Effectiveness of mHealth Interventions in Medication Adherence among Patients with Cardiovascular Diseases: A Systematic Review

mHealth interventions have been reported to improve adherence to long-term therapies in chronic conditions. Therefore, this study aimed at determining the effectiveness of mHealth interventions in medication adherence among patients with cardiovascular diseases (CVDs), a leading cause of mortality globally. Relying on our inclusion criteria and the PRISMA recommendations, a literature search was carried out in the PubMed, Medline, and ProQuest databases for primary studies that investigated the impact of mHealth on medication adherence for cardiovascular disease (CVD) between 2000–2021. A total of 23 randomized controlled trials with 34,915 participants matched the selection criteria. The mHealth interventions used included text messages, mobile phone applications, and voice calls, which were used either as a single intervention or combined. Additionally, studies on enhancing drug adherence had contradictory findings: most of the studies elaborated positive results; however, six studies were unable to reveal any significant effect. Finally, a risk bias analysis revealed varying outcomes across all studies. This review, as a whole, supported the notion that mHealth interventions can be effective in improving adherence to CVD medication even though they could not improve adherence to all CVD medications when compared with controls. Further trials with more refined designs integrated with comprehensive interventions are needed to produce better health outcomes.


Introduction
Cardiovascular diseases (CVDs) account for 17.9 million deaths annually (31% of all deaths globally) [1]. It is a key contributor to premature deaths and escalating healthcare expenses [2,3]. Cardiovascular diseases are projected by Global Health to remain the leading cause of mortality in 2030 [4]. To reduce the burden of CVDs, adequate control of CVD risk factors is required; such factors include high blood pressure, excess body weight, high blood lipids, cigarette smoking, and diabetes. Elevated blood pressure has been reported to be the most significant contributing factor to CVDs [5]. It was indicated in the Global Burden of Disease study that each of the above-mentioned risk factors was among the top 10 causes of a loss in disability-adjusted life years [6].
Adherence is the degree to which an individual's behavior in taking medications, maintaining a diet, and implementing other lifestyle changes matches with accepted advice from a healthcare professional. Medication non-adherence is acknowledged as a leading healthcare problem that can be prevented and as a serious obstacle to improving clinical outcomes. Non-adherence to medication represents the leading cause of mortality for more than 60% of cardiovascular patients. Medication use is a complex activity that is influenced by a variety of elements that include experience, beliefs, and culture. Medication-taking

Quality Evaluation
Cochrane's risk-of-bias assessment tool was used to analyze the studies when evaluating the bias. Moreover, the Cochrane Handbook for Systematic Reviews of Interventions was used for the quality assessments of all trials. Allocation concealment, randomization, blinding concealment, attrition rates, sufficient data reporting, and non-selective reporting of outcomes were the markers used for assessing quality. A 2-point Likert scale (Yes (1) and No (0)) was used. Then, studies were categorized into three classes according to their reported score: (1) a score of more than 4, which indicated a good-quality study; (2) a score of 3-4, which indicated a moderate quality; and (3) a score less than 2, which indicated a poor quality.

Result
The search of the databases identified a total of 1254 studies that were screened to comply with the eligibility criteria. Studies were eliminated because the content of their titles and abstracts did not meet the inclusion criteria. This systematic review did not include studies that did not use adherence measurement as an endpoint. A good number of studies (58) were excluded due to the wrong populations (participants other than cardiovascular diseases), wrong interventions (interventions other than mHealth intervention), adherence to medication not reported as an endpoint, and for being systematic reviews. Twenty-three peer-reviewed journal articles met the inclusion criteria  and were taken into account in the analysis. The PRISMA flowchart is depicted in Figure 1.
Twenty-three peer-reviewed journal articles met the inclusion criteria  and were taken into account in the analysis. The PRISMA flowchart is depicted in Figure 1.

