT Participant with uncontrolled hypertension (N=333; EG=166, CG=167) Smartphone coaching app with home BP monitoring 24 Active comparator Self-reported MA
Santo et al. [73] (2019), Australia RCT Patients with CHD (N=163; EG=107, CG=56) Medication reminder smartphone apps 12 Usual care MMAS-8
Schwalm et al. [74] (2019), Colombia and Malaysia Cluster RCT Patients with hypertension (N=1,371; EG=740, CG=631) Tablet-based management algorithm, counselling, and adherence support 48 Usual care MMAS-8
Shi et al. [75] (2025), China RCT Patients with AF (N=208, EG=104, CG=104) Digital animation-based education 12 Standard care MARS-5
Still et al. [76] (2020), USA RCT African Americans with hypertension (N=60; EG=30, CG=30) Community and technology-based hypertension self-management 12 Usual care HBCS
Ullrich et al. [77] (2025), Germany RCT Patients with CAD (N=240; EG=121, CG=119) PreventiPlaque app for lifestyle change and adherence support 48 Standard care Self-assessment of MA
Varleta et al. [78] (2017), Chile RCT Patients with hypertension (N=314; EG=163, CG=151) SMS (education for medication adherence) 24 No intervention MGL
Xu et al. [79] (2024), China RCT Patients with AF (N=96; EG=48, CG=48) Alfalfa app for comprehensive AF management 12 Standard care MMAS-8
Yang et al. [80] (2023), China RCT Patients with hypertension (N=368; EG=184, CG=184) Transitional care with WeChat support, telephone follow-up, and home visits 52 Usual care Self-designed questionnaire
Yildirim Keskin et al. [81] (2025), Turkey RCT Patients with hypertension (N=80; EG=40, CG=40) Mobile app with self-monitoring, medication reminders, education, and feedback 5 Standard care HBCS
Yoon et al. [82] (2024), South Korea RCT Patients with AF (N=498; EG=248, CG=250) App-based feedback for edoxaban adherence support 24 Standard care Pill count
Yoon et al. [83] (2025), South Korea RCT Patients with uncontrolled hypertension (N=154; EG=79, CG=75) BP self-monitoring app with feedback 24 Active comparator Pill count
Zhai et al. [84] (2020), China Cluster RCT Patients with hypertension (N=384; EG=192, CG=192) SMS and personal consultation 12 Usual care MMAS-8

ACS, acute coronary syndrome; AF, atrial fibrillation; Apps, applications; ARMS-14, adherence to refills and medication scale; BAQ, Brilique Adherence Questionnaire; BMQ, Brief Medication Questionnaire; BP, blood pressure; CAD, coronary artery disease; CG, control group; CHD, coronary heart disease; CHF, chronic heart failure; CVD, cardiovascular disease; EG, experimental group; HBCS, Hill-Bone Compliance Scale; HF, heart failure; MA, medication adherence; MAQ, Medication Adherence Questionnaire; MARS, Medication Adherence Rating Scale; MEMS, medication event monitoring system; MGL, Morisky-Green-Levine; MMAS, Morisky Medication Adherence Scale; MMAS-8, Morisky Medication Adherence Scale-8; MMS, Modified Morisky Scale; N, total sample size; PCD, proportion of days covered; RAI, Rief Adherence Index; RCT, randomized controlled trial; SMS, short message service; VES, Voils Medication Non-Adherence Extent Scale.

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      Effectiveness of mobile health interventions to improve medication adherence for patients with cardiovascular disease: a systematic review and meta-analysis
      Image Image Image
      Fig. 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flow diagram of study selection for the systematic review and meta-analysis.
      Fig. 2. Risk of bias assessment of included studies.
      Fig. 3. Forest plot of the overall effect size of mHealth interventions on medication adherence in patients with cardiovascular disease. CI, confidence interval; SD, standard deviation; SMD, standardized mean difference.

      Fig. 1.

      Fig. 2.

      Fig. 3.

