Effect of mobile phone applications on medication adherence among patients with coronary artery diseases: A scoping review

Abstract

Patients with cardiovascular disease rely on medication to achieve favorable long-term clinical results. Poor adherence has been linked to a relative increase in mortality of 50%-80% as well as higher health care costs. This scoping review thus aimed to explore the evidence of the effects of mobile health care apps on medication adherence in patients with cardiovascular diseases. A comprehensive data search and extraction was done in line with the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist. A total of 10 studies were included for the review. The mean pooled improvement in adherence was found to be 18% and the most effective tool was the digital therapeutics app discussed in Li et al’s study. Smartphones and apps enhance coronary artery disease management by promoting medication compliance. Challenges include data security and smartphone usage among the elderly. Tailored apps or voice assistants offer potential solutions.

Key Words: Mobile phone applications; Coronary artery disease; Medication adherence; Digital technology; Compliance

Core Tip: Mobile health applications can improve medication adherence in patients with coronary artery disease (CAD), with studies showing an average 18% increase in adherence. Digital therapeutics apps demonstrated the greatest effectiveness. While smartphones offer a promising platform for enhancing CAD management, challenges such as data security and limited technology use among older adults remain. Tailored apps and voice-assisted interventions may overcome these barriers and optimize adherence.

INTRODUCTION

The majority of cardiac rehabilitation methods are best suited for significant cardiovascular episodes or therapies like myocardial infarction or coronary artery bypass grafting. Low-cost approaches that integrate technological interventions and customized health coaching focused on medication adherence, risk factor management, and exercise have the ability to improve cardiovascular outcomes when compared to current care models and conventional rehabilitation's low referral and retention rates. Cardiac rehabilitation, which aims to increase survival after a cardiac episode, emphasizes the control of cardiovascular risk factors and drug adherence. Medication compliance is crucial for any disease, especially cardiovascular disease patients' lasting clinical outcomes[1,2], and a strong compliance with prescribed medications is required to control the disease[3,4]. Various self-care methods are shown to improve patient's actual use of the knowledge learned during the educational programs. Text messaging has been shown to increase medication adherence, according to a comprehensive assessment of mobile adherence platforms[5]. However, text-only interventions have a tiny impact and a limited ability to track adherence[6].

National and international guidelines strongly endorse the prolonged use of secondary preventive drugs since it has been shown to enhance the prognosis of cardiovascular illnesses[7-9]. But barely 30%-50% of patients actually adhere to long-term secondary prevention medications, and the situation is significantly worse in low-income nations. This indicates the huge disparity between actual practice and prescribed recommendations, which is a major impediment to the overall advantages of these medicines[10-13]. Poor adherence has been associated with a relative rise in mortality of 50%-80% as well as higher costs for medical treatment[14,15].

Recent systematic reviews have shown that, rather than reducing mortality for secondary prevention, mobile health (mHealth) significantly reduced patients' cardiovascular risk factors; however, among the trials included in the meta-analysis, only a small number were carried out in low-income nations[16,17]. Improving health outcomes requires the creation of strategies to address pharmaceutical nonadherence. A recent network meta-analysis of numerous diverse interventions revealed that those that utilized technology had a favorable, though transient, impact on drug adherence[18]. The mHealth interventions, such mobile apps, have been introduced because of the increasing integration of technology into daily life to assist patients and health care professionals in managing disease[19,20].

To maximize the long-term management of congenital heart disease (CHD), an innovative and efficient management paradigm other than traditional, hospital-based follow-up therapies is required. Recent advancements in technology have made digital therapeutics (DTx) a promising option for the secondary prevention management of chronic diseases[21,22]. DTx uses technology, such as smartphones and software applications, to deliver healthcare services directly to patients using evidence-based, clinically evaluated software algorithms or apps.

Although secondary prevention medications are essential for managing coronary artery disease (CAD), adherence rates remain low, especially in low-income and middle-income countries. Traditional cardiac rehabilitation programs are limited in reach and effectiveness, and text-based interventions offer minimal impact. While mHealth apps show promise in supporting medication adherence, there is limited consolidated evidence focusing specifically on their role in CAD management. This study addresses that gap by reviewing the effectiveness of mobile phone applications in improving medication adherence among CAD patients. Therefore, the objective of this study is to explore the evidence of the effects of mHealth care apps on medication adherence in patients with cardiovascular diseases (CVD).