Characteristics of Trials
This section provides an overview of the authors, the study designs, the intervention subcategories, the types of intervention, the study country, the outcomes, and the adherence measurement tools. Adherence or correlates of adherence were used as the outcome measure. While the study findings provided the final results, the adherence measurement described how adherence was measured in each study. All of these were randomized controlled trials, including seven from the USA with multiple time durations; i.e., one month, 12 months, 28 days, 12 months, 3 months, 6 months, and 3 months, respectively; the sample sizes in these studies were: 90, 21,750, 60, 253, 126, 179, and 413, respectively [17,[19][20][21]29,32,33]. Two more studies from Canada had durations of 4 months and 12 months with sample sizes of 90 and 2632, respectively [35,36]. Three studies were conducted in China that were 6 months, 1 month, and 3 months of duration with sample sizes of 280, 50, and 445, respectively [24,31,37]. In addition, two studies were from Pakistan, each with a 3-month duration and with sample sizes of 200 and 201, respectively [23,30]. One more study was conducted in South Africa with a 12-month duration and a sample size of 1372 [25]. Some studies from other parts of world included the following: one from the United Kingdom with a duration of 6 months and a sample size of 301 [18]; one from Australia

Characteristics of Trials
This section provides an overview of the authors, the study designs, the intervention subcategories, the types of intervention, the study country, the outcomes, and the adherence measurement tools. Adherence or correlates of adherence were used as the outcome measure. While the study findings provided the final results, the adherence measurement described how adherence was measured in each study. All of these were randomized controlled trials, including seven from the USA with multiple time durations; i.e., one month, 12 months, 28 days, 12 months, 3 months, 6 months, and 3 months, respectively; the sample sizes in these studies were: 90, 21,750, 60, 253, 126, 179, and 413, respectively [17,[19][20][21]29,32,33]. Two more studies from Canada had durations of 4 months and 12 months with sample sizes of 90 and 2632, respectively [35,36]. Three studies were conducted in China that were 6 months, 1 month, and 3 months of duration with sample sizes of 280, 50, and 445, respectively [24,31,37]. In addition, two studies were from Pakistan, each with a 3-month duration and with sample sizes of 200 and 201, respectively [23,30]. One more study was conducted in South Africa with a 12-month duration and a sample size of 1372 [25]. Some studies from other parts of world included the following: one from the United Kingdom with a duration of 6 months and a sample size of 301 [18]; one from Australia with a 3-month duration and a sample size of 165 [34]; one from France with a 1-month duration and a sample size of 5546 [16]; one from Malaysia with a 2-month duration and a sample size 62 [22]; one from Iran with a 3-month duration and a sample size of 123 [26]; and one from New Zealand with a duration of 12 months and a sample size of 306 [38] (Table 1).   In the intervention group, patient satisfaction was also higher.

Types of Intervention
The types of mobile phone-based interventions varied from single SMS interventions [16][17][18]22,23,25,37] to a combination of SMS + Micro Letter (ML) (Micro Letter platforms are open-access Kik Messenger-like programs that provide users in China with access to news and other information) [24]; interactive voice interventions [19]; a multifaceted intervention including medication reconciliation and tailoring; patient education; collaborative care between a pharmacist and a patient's primary care clinician and voice messaging [21]; interactive text messages [25]; wireless self-monitoring devices [27]; a webbased app using interactive patient assistance tools [28]; individual and peer comparison of reminder alarms [29]; talking treatment intervention that involved SMS and voice calls [30]; WeChat + a BlackBerry reminder app [31]; an alert electronic reminder device (wireless pill bottle) with an automated message sent to the individual via email, text, or automated phone call [32]; and advanced mobile apps [33,34]. One study in Canada used mail-outs and mail-outs plus phone calls [36]; a qualitative study used engagement of intervention and control groups for positive and negative adherence [35]; and Text4HeartII featured educational and motivating materials to promote medication use [38] (as explained in Table 1).

Hypertension
In this review, nine studies were found that used blood pressure as an outcome measure along with adherence to medication [18,19,21,23,25,27,31,33,37]. Three trials elaborated a significant decrease in blood pressure and improved medication adherence [25,27,37]. At the same time, six trials could not provide any evidence of a decrease in blood pressure [18,19,21,23,31,33].