      Effectiveness of mobile health interventions to improve medication adherence for patients with cardiovascular disease: a systematic review and meta-analysis
      Characteristic Category n (%)
      Publication year 2015–2019 15 (28.8)
      2020–2026 37 (71.2)
      Publication country China 12 (23.1)
      USA 10 (19.2)
      Australia 4 (7.7)
      South Korea 3 (5.8)
      Other countriesa) 23 (44.2)
      Study design RCT 43 (82.7)
      Pilot RCT 4 (7.7)
      Othersb) 5 (9.6)
      Participants Hypertension 23 (44.2)
      Coronary heart disease 8 (15.4)
      Atrial fibrillation 6 (11.5)
      Acute coronary syndrome 4 (7.7)
      Coronary artery disease 3 (5.8)
      Heart failure 3 (5.8)
      Mixed or general CVDc) 5 (9.6)
      Sample size <100 16 (30.8)
      100–299 20 (38.4)
      300–999 12 (23.1)
      ≥1,000 4 (7.7)
      Type of intervention Text message-based interventions 20 (38.5)
      Smartphone application-based interventions 22 (42.3)
      Multicomponent mHealth interventions 10 (19.2)
      Duration of intervention (wk) ≤12 25 (48.1)
      13–24 12 (23.1)
      ≥25 15 (28.8)
      Comparator Usual or standard care 41 (78.8)
      Active comparator 7 (13.5)
      No intervention 4 (7.7)
      Outcome measurement tools Morisky-based tools 22 (42.3)
      Hill-Bone Compliance Scale 5 (9.6)
      Other self-reported scales 16 (30.8)
      Objective measures 9 (17.3)
      Author (year), country Study design Participant/sample size Intervention type Duration (wk) Comparator Outcome measurement tools
      Abel et al. [33] (2023), USA Pilot RCT Black women diagnosed with hypertension (N=77; EG=41, CG=36) Interactive technology-enhanced coaching (wearable monitoring and coaching with integrated data tracking) 36 Active comparator Self-reported MA
      Abu-El-Noor et al. [34] (2021), Palestine RCT Patients with hypertension (N=191; EG=97, CG=94) Mobile app with medication reminders, education, and BP self-monitoring 12 Usual care HBCS
      Akhu-Zaheya et al. [35] (2017), Jordan RCT Patients with CVD (N=160; EG=52, Placebo=52, CG=56) SMS (adherence, diet, and smoking cessation) vs. general messages (placebo) 12 Usual care MMAS-8
      Arshed et al. [36] (2024), Pakistan RCT Patients with hypertension (N=423; EG=214, CG=209) Mobile app-based reminders and education 24 Standard care Pill count
      Bae et al. [37] (2021), South Korea RCT Older patients with CHD (N=879; EG=440, CG=439) One-way SMS and website support 24 Standard care MMS
      Bermon et al. [38] (2021), Colombia RCT Patients with atherosclerotic CVD (N=930; EG=462, CG=468) SMS (education and behavior change) 52 Active comparator MMAS-8
      Bhandari et al. [39] (2022), Nepal Pilot RCT Patients with hypertension (N=200; EG=100, CG=100) SMS (education, reminders, and tailored messages) 12 Standard care HBCS
      Bobrow et al. [40] (2016), South Africa RCT Adults with high BP (N=1,372; EG1=457, EG2=458, CG=457) One-way SMS (EG1) vs. interactive SMS (EG2) 52 Usual care PDC
      Bozorgi et al. [41] (2021), Colombia RCT Patients with hypertension (N=120; EG=60, CG=60) Mobile app for BP management 8 Usual care HBCS
      Buis et al. [42] (2024), USA RCT Patients with uncontrolled hypertension (N=87; EG=44, CG=43) Mobile app with BP monitoring and pedometer 48 Usual care ARMS-14
      Canguçu et al. [43] (2024), Brazil Crossover RCT Patients with hypertension (N=155; EG=77, CG=78) Text messages with and without reminders 12 No intervention BMQ
      Chandler et al. [44] (2019), USA RCT Hispanic adults with uncontrolled hypertension (N=54; EG=26, CG=28) Smartphone app with medication adherence stops hypertension 36 Active comparator MMAS
      Chen et al. [45] (2019), China RCT Patients with CHF (N=767; EG1=252, EG2=255, CG=260) SMS (EG1) vs. structured telephone support (EG2) 26 Usual care Self-reported MA
      Chow et al. [46] (2022), Australia RCT Patients with ACS (N=95; EG=52, CG=43) Text message-based cardiac education and support 52 Usual care Self-reported MA
      Márquez Contreras et al. [47] (2019), Spain RCT Participant with hypertension (N=154; EG=77, CG=77) Smartphone app for hypertension management 52 Usual care Electronic MEMS
      Desteghe et al. [48] (2025), Belgium RCT Patients with AF (N=1,038; EG1=345, EG2=347, CG=346) In-person education (EG1), online education (EG2) 4,12,24,48 (every 24-wk until study end) Standard care Electronic MEMS
      Ding et al. [49] (2024), China RCT Patients with mechanical valve replacement (N=84; EG=41, CG=43) Mobile app for self-monitoring, medication reminders, and nurse-supported follow-up care 24 Standard care MMAS-8
      Eynan et al. [50] (2024), Canada Pilot study Patients with CHF (N=54; EG1=14, EG2 =13, EG3=12, CG=15) Text messages for education and medication reminders (EG1) vs. coaching (EG2) vs. combined intervention (EG3) 12 Usual care MMAS-8
      Fang & Li [51] (2016), China RCT Outpatients with CAD (N=271; EG1=91, EG2=90 EG3=90) SMS reminders and education material via SMS alone (EG1) vs. SMS and micro letter (EG2) vs. phone (EG3) 24 Active comparator MMAS
      FarzanehRad et al. [52] (2024), Iran RCT Patients with HF (N=159; EG1=50, EG2=54, CG=55) Tailored text messaging (EG1) vs. pillbox organizers (EG2) 12 Usual care MARS, pill count
      Fu et al. [53] (2025), China RCT Patients with CHD (N=60; EG=30, CG=30) Internet-based continuous nursing with multidisciplinary support 24 Standard care MMAS