METHODOLOGY

Identifying the research question

The first step of any review is the identification of the research question. The scoping review relies on a broad range of literature in contrast to systematic review which is limited in study design. The research question developed was “what is the current state of knowledge on the effect of mobile phone applications on medication adherence among patients with CADs”. The objective of this review is to identify and map the existing literature on the use of mobile phone applications to improve medication adherence among CAD patients.

Information sources: We searched PubMed, EMBASE, Scopus, Web of Science, ScienceDirect, and Google Scholar. The basic plan joined together the themes "mobile phone applications", "medication adherence", and coronary disease with the keyword string: "mobile phone applications" AND "medication adherence" AND "coronary artery disease" OR "CAD" OR "cardiac diseases". For PubMed, we applied MeSH terms telemedicine, remote consultation, internet, cellular phone, smartphone, text messaging, mobile applications, monitoring, ambulatory, medication adherence, coronary disease (or broader heart diseases when appropriate), artificial intelligence, machine learning, decision support systems, clinical, and wearable electronic devices, combining them with free-text synonyms (telemedicine, telehealth, eHealth, mHealth, exchange telephones, telephone, telephone exchange, video consult, videoconference, internet, web, online, remote monitor, telemonitor, home monitor, mobile app, smartphone, cell phone, SMS OR text message, WhatsApp, WeChat, chatbot, conversational agent, digital therapeutics, wearable, Bluetooth, NFC, IoT, artificial intelligence, machine learning, decision support) and intersecting with medication adherence/compliance and coronary artery disease/CAD/CHD/heart disease terms; for EMBASE, we mapped these to Emtree – 'telemedicine'/exp, 'remote consultation'/exp, 'internet'/exp, 'mobile phone'/exp, 'smartphone'/exp, 'text messaging'/exp, 'mobile application'/exp, 'telemonitoring'/exp, 'eHealth'/exp, 'wearable device'/exp, 'artificial intelligence'/exp, 'machine learning'/exp, 'clinical decision support system'/exp, with 'medication adherence'/exp or 'patient compliance'/exp and 'coronary artery disease'/exp; and because Scopus, Web of Science, ScienceDirect, and Google Scholar do not use controlled vocabularies, we deployed the same comprehensive keyword blocks in their respective fields (title-abs-key for Scopus, topic/TS for Web of Science, all fields/full text for ScienceDirect, and general query for Google Scholar), ensuring coverage of technologies and methods that support telemedicine implementation alongside adherence and coronary disease concepts. The initial search generated 3114 publications without filters, which reduced to 128 with filters applied for last 5 years, research articles/data articles, subject area medicine and dentistry, and open access. Google Scholar produced 76300 results initially (no filters); limiting the range to 2019-2023, sorting order to date, permitting all articles, and screening first three pages resulted in 28 results (Figure 1).

Figure 1  Flowchart showing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses study selection process.

Selection of sources of evidence: The selection of the sources of the evidence was done based on the eligibility criteria mentioned above (Table 1).

Table 1 Inclusion and exclusion criteria for the selection of the articles under study.

Criteria	Inclusion	Exclusion
Time period	2019-2023	Before 2019
Language	English	Other than English
Type of article	Quantitative, qualitative or mixed-method	Not peer reviewed, reviews, systematic or scoping reviews, chapters
Study focus	That examine the effect of mobile phone applications on medication adherence among CAD patients	Studies focusing on factors other than effect of mobile phone applications on medication adherence among CAD patients/or those featuring non-CAD/general patients

CAD: Coronary artery disease.

Data items and data charting process: Data were extracted from the individual selected studies which included year of publication, study design, objectives of the study, intervention tool used and the conclusion. Also, the sample size used in each study, medication adherence either in percentage or improvement, and the intervention app/tool features were extracted and comparative tables for these were made.

Critical appraisal of individual sources of evidence: Critical appraisal of individual sources of evidence/individual study was done by the researcher, was included in the result part and also discussed in the discussion.