Heart Failure
In this review, only one study assessed the impact of mHealth on medication adherence in patients with heart failure [20]. This study, which had 60 participants and a 28-day duration, was a four-arm trial with two arms using the ePill box silent and reminder vs. smartphone silent and reminder. No improvement in adherence to medication was documented. mHealth has been utilized in patients with peripheral arterial disease. However, in this review, no particular study was found to determine the impact of mHealth on adherence to medication in patients with peripheral arterial disease.

Stroke
Two studies evaluated the impact of mHealth on medication adherence in patients with stroke. They were conducted in Pakistan with sample sizes of 200 and 201 participants and a duration of three months [23,30]. One reported a significant improvement in medication adherence [23], while the other failed to show substantial results [30].

Diabetes Mellitus
One trial evaluated the effect of mHealth intervention on adherence to medication in patients with cardiovascular disease comorbidity of type 2 diabetes mellitus. The trial used three groups: interactive voice recognition phone calls in group 1; interactive voice recognition enhanced phone calls in group 2; and a control group with usual care in group 3. Both intervention groups showed results that were 2.2 percentage points higher than those of the control group (95% CI, 1.1-3.4) with a difference of 3.0 (95% CI, 1.9-4.2) [19].

Risk of Bias and Methodological Quality Assessment
Seven domains were scored as low, high, and unclear risk of bias. Fifteen trials were considered high-quality studies because they received good scores across at least five domains [17,19,21,23,25,26,29,30,[32][33][34][35][36][37][38], while six trials achieved good scores in less than five with a moderate quality [16,18,20,22,27,28], and two received good scores in only two of the seven domains and had a poor quality [24,31]. The bias in the included studies was systematically assessed and is presented in Figure 2.

Stroke
Two studies evaluated the impact of mHealth on medication adherence in patients with stroke. They were conducted in Pakistan with sample sizes of 200 and 201 participants and a duration of three months [23,30]. One reported a significant improvement in medication adherence [23], while the other failed to show substantial results [30].

Diabetes Mellitus
One trial evaluated the effect of mHealth intervention on adherence to medication in patients with cardiovascular disease comorbidity of type 2 diabetes mellitus. The trial used three groups: interactive voice recognition phone calls in group 1; interactive voice recognition enhanced phone calls in group 2; and a control group with usual care in group 3. Both intervention groups showed results that were 2.2 percentage points higher than those of the control group (95% CI, 1.1-3.4) with a difference of 3.0 (95% CI, 1.9-4.2) [19].

Risk of Bias and Methodological Quality Assessment
Seven domains were scored as low, high, and unclear risk of bias. Fifteen trials were considered high-quality studies because they received good scores across at least five domains [17,19,21,23,25,26,29,30,[32][33][34][35][36][37][38], while six trials achieved good scores in less than five with a moderate quality [16,18,20,22,27,28], and two received good scores in only two of the seven domains and had a poor quality [24,31]. The bias in the included studies was systematically assessed and is presented in Figure 2.