      Ghafouri et al. [54] (2024), Iran RCT Persons with cardiac disease (N=121; EG=58, CG=63) Mobile app with education, risk assessment, and feedback 3 Usual care MAQ
      Gong et al [55] (2020), China RCT Patients with hypertension (N=443; EG=225, CG=218) Yan Fu app with self-monitoring, reminders, education, and alerts 24 Usual care MMAS-8
      Hsieh et al. [56] (2021), Taiwan RCT Patients with AF (N=231; EG=115, CG=116) Web-based integrated management with education, monitoring, and multidisciplinary support 24 Usual care MARS
      Indraratna et al. [57] (2022), Australia Pilot RCT Patients with ACS or HF (N=164; EG=81, CG=83) TeleClinical Care smartphone app with telemonitoring and educational messaging 28 Usual care MGL
      Kamal et al. [58] (2018), Pakistan RCT Patients with CAD (N=100; EG=49, CG=51) Interactive voice response-based tailored medication reminders and education 12 Usual care MMAS-8
      Kha et al. [59] (2025), Australia RCT Patients with ACS (N=1,379; EG=697, CG=682) SMS (medication adherence and lifestyle change) 48 Standard care Self-reported adherence based on missed days
      Khonsari et al. [60] (2015), Malaysia Mixed-methods RCT Patients with ACS (N=62; EG=31, CG=31) Automated web-based system for SMS management 8 Usual care MMAS-8
      Khonsari et al. [61] (2020), Iran Mixed-methods RCT Patients with CHD (N=78; EG=39, CG=39) SMS medication reminders 12 Usual care MMAS-8
      Kim et al. [62] (2016), USA RCT Patients with hypertension (N=95; EG=52, CG=43) Wireless self-monitoring platform with app support and nurse monitoring 24 Standard care MMAS-8
      Krackhardt et al. [63] (2023), Germany RCT Patients with ACS (N=676; EG=342, CG=334) Smartphone app with reminders and motivational messages 4 No intervention BAQ
      Lao et al. [64] (2023), China RCT Patients with CHD (N=140; EG=70, CG=70) Mobile health app for cardiac rehabilitation 12 Usual care Pill count
      Magnani et al. [65] (2025), USA RCT Patients with AF (N=243; EG=123, CG=120) Smartphone app with a relational agent for education, adherence monitoring, self-care, and heart rate and rhythm monitoring 48 Active comparator PDC
      Mehta et al. [66] (2024), USA RCT Patients with hypertension (N=86; EG1=35, EG2=36, CG=15) Bidirectional SMS monitoring 12 Usual care Self-reported MA
      Meyer et al. [67] (2025), Germany RCT Patients with hypertension (N=102; EG=52, CG=50) Internet-based cognitive behavioral therapy, lifestyle counseling, and self-monitoring 12 Usual care RAI
      Morawski et al. [68] (2018), USA RCT Patients with uncontrolled hypertension (N=411; EG=209, CG=202) Medisafe smartphone app 12 Usual care MMAS-8
      Ni et al. [69] (2018), China RCT Patients with CHD (N=50; EG=25, CG=25) Text messages (education via WeChat and medication reminders) 4 Usual care VES
      Ni et al. [70] (2022), China RCT Patients with CHD (N=196; EG=103, CG=93) Messaging intervention (reminders and education) 9 Usual care VES
      Park et al. [71] (2015), USA RCT Patients with CHD (N=90; EG1=30, EG2=30, CG=30) SMS reminders and education (EG1) vs. SMS (education only) 4 No intervention MMAS-8
      Persell et al. [72] (2020), USA RCT Participant with uncontrolled hypertension (N=333; EG=166, CG=167) Smartphone coaching app with home BP monitoring 24 Active comparator Self-reported MA
      Santo et al. [73] (2019), Australia RCT Patients with CHD (N=163; EG=107, CG=56) Medication reminder smartphone apps 12 Usual care MMAS-8
      Schwalm et al. [74] (2019), Colombia and Malaysia Cluster RCT Patients with hypertension (N=1,371; EG=740, CG=631) Tablet-based management algorithm, counselling, and adherence support 48 Usual care MMAS-8
      Shi et al. [75] (2025), China RCT Patients with AF (N=208, EG=104, CG=104) Digital animation-based education 12 Standard care MARS-5
      Still et al. [76] (2020), USA RCT African Americans with hypertension (N=60; EG=30, CG=30) Community and technology-based hypertension self-management 12 Usual care HBCS
      Ullrich et al. [77] (2025), Germany RCT Patients with CAD (N=240; EG=121, CG=119) PreventiPlaque app for lifestyle change and adherence support 48 Standard care Self-assessment of MA
      Varleta et al. [78] (2017), Chile RCT Patients with hypertension (N=314; EG=163, CG=151) SMS (education for medication adherence) 24 No intervention MGL
      Xu et al. [79] (2024), China RCT Patients with AF (N=96; EG=48, CG=48) Alfalfa app for comprehensive AF management 12 Standard care MMAS-8
      Yang et al. [80] (2023), China RCT Patients with hypertension (N=368; EG=184, CG=184) Transitional care with WeChat support, telephone follow-up, and home visits 52 Usual care Self-designed questionnaire
      Yildirim Keskin et al. [81] (2025), Turkey RCT Patients with hypertension (N=80; EG=40, CG=40) Mobile app with self-monitoring, medication reminders, education, and feedback 5 Standard care HBCS
      Yoon et al. [82] (2024), South Korea RCT Patients with AF (N=498; EG=248, CG=250) App-based feedback for edoxaban adherence support 24 Standard care Pill count
      Yoon et al. [83] (2025), South Korea RCT Patients with uncontrolled hypertension (N=154; EG=79, CG=75) BP self-monitoring app with feedback 24 Active comparator Pill count
      Zhai et al. [84] (2020), China Cluster RCT Patients with hypertension (N=384; EG=192, CG=192) SMS and personal consultation 12 Usual care MMAS-8
      Table 1. Descriptive summary of the included studies (N=52)