RESULTS

Out of the 10 selected studies, 8 were randomized control trials (RCTs), one was qualitative and one was a prospective study. Table 2[23-32] summarizes the general characteristics of the selected studies, showing that use of mobile applications as interventions improved medication adherence. However, in one study using the Bright Heart program as an intervention, there was no significant difference found in adherence to medications between the intervention and control groups. Two protocols have also been selected using different mobile applications for improvement of adherence to medication.

Table 2 General characteristics of the selected studies.

Ref.	Year	Design	Objectives	Intervention	Follow up	Findings
Ni et al[23]	2018	RCT	To assess if the mobile phone-based mHealth intervention could improve medication adherence and relevant health outcomes among patients with CHD	mHealth	90 days	Nonadherence decreased more significantly in the experimental group
Devaraju et al[24]	2022	RCT	To study the use of technology on ensuring clinical follow ups of post percutaneous transluminal coronary angioplasty patients treatment compliance and study its effectiveness on modification of risk factors and adherence to lifestyle	Jayadeva Hrudaya Spandana	6 months	Adherence increased significantly in the experimental group
Chapman-Goetz et al[25]	2023	Prospective, single-blinded RCT	To assess the acceptability and feasibility of an app in supporting medication adherence in heart failure	Tiered intervention added to the NPS MedicineWise dose reminder app (MedicineWise app)	6 months	Protocol (results not known)
Li et al[26]	2022	Parallel designed, open-label, single-center, RCT	To explore the effect of a self-management mobile app to be used at home on the long-term use of secondary prevention medications in patients with CHD in China	Digital therapeutics app	12 + 6 months	Significant increase in adherence after using the app
Olivier et al[27]	2021	Open-label, single-center RCT	To evaluate the efficacy of a blended intervention with custom-designed mobile application and personalized health coaching to improve adherence to cardiovascular medications and risk factors	Bright Heart program, (mobile intervention)	90 ± 10 days	Not much significant difference was found between the intervention and control arms
Park et al[28]	2020	Qualitative	To examine the efficacy of a mHealth intervention using text messaging to improve adherence to antiplatelet and statin medications among patients with a history of myocardial infarction and/or percutaneous coronary intervention	TM + mobile app	4 weeks	TM increase compliance
Dorje et al[29]	2019	Parallel-group, single-blind, RCT	To assess the effectiveness of a smartphone-based cardiac rehabilitation and secondary prevention programme delivered via the social media platform WeChat (SMART-CR/SP)	SMART-CR/SP	12 months	SMART-CR/SP was found to be a cardiac rehabilitation and secondary prevention service model with high efficacy and accessibility and to be easy to use
Kauw et al[30]	2019	Prospective study	To investigate what advantages mHealth offers in the management of adults with CHD and to evaluate the acceptance of mHealth through adherence and patient experience	mHealth	12 months	Adherence to weekly measurements of weight and blood pressure were measured (non-pharmacological adherence)
Gallagher et al[31]	2019	RCT	To evaluate the impact of a game-based mobile app, in secondary prevention behaviours for cardiovascular risk factors	MyHeartMate	6 months	Protocol (results not known)
Santo et al[32]	2019	Parallel-design, single-centre, single-blind RCT	To assess the reach, acceptability, utility, and engagement with the apps that were used in the MEDication reminder APPlications (apps) to improve medication adherence in coronary heart disease (MedApp-CHD) study	MEDAPP	3 months	The participants in the medication reminder app groups had a significantly higher medication adherence, when compared to the usual care group

CHD: Congenital heart disease; mHealth: Mobile health; RCT: Randomized control trial; TM: Text messages.

Table 3 summarizes the results of the selected studies[23-32]. It demonstrates the percentage change in adherence for medications after using mobile applications as intervention. Most of the studies have found an increase in adherence and compliance for medications used in CVD. Ni et al[23] shows about 23% decrease in nonadherence whereas other studies find improvement in adherence and compliance. Sample sizes ranged between 26 (trial failure) and 394. The results of the protocol are not known to the authors. Based on the included studies, a combined, pooled average of improvement in adherence was found to be approximately 18%.

Table 3 Results of the selected studies.