Discussion
In recent times, technology-based interventional approaches such as mHealth, eHealth, and telehealth have become essential tools. mHealth technology is at the same time assessable, faster, and acceptable to the community.
Findings from previous studies showed improvement in medication adherence in patients after mobile health interventions. mHealth, which has gained popularity in recent times, has attained amassed interest as a tool for enhancing health promotion interventions as well as the provision of CVD prevention in an accessible manner with a relatively lower cost. Moreover, mHealth has a specific potential to promote lifestyle modification and is an effective tool for improving adherence [39].
In the present review, studies on enhancing drug adherence had contradictory findings: 17 trials (73.9%) reported a significant improvement in medication adherence, whereas 6 studies (26%) failed to show a substantial impact of the intervention on medication adherence. The effectiveness of mHealth interventions on cardiovascular medication adherence has also been reported in other systematic reviews, which showed that the majority of studies reported an improvement in medication adherence and other health outcomes [39,40]. Similar findings from an existing body of literature strengthen the current review's evidence [41][42][43].
In the current review, most of the studies used SMS only as an intervention, while some trials used SMS in combination with Micro Letters or voice calls. In a meta-analysis review, patients who received SMS-based interventions were found more likely to be adequately adherent than the control group [44]. mHealth apps (mobile and web-based) were also another popular mHealth intervention identified in the current review. A review was conducted to assess the impact of mHealth apps on adherence to CVD that revealed mixed results with reasonable acceptability [45]. As such, the two commonly used modes of mHealth delivery are text messaging (short messaging service or SMS) and smartphone applications [39]. In the present review, seven studies employed the use of SMS to improve medication adherence, while five studies employed mHealth-based apps. Both SMS and phone applications have the ability to serve the role of a drug intake reminder, blood pressure monitor, or routine clinic appointment reminder [40].
The current review identified other mHealth technologies used that included phone calls, interactive patient support apps, interactive text messages, interactive voice calls, wireless self-monitoring gadgets, electronic pill bottle reminder devices, emails, automated messages, and phone calls. Similarly, numerous effective delivery strategies were identified in other systematic reviews; these included SMS [46,47], automated phone messages, and reminders [48].
However, in certain cases, the reported beneficial outcome was not always a result of the patient's choice of a particular tool. A study compared different interventions to determine their effects on adherence to medication. The study used various tools that included pillboxes and smartphones. Smartphones reported a higher adherence score than pill counts [20]. Moreover, a multifaceted intervention medication reconciliation and tailoring, patient education, collaborative care between a pharmacist and a patient's primary care clinician, and voice messaging were used [21].
A meta-analysis of mHealth intervention to improve adherence in chronic diseases such as diabetes mellitus, asthma, cardiovascular diseases, epilepsy, and HIV infection showed improvement in medication adherence; this included 16 randomized controlled trials [49]. mHealth intervention had a positive impact on improving antiretroviral therapies in HIV patients and in antituberculosis treatment [49]; however, cardiovascular diseases require further refined designs.
mHealth is a more personalized method that helps individuals in managing their condition using an apt and standard design form. It has also been found to be an efficient tool in various studies of real-time data analysis. A study showed interest in smartwatches because they are a potential tool and have multifunctional dimensions like a smartphone.
The study presented a smartwatch-based medication reminder system and also introduced an early prototype to improve medication adherence [50]. Furthermore, it could help the clinicians to improve adherence to treatment, which could be beneficial to the the patient [51]. This has been found in different studies in which medication adherence was measured using different designs, sample sizes, intervention approaches of mHealth, adherence measuring tools, outcome measures, and the multitude of methods.
Mobile health strategies are instrumental in addressing the issue of medication nonadherence, which has been a challenge in the management of chronic diseases. Despite the positive feedback reported regarding the effectiveness of mHealth interventions in adherence to cardiovascular medications, more studies on the cost-effectiveness of this intervention type are required, especially in resource-limited settings. Some studies have stated that mHealth interventions are cost-effective; for instance, an mHealth intervention study in Kenya reported a marginal cost of USD 0.02 per SMS and a weekly SMS, which represented a cost of almost USD 1 per patient annually. This suggested that mHealth intervention requires a minimum cost to implement but has the potential to yield effective results. However, it was suggested that the cost of other equipment used (as well as the training of research assistants) be included in the total cost. Apart from being less capital intensive, implementing this type of intervention is also convenient for both the health provider and the patient who is the recipient because messages reach the patient at any time and place. This intervention is also less time-consuming and curbs the problem of accessibility to the health facility because patients can communicate directly with their health providers through voice calls and phone apps. It is convenient in resource-limited facilities where venues for routine health education sessions are limited or unavailable, but health information can be delivered through this medium. The