Exploring mHealth interventions for medication management: a scoping review of digital tools, implementation barriers, and patient outcomes

Research objectives

This scoping review examines and summarises the available information on the impact of mobile health applications on medication adherence and health outcomes. The study evaluates the effectiveness of the mobile health app, classifies its characteristics, and identifies research needs. This study’s evidence map may help doctors, policymakers, and software developers integrate mobile health therapies into clinical care. Additionally, the study examines the following:

• I.

  Classify mobile health applications that improve medication adherence.

• II.

  Evaluate the effectiveness of mobile health applications in enhancing prescription adherence across various patient categories and health concerns.

• III.

  Assess how mobile health treatments affect disease progression, hospital readmissions, and patient-reported health indicators, and

• IV.

  Identify the factors that facilitate or hinder the use of mobile health apps.

This research helps to understand the role of digital health technologies in medication management and identify ways to improve their clinical application.

Research questions

The following major research issues guide our scoping review. First, what sorts of mobile health applications are currently in use to urge patients to take their prescriptions as recommended? Mobile health applications vary in terms of functionality, user base, and connection to healthcare systems. These applications include reminders, trackers, skill-builders, and telemedicine platforms (Plow et al., 2022). Push notifications, SMS alerts, and alarms from appointment-based reminder apps help patients remember to take their medications on time. Healthcare providers and patients may benefit from apps that track medication use, dose adherence, and treatment progress (Ghozali, 2024). Educational apps that contain research-backed content may help patients better understand their condition and the importance of therapy. Telemedicine apps allow doctors and pharmacists to consult remotely and manage medications in real-time (Fenton, Faruque & Mollenkopf, 2025). Some cutting-edge mobile health applications utilize AI and ML algorithms to predict non-compliance and monitor compliance patterns. Gamification, peer-support networks, and electronic health record (EHR) integration may attract users to these apps (Grover & Arora, 2024). The most effective approach to determine which mobile health applications enhance medication adherence and which features promote sustained patient involvement is to provide education on the many categories of these applications. Second, to what degree may particular mobile health app features and capabilities promote medication adherence? This query concerns the effectiveness of mobile health applications in terms of their simplicity, ease of use, and ability to address patient-specific challenges that impact medication adherence. Refill alerts, personalised adherence strategies, interactive dashboards to track prescription use, and automated reminders can all improve adherence. According to studies, apps that enable two-way communication with doctors help patients stick to their treatment programs (Turchioe, Lai & Siek, 2024; Ali et al., 2024). These apps encourage responsibility and provide instant medical advice. Some apps utilise behavioural science concepts, such as gamification, social motivation, and incentives, to enhance engagement and adherence. Several apps utilise AI-powered analytics to dynamically adjust intervention methods or reminder frequencies to match patient behaviour (Khalid et al., 2024). Another emerging trend is biometric monitoring, which utilises mobile apps and wearable devices to track vital indicators, including heart rate, blood sugar, and blood pressure. It correlates this data with medication adherence (Del-Valle-Soto et al., 2024). These technological advances, user acceptability, digital literacy, and accessibility should be considered when examining the impact of mobile health apps on prescription adherence. Third, in what ways may health outcomes, such as sickness management and quality of life (QoL), be influenced by mobile health applications? Mobile health applications can enhance medication adherence, self-management, reduce hospital readmissions, and mitigate disease complications, ultimately improving patient health outcomes. Patient outcomes for chronic diseases, diabetes, hypertension, cardiovascular disease, and mental health issues improve with mobile health therapy (Elkefi, 2024). These applications provide self-monitoring, symptom tracking, and rapid access to instructional resources, enabling patients to engage in their treatment actively. By holding patients responsible for their medications, mobile health apps may enhance disease management, reduce symptom aggravation, and reduce ER visits (Makki et al., 2024). There is strong evidence that people with greater control over their treatment are less afraid and more confident in their health choices (Kohut et al., 2024). Some mobile health and telemedicine technologies enable doctors to respond rapidly, thereby decreasing the risks associated with delayed therapy. To determine the long-term benefits of mobile health therapies, death rates, healthcare costs, and patient-reported outcomes must be studied across demographics and medical conditions. Fourth, what are the standard obstacles and challenges associated with the use of mobile health apps for prescription administration? Many barriers hinder patients from routinely and extensively adopting mobile health applications, despite their potential. One of the biggest challenges is that many patients, particularly those from low-income backgrounds and older individuals, lack the technological skills to use complex mobile applications (Chan et al., 2024). Not all patients have access to telephones, consistent internet, or data plans to utilise the applications, which may cause accessibility issues. Some patients may avoid mobile health applications owing to privacy and security concerns about storing and sharing sensitive medical data (Xing et al., 2024). Another user engagement issue is that many patients discontinue using the app after the initial period, resulting in inconsistent adherence benefits. Additionally, mobile health solutions require the cooperation of healthcare practitioners. Due to concerns over data reliability, legal constraints, and workflow disruptions, many clinicians are hesitant to incorporate these technologies into routine therapy (Pesapane et al., 2025). Addressing these constraints through user-centred design, regulatory oversight, and integration of healthcare infrastructure would enhance the effectiveness of mobile health therapy. Finally, what are the most significant research gaps in mobile health apps and medication adherence research? Several studies have examined how mobile health applications can aid in medication management, yet gaps remain. Unfortunately, there have been no large-scale, long-term clinical investigations on how mobile health therapies improve health outcomes and drug adherence. Earlier research on app-based adherence improvements is experimental or short-term, making it difficult to assess durability (Liu et al., 2024; Gordon et al., 2025). Few studies have evaluated the effectiveness of mobile health applications in diverse healthcare environments, patient demographics, and medical problems (Khamaj & Ali, 2024; Garavand et al., 2024). There are few studies on how behavioural change theories affect app effectiveness and how AI and predictive analytics improve prescription adherence (Zhang et al., 2025; Bucher, Blazek & Symons, 2024). The cost-effectiveness of mobile health programs requires additional study. Digital health ethics and regulation, including patient consent, data privacy, and healthcare provider duties, should be studied. Better evidence may help address these gaps, assisting in the development and implementation of mobile health solutions to promote medication adherence.