Ref.	Design	Sample size	Results
Ni et al[23]	RCT	I: 103, C: 93	Non adherence	Decreased to 23.06% in I, 1.13% in C
Devaraju et al[24]	RCT	I: 100, C: 100	Compliance, adverse events	89 in I, 78 in C, 11%, 3 vs 15, 14% less in Intervention group
Chapman-Goetz et al[25]	RCT	N = 55, equal in both arms	Medication adherence	Protocol (results not known)
Li et al[26]	RCT	I:143, C: 147	Medication adherence	I: 86.2% adherence, C: 69.2% adherence
Olivier et al[27]	RCT	I: 11, C: 15 (trial failure), planned sample size: 132	Medication adherence	I: 88% (PDC), C: 94% (PDC)
Park et al[28]	Qualitative	28	Compliance (increase)	TM reminders + app increases compliance. Multiple verbatims supported this
Dorje et al[29]	RCT	I: 156, C: 156	Compliance (increase)	I: 37% adherence, C: 22% adherence
Kauw et al[30]	Prospective	109	Adherence to weekly measurements of weight and blood pressure	Adherence of more than 70% was registered in 77% of the patients
Gallagher et al[31]	RCT	I: 197, C: 197	Behaviour change, adherence	Protocol (results not available)
Santo et al[32]	RCT	I1: 54, I2: 53, C: 56	Compliance	More than 50% of the I1, more than 75% of the I2

C: Control; I: Intervention; PDC: Proportion of days covered; RCT: Randomized control trial.

The details of the application used for improvement of adherence has been demonstrated in Table 4[23-29,31,32]. Devaraju et al[24] used Jayadeva Hrudaya Spandana mobile application which detects risk factors, symptoms, compliance, discharge report. Whereas Chapman-Goetz et al[25] used medicine Wise app and tiered intervention added to the NPS MedicineWise dose reminder app. Some applications have modules for demonstration while some are with alarms, reminders and snooze facility. Gallagher et al[31] developed an app (Myheartmate) which is based on a game and it is one of the most unique apps as it makes a heart avatar of the self and one has to take care of the heart as it is done for the real human heart e.g. exercise, diet, medicines etc. Another app worth mention was Santo et al’s app[32] MEDAPP which compared two apps basic and advanced, advanced having snoozing of alarm, medicine taken and missed options. Most effective among all were Li et al’s DTx app[26] which showed 17% improvement in the intervention group and Dorje et al’s SMART-CR/SP app[29] showing 15% improvement.

Table 4 Intervention/app details.

Ref.	App	App/intervention details
Ni et al[23]	mHealth	WeChat (Tencent Inc) and message express (Blue Mobile), message delivery apps (Reminders)
Devaraju et al[24]	Jayadeva Hrudaya Spandana	Risk factors, symptom, compliance, discharge report
Chapman-Goetz et al[25]	MedicineWise app	Tiered intervention added to the NPS MedicineWise dose reminder app
Li et al[26]	Digital therapeutics app	Three modules: Discharge module, home management module, follow up module, alarm system
Olivier et al[27]	Bright Heart program, (mobile intervention)	A mobile therapeutic intervention (WellnessMate, Cupertino, CA, United States) of personal health coaching with components of cardiac rehabilitation
Park et al[28]	MEMS	The mHealth mobile tech
Dorje et al[29]	SMART-CR/SP	WeChat based, pedometer, heart rate monitor, medicine reminder
Gallagher et al[31]	MyHeartMate	App based mobile game using heart avatar
Santo et al[32]	MEDAPP	Basic app: Alarm reminders. Advanced app: Snooze, taken and missed options

mHealth: Mobile health.

All the involved studies that were RCT (6 out of 10, two RCTs were protocols only), were analyzed individually for risk of bias using revised Cochrane risk-of-bias tool for randomized trials (Table 5)[23,24,26,27,29,32]. It was observed that out of the 6 RCTs 3 had low risk of bias, one had some concerns and two had high risk of bias. Devaraju et al’s study[24] showed high risk of bias in the domain 3 which deals with “risk of bias due to deviations from the intended interventions (effect of adhering to intervention)” and domain 5 which deals with “risk of bias in measurement of the outcome” while Olivier et al’s study[27] was identified to have high risk of bias in the domain-2 [risk of bias due to deviations from the intended interventions (effect of assignment to intervention)] and domain-3 [risk of bias due to deviations from the intended interventions (effect of adhering to intervention)].

Table 5 Risk of bias using revised Cochrane risk-of-bias tool for randomized trials.

Domain	Ni et al[23]	Devaraju et al[24]	Li et al[26]	Olivier et al[27]	Dorje et al[29]	Santo et al[32]
Domain-1: Risk of bias arising from the randomization process
1.1	Y	NI	Y	Y	Y	Y
1.2	Y	NI	Y	Y	Y	PY
1.3	N	NI	N	N	N	PN
	Low risk	Some concerns	Low risk	Low risk	Low risk	Low risk
Domain-2: Risk of bias due to deviations from the intended interventions (effect of assignment to intervention)
2.1	Y	Y	Y	Y	Y	Y
2.2	Y	Y	Y	Y	N	Y
2.3	N	N	N	Y	N	N
2.4	NA	NA	NA	Y	NA	NA
2.5	NA	NA	NA	N	NA	NA
2.6	PY	NI	PY	PN	PY	NI
2.7	NA	PN	NA	PY	NA	N
	Low risk	Some concerns	Low risk	High risk	Low risk	Some concerns
Domain 3: Risk of bias due to deviations from the intended interventions (effect of adhering to intervention)
3.1	Y	Y	Y	Y	Y	Y
3.2	Y	Y	Y	Y	N	Y
3.3	NA	NI	NA	PN	NA	NA
3.4	N	NA	N	Y	N	N
3.5	N	NA	N	Y	N	N
3.6	NA	NI	NA	PN	NA	NA
	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Domain 4: Risk of bias due to missing outcome data
4.1	Y	Y	Y	N	Y	Y
4.2	NA	NA	NA	N	NA	NA
4.3	NA	NA	NA	N	NA	NA
4.4	NA	NA	NA	NA	NA	NA
	Low risk	Low risk	Low risk	Low risk	Low risk	Low risk
Domain 5: Risk of bias in measurement of the outcome
5.1	N	PY	N	N	N	PN
5.2	N	PN	N	N	N	N
5.3	Y	NA	Y	Y	Y	Y
5.4	N	NA	N	N	N	N
5.5	NA	NA	NA	NA	NA	NA
	Low risk	High risk	Low risk	Low risk	Low risk	Low risk
Domain 6: Risk of bias in selection of the reported result
6.1	Y	NI	Y	Y	Y	Y
6.2	N	PN	N	N	N	N
6.3	N	PN	N	N	N	N
	Low risk	Some concerns	Low risk	Low risk	Low risk	Low risk
Overall risk-of-bias judgement	Low risk	High risk	Low risk	High risk	Low risk	Some concerns

N: No; NA: Not available; NI: No information; PN: Probably no; PY: Probably yes; Y: Yes.

The study by Santo et al[32] was identified to have some concerns in risk of bias because of the domain-2 [risk of bias due to deviations from the intended interventions (effect of assignment to intervention)].

Clumps of evidence surround reminder/messaging ecosystems (programs based on WeChat and medication-reminder apps) and organised self-management (DTx) apps, with follow-up most typically at 3-12 months and a single long-term outlier at 18 months with improved adherence (Figure 2)[23-32]. High-enrolment RCTs and a large gamification-based app protocol (as yet reporting no results) have the largest bubbles, reflecting high activity but sparse evaluable outcomes in that subdomain. Most evaluable RCTs in the reminder/messaging and DTx strata have directionally positive adherence, while a tiny app-plus-coaching RCT at approximately 3 months has no difference, and the monitoring cohort registers non-pharmacologic adherence but not medication adherence. The map collectively implies the most dense and positive signal in reminder/messaging and DTx groups, gaps in evidence in gamification-based and in all-coaching-based models with closed endpoints, and infrequent follow-up beyond 12 months.