participants in all of the chosen trials were diverse. The fact that they varied from one another in terms of their medical and social profiles may have influenced the results, but the authors used the data from the studies to assess how well the interventions worked. Additionally, there was no established time frame for assessing therapies because adherence is a behavior that lasts a lifetime. Studies on enhancing drug adherence have contradictory findings, where seventeen studies (73.91%) reported significant improvement in medication adherence whereas, six studies (26.09%) failed to show a substantial impact of the intervention on medication adherence. Overall, RCTs using various interventions in the current review showed improvement in adherence to CVDs (Figure 3). The study presented a smartwatch-based medication reminder system and also introduced an early prototype to improve medication adherence [50]. Furthermore, it could help the clinicians to improve adherence to treatment, which could be beneficial to the the patient [51]. This has been found in different studies in which medication adherence was measured using different designs, sample sizes, intervention approaches of mHealth, adherence measuring tools, outcome measures, and the multitude of methods.
Mobile health strategies are instrumental in addressing the issue of medication nonadherence, which has been a challenge in the management of chronic diseases. Despite the positive feedback reported regarding the effectiveness of mHealth interventions in adherence to cardiovascular medications, more studies on the cost-effectiveness of this intervention type are required, especially in resource-limited settings. Some studies have stated that mHealth interventions are cost-effective; for instance, an mHealth intervention study in Kenya reported a marginal cost of USD 0.02 per SMS and a weekly SMS, which represented a cost of almost USD 1 per patient annually. This suggested that mHealth intervention requires a minimum cost to implement but has the potential to yield effective results. However, it was suggested that the cost of other equipment used (as well as the training of research assistants) be included in the total cost. Apart from being less capital intensive, implementing this type of intervention is also convenient for both the health provider and the patient who is the recipient because messages reach the patient at any time and place. This intervention is also less time-consuming and curbs the problem of accessibility to the health facility because patients can communicate directly with their health providers through voice calls and phone apps. It is convenient in resource-limited facilities where venues for routine health education sessions are limited or unavailable, but health information can be delivered through this medium. The participants in all of the chosen trials were diverse. The fact that they varied from one another in terms of their medical and social profiles may have influenced the results, but the authors used the data from the studies to assess how well the interventions worked. Additionally, there was no established time frame for assessing therapies because adherence is a behavior that lasts a lifetime. Studies on enhancing drug adherence have contradictory findings, where seventeen studies (73.91%) reported significant improvement in medication adherence whereas, six studies (26.09%) failed to show a substantial impact of the intervention on medication adherence. Overall, RCTs using various interventions in the current review showed improvement in adherence to CVDs (Figure 3).  However, in planning a mHealth intervention, it is recommended that studies of this nature design an acceptable, standardized, systematic, and validated mobile phone application to facilitate the implementation. In the present review, only the studies in [34,52] had customized applications designed for their interventions. Furthermore, the timing of sending messages and making calls as well as the language used must be acceptable to and understandable by the patients. Close attention should be paid to the content and frequency of the messages and calls to fully achieve the goal of the intervention and gain the benefits thereof. An alternative method should be considered for patients who have problems with their sight, speech, or hearing, as well as for patients who cannot read. All these factors should be considered in designing mHealth interventions. Lastly, one of the goals of behavioral interventions is to produce lasting or durable behavioral changes that will yield positive health outcomes. On this note, it is suggested that future research on mHealth focus on the long-term impact of this intervention on adherence behavior among cardiovascular disease patients because this will provide more authentic findings and justifications.

Conclusions
This review, as a whole, supported the notion that mHealth interventions can be effective in improving adherence to CVD medication even though the use of various mHealth interventions could not improve adherence to all CVD medications when compared with controls. Further trials with more refined designs integrated with comprehensive and effective interventions are needed. Furthermore, cost-effectiveness studies of such interventions should be conducted to further derive the benefits of mHealth interventions. This will enable policymakers to reserve capital-intensive interventions for patients who are most in need of such interventions; for instance, patients dealing with factors that mHealth cannot overcome (such as adverse drug reactions or a high pill burden).
Author Contributions: All authors contributed significantly and equally for this study. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.

Institutional Review Board Statement:
This study did not require informed consent or ethical approval because it was an aggregate study.

Informed Consent Statement:
This study did not require informed consent or ethical approval because it was an aggregate study. Data Availability Statement: All accessed data for this study is shared in the manuscript. In case of any further requirement, 1st author can be accessed.

Conflicts of Interest:
The authors declare no conflict of interest.