      CVD, cardiovascular disease; RCT, randomized controlled trial.

      a)Other countries include Jordan, Pakistan, Colombia, Nepal, South Africa, Brazil, Spain, Belgium, Canada, Iran, Taiwan, Palestine, Malaysia, Germany, Chile, and Turkey, as well as one multinational study. b)Others include cluster RCTs, crossover RCTs, and mixed-methods RCTs. c)Mixed or general CVD includes general CVD and combined conditions such as acute coronary syndrome, heart failure, or mechanical valve replacement.

      Table 2. Characteristics of studies included in the systematic review (N=52)

      ACS, acute coronary syndrome; AF, atrial fibrillation; Apps, applications; ARMS-14, adherence to refills and medication scale; BAQ, Brilique Adherence Questionnaire; BMQ, Brief Medication Questionnaire; BP, blood pressure; CAD, coronary artery disease; CG, control group; CHD, coronary heart disease; CHF, chronic heart failure; CVD, cardiovascular disease; EG, experimental group; HBCS, Hill-Bone Compliance Scale; HF, heart failure; MA, medication adherence; MAQ, Medication Adherence Questionnaire; MARS, Medication Adherence Rating Scale; MEMS, medication event monitoring system; MGL, Morisky-Green-Levine; MMAS, Morisky Medication Adherence Scale; MMAS-8, Morisky Medication Adherence Scale-8; MMS, Modified Morisky Scale; N, total sample size; PCD, proportion of days covered; RAI, Rief Adherence Index; RCT, randomized controlled trial; SMS, short message service; VES, Voils Medication Non-Adherence Extent Scale.

      Table 1.

      Table 2.

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