Figure 2 Bubble evidence map. Follow-up on X-axis, intervention category on Y-axis; bubble size represents total sample; shape represents design; annotation represents author-year + outcome. CHD: Congenital heart disease; DTx: Digital therapeutics; mHealth: Mobile health.

As evident through Figure 3, counts are aggregated in the box titled "improved adherence" across a number of intervention families (WeChat-based programs, reminder programs, DTx self-management, and texts-plus-apps), "no difference" is flagged by an individual app-plus-coaching trial, "protocol/no results" is found for reminder and game-based programs, and "non-pharmacologic adherence" is reserved just for the monitoring group. This pattern confirms that reminder/messaging and DTx approaches now have the strongest support with positive outcomes, whilst game-based and augmented-coaching interventions are remaining underpowered or constrained with regard to published endpoints of adherence. The heatmap generally highlights where there already is evidence to inform actions (reminder/messaging, DTx) and where spotlight trials are most needed (game-based, augmented-coaching, follow-up long-term).

Figure 3 Evidence matrix heatmap (intervention category × outcome category, with counts). DTx: Digital therapeutics.

DISCUSSION

Various studies have shown that even training or capacity building of front-line workers in the screening of CVD increases their ability to diagnose the disease at community level[33]. Similar is the case when the patients are trained with the help of mobile based healthcare apps to increase compliance. Pérez-Jover et al[34] conducted a systematic review of studies from various databases to assess the impact of mobile apps on treatment adherence and perceived value. Eleven studies published between 2000 and 2017 were included, with sample sizes ranging from 16 participants to 99 participants. Seven studies confirmed that mobile apps improved treatment adherence, with a percentage increase in self-reported adherence and reduced missed doses. Users found the apps easy to use and helpful, rating them an average of 8.1 out of 10. The findings suggest that mobile apps are beneficial for managing medication at home and enhancing treatment adherence[34]. In contrast Pérez-Jover et al[34], our present review included 9 studies, most of which were RCT, having sample sizes ranging from 26 to 394.

In a single centre randomized controlled experiment, Dorsch et al[35], participants older than 45 who were hospitalized for acute decompensated HF or who were discharged from the hospital in the past 4 weeks were included. The "app group" received the intervention, which emphasized consistent monitoring and management by oneself. The "no-app group" (also known as the control group) received the usual care. The Minnesota Living with Heart Failure Questionnaire (MLHFQ) score after six and twelve weeks was the main result. Recurrent HF admissions and the Self-Care Heart Failure Index Questionnaire score were secondary outcomes. A total of 83 participants completed the preliminary evaluations. The self-monitoring and self-management elements of the adaptive mobile app intervention increased the MLHFQ after 6 weeks, but the effects were not sustained after 12 weeks. According to self-report, there was no effect on managing HF. To increase app usage over a longer period of time and to ascertain whether the app can lower HF readmissions in a bigger sample, more research is required[35]. In one of the studies included in our research by Santo et al[32], three groups were compared (1) Standard treatment; (2) A simple app; and (3) An advanced app by patients with CHD who used medication reminder apps to those receiving normal care, those who used apps with more features did not further increase medication adherence. These findings imply that pharmaceutical applications may aid with patients' adherence to their medications for long-term diseases, but additional research is needed to see whether such advantages last.

Piña et al's review[36] found that patients who do not take the drugs according to their prescriptions, do not get benefit from those. This accurate remark emphasizes the significance of drug compliance. Also, this was similar to our study findings because most of the individual studies included in our study showed that adherence is necessary for control of symptoms such as increased heart rate, increased blood pressure (BP) etc. The patients' inconsistent nonadherence to prescribed medicine frequently frustrates care givers. The time restrictions providers’ face nowadays make it challenging to determine the degree of non-adherence. There are undoubtedly numerous obstacles to medication adherence, not only between patients and providers but also across healthy systems, insurers, and payment systems. A total of 62% of persons with chronic illnesses, such as hypertension, diabetes, and hyperlipidaemia, who participated in a cross-sectional study on accidental nonadherence, reported forgetting to take their medications, and 37% reported having run out of them within a year. These grave statistics call for rapid legislative and systemic changes to help patients adhere. The effects of CVD medications, such as BP control and subsequent events, may differ depending on medication adherence. This review examines medicine adherence, with all of its inherent problems, suggests structural and policy adjustments that are necessary to improve medication adherence rates worldwide and meet the individual goals for achieving better health.

According to Orcioni et al’s study’s suggestion[37], using cutting-edge technologies like cell phones, the internet, and online tech are effective in assisting patients in completing their medications. The deployed system offers a calendar that serves as a reminder of the presumptions, guarantees that drugs are identified using near-field communication (NFC), and enables family members and medical personnel to check and manage therapy remotely in real-time. Additionally, the system offers centralized data on the patient's therapeutic status, which is beneficial in deciding on new, compatible medicines[37]. In one of our included studies[16] an innovative app that uses a game and a heart avatar was found to be a nice innovative intervention.

Since its initial conception in the 1980s, telemedicine has matured from an idealized model of distant care to patient-centered, mHealth ecosystems combining task-shifting, self-management, and real-time monitoring for cardiovascular populations. Initial steps involved capacity-building at the community level, with training frontline workers measurably enhancing cardiovascular screening and diagnostic capacity outside of tertiary centres[33]. A first phase of patient-facing apps (2000-2017) then showed that plain, usable devices could elevate treatment adherence and be cherished by users, paving the way for feasibility and acceptability of medication support from home[34]. Later randomized work escalated beyond reminders to formalized self-monitoring/self-management following high-risk events: For instance, an adaptive heart-failure app improved quality-of-life at six weeks, but effects were lost by twelve weeks, emphasizing the challenge of preserving engagement over the longer term[35].

Crucially, feature inflation has not ensured better outcomes – when standard care was contrasted with simple and advanced reminder apps in CHD, the advanced jadx set did not further elevate adherence, emphasizing parsimony and fit over sophistication[32]. Concomitantly, research has rewritten nonadherence as a system-level challenge requiring structural and policy solutions as much as digital prompts, due to the prevalence of unintentional nonadherence in chronic disease[36]. The present status of telemedicine incorporates integrated technologies – for instance, NFC-enabled drug recognition, caregiver/clinician dashboards, and centralized therapy data to facilitate timely adjustments – linking home routines with professional monitoring[37]. Developing engagement approaches like gamification (e.g., a heart-avatar app) reflect the sector's shift towards motivational design, but also reflect the requirement for bigger, longer trials to validate durability of benefit[16]. Individually, but together as an arc from community capacity development to interoperable data-rich mHealth, telemedicine success now hinges on sustained use[16,32], human-centered ease of use, and robust integration with health-system workflow and policy[33-37].

This study has few limitations. Despite doing the search in the most significant medical databases, it is likely that other databases were not taken into consideration, which is one of the study's potential weaknesses. Additionally, even though we employed a wide variety of descriptors/keywords to develop a more exact technique, a specific phrase from a particular location might have escaped control.

Additionally, we did not take conference abstracts into consideration or include papers written in languages other than English. The difficulty in assembling the results due to the extensive variability of methodology and outcomes from the papers that were found is another restriction to be highlighted.

CONCLUSION

Smartphones, software, and connected devices can improve the management of CADs by encouraging medication compliance and comprehensive disease management. There are now interactive teaching and social support tools built into mobile applications that target pharmaceutical non-adherence. For mobile applications to be fully integrated into healthcare administration, a number of problems must be solved. Security of patient health information is essential, demanding the prioritization of cybersecurity and routine risk assessments for applications and related devices. For mobile applications to be effective, smartphone penetration is essential, yet elderly patients use smartphones less frequently. Applications that are tailored to carriers or the use of voice assistants in the home are some solutions.

Mobile applications that address several facets of CAD care can be introduced into daily practice once the evidence persuades doctors and smartphone adoption increases. By altering patient relationships and increasing the effectiveness of disease management, apps' widespread adoption has the potential to provide benefits that go beyond drug adherence. A patient-driven virtual care paradigm is made possible by digitalization, freeing up physical facilities for urgent cases and promoting research and innovation. Mobile software and applications will likely play a major role in healthcare as technology advances and flaws are fixed.

ACKNOWLEDGEMENTS

The authors extend their appreciation to the Deanship of Post graduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2025-2094).