Significance of the study

This scoping investigation is crucial because digital health solutions are gaining popularity for enhancing patient participation, treatment adherence, and healthcare accessibility. Medication non-adherence is a challenging issue that requires innovative, evidence-based interventions. Mobile health app integration with medication management can transform adherence through customised, scalable, and affordable solutions (Coman et al., 2024). These therapies vary in effectiveness depending on healthcare infrastructure, patient demographics, medical issues, and app design (Pulimamidi, 2024). This study draws on current research to illuminate the various healthcare contexts in which mobile health apps operate and their impact on patient care.

This study benefits healthcare providers, IT firms, researchers, and government officials. By understanding mobile health apps, doctors and pharmacists can better incorporate digital solutions into their clinical practice and provide patients with more effective advice. App developers and healthcare IT businesses can utilise the review’s conclusions to design mobile health applications that better meet users’ needs and adhere to research-backed best practices. Healthcare organisations and governments can use the findings to support digital health initiatives, standardize mobile health applications, and ensure equitable access. For scholars, this study’s comprehensive examination of the existing material will fill certain research gaps. While systematic reviews and meta-analyses focus on a single research work, this scoping review maps out various research works and identifies trends. The results of this study influence systematic reviews, experimental research, and policy-driven studies to enhance digital medication adherence programs.

Finally, mobile health app research on effectiveness and long-term consequences is in its early stages, but it shows promise in addressing medication non-adherence. This scoping review’s thorough synthesis of pertinent studies will address a key gap in our understanding of how these technologies affect patient behaviour and clinical outcomes. Methodically reviewing mobile health treatments introduces new, technology-driven approaches for enhancing drug adherence, thereby contributing to digital healthcare.

Literature review

The rapid digitisation of healthcare has significantly impacted patient management, particularly in terms of medication adherence. Mobile health applications are powerful digital remedies because they provide real-time monitoring, personalised medication reminders, AI-powered recommendations, and interactive patient engagement (Buttigieg, 2025; Periáñez et al., 2024). These developments address non-adherence to pharmacological therapy, a significant issue in healthcare. Long-term health issues, including diabetes, heart disease, and mental illness, need drug compliance (Dhingra et al., 2024). For decades, physician supervision, medication organisers, and patient education have failed to improve medication adherence. The research suggests that digital therapeutics address this issue in a scalable, cost-effective, and behaviorally flexible manner.

A recent study suggests that mobile health applications help patients take their meds, particularly those with chronic diseases. These apps utilise gamification, automated reminders, and incentives to promote adherence (Bertolazzi, Quaglia & Bongelli, 2024). According to research, seniors and individuals with complex medication regimens are more likely to follow their treatment programs when reminders are provided through push notifications, SMS alerts, and voice-assisted reminders (Jelassi et al., 2024). These applications’ AI-powered algorithms utilise behavioural analytics to forecast compliance patterns and identify early warning signs of non-compliance. Despite promising short-term results, the long-term effectiveness and user involvement of these treatments remain concerns. The studies also mention personalised treatments, in which mobile health applications learn a user’s health patterns, provide tailored regimen adherence approaches, and integrate with wearable health devices (Deepthi et al., 2024; Aziz et al., 2024). Studies show that applications that employ real-time physiological monitoring, such as glucose monitors for diabetics or heart rate variability assessments for cardiovascular patients, improve adherence by combining drug ingestion with quick physiological feedback (Zhang et al., 2024; Keshet et al., 2023).

The effects of mobile health applications on patients’ health outcomes beyond adherence have been studied recently (Mikulski et al., 2022; Grundy, 2022). Digital health therapies are beneficial for mental problems when taking psychotropic medication is challenging. Mental health applications featuring cognitive behavioral therapy modules, mood tracking, and AI-driven emotional analysis support pharmacological adherence and holistic treatment (Olawade et al., 2024). Cardiovascular disease patients also exhibited improved lipid profiles, reduced incidence of significant adverse cardiac events, lower systolic and diastolic blood pressure, and greater digital adherence (Vazquez-Agra et al., 2024; Lampreia, Madeira & Dores, 2024). Despite a strong correlation between the app and good health, methodological shortcomings make causal inferences problematic.

Despite their potential, mobile health applications face barriers to adoption. Technical literacy and smartphone accessibility disparities, commonly referred to as the digital divide, persist, particularly among low-income and elderly groups (Li et al., 2024). Another impediment to adoption is patients’ concerns about the privacy of their medical records and prescriptions. There have been no large-scale, randomised controlled trials (RCTs) on the long-term impact of mobile applications on adherence and clinical outcomes. The study reveals that mobile health applications enhance individual adherence but are not effectively integrated into larger healthcare systems (Oppenheimer et al., 2024). Fragmented digital health ecosystems and incompatibility between electronic health records (EHRs) hinder clinician-led monitoring and intervention adjustments (Cerchione et al., 2023). The meta-analysis organises studies by their primary factors in Table 1.

Despite increased research on medication adherence and mobile health applications, substantial gaps remain in the literature. There are few longitudinal studies on long-term adherence, user retention, or sustained engagement compared to the wealth of research on short-term adherence (Yanai et al., 2024; Wang et al., 2024). Few studies have evaluated the effectiveness of mobile health features, such as gamification and AI-driven adherence prediction (Yanyan, Iahad & Yusof, 2025; Kuru, 2024). Furthermore, there is limited evidence on the viability and cost of these therapies in diverse healthcare systems, particularly in low- and middle-income countries (Bharadwaj et al., 2024; Gupta et al., 2024). Earlier studies have paid little attention to behavioural and psychological variables that affect adherence to the mobile health ecosystem (Chew et al., 2025; Saito & Kumano, 2025). This scoping review addresses these gaps in the literature by combining data from various research studies, identifying key aspects that increase adherence, and identifying potential adoption barriers. This research supports governments, healthcare providers, and mobile health developers in optimising digital adherence solutions to enhance global patient health.

Study design

This scoping review methodically examines the data to gain a deeper understanding of how mobile health applications can enhance patient outcomes and medication adherence. The study employs a systematic methodology that adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) standards for openness, reproducibility, and methodological rigour (Peters et al., 2021). The scoping review approach simplifies mapping all research to identify key topics, intervention techniques, and knowledge gaps. The methodological approach, comprising a systematic literature search, study selection, data extraction, and theme synthesis, ensures an impartial analysis of relevant empirical evidence.

This scoping analysis suggests that mobile health applications may improve medication adherence and patient outcomes. The final analysis comprises 319 studies from 2020 to 2024, identified through an extensive screening process to ensure compliance with PRISMA. This research uses quantitative and qualitative data from mobile health app trials, using scoping review best practices. Scoping reviews synthesise multiple studies to analyse existing trends and inform future research, whereas systematic reviews focus on a specific research topic and evaluate the research methods. The methodological approach covers randomised controlled trials, observational studies, and real-world healthcare implementation research. The primary question of this review is the impact of mobile health applications on medication adherence and patient outcomes. The analytical process, including data collection, classification, and synthesis, ensures methodical extraction and intelligible presentation of key conclusions.

Experimental design

This scoping review adhered to the PRISMA-ScR criteria to ensure methodological rigour and transparency. This research examined the features, effectiveness, implementation challenges, and patient-centred outcomes of mobile health therapies aimed at improving medication adherence. Scopus, Web of Science, PubMed/MEDLINE, CINAHL, and Google Scholar were searched for peer-reviewed articles published between 2020 and 2024. The inclusion criteria were randomised controlled trials, observational studies, or mixed-methods research designs to provide real-world evidence on mHealth interventions aimed at improving medication adherence. Two reviewers independently reviewed titles and abstracts, and then the whole texts. A structured data extraction approach was utilised to document research design, patients, intervention features, adherence results, and adoption barriers. Through theme synthesis, key concepts and patterns were identified.

Eligibility criteria

Specific inclusion and exclusion criteria were used to assess the study scope and eligibility, ensuring the review includes only relevant, high-quality literature. According to the inclusion criteria, the evaluation comprises studies that utilise mobile health applications to enhance medication adherence across various patient demographics. The study includes empirical data on clinical effectiveness, healthcare utilisation, patient-reported experiences, and medication adherence. The review comprises observational studies, mixed-methods designs, controlled trials, and clinical trials of mobile health therapies to assess their effectiveness, practical implementation, and patient engagement. Research was excluded if it lacked empirical data, emphasised non-digital treatment, or failed to provide measurable patient outcomes. Due to the eligibility framework, this evaluation remains focused, relevant, and current in light of digital health developments.

Search strategy

Thorough literature searches across many electronic databases provide a robust and complete evidence synthesis. Health informatics, digital health technology, and clinical treatments are the primary focus; therefore, Scopus, Web of Science, CINAHL, Google Scholar, and PubMed/MEDLINE were utilised. Advanced search filters and Boolean operators ensure reliable retrieval of literature. The search keywords were carefully combined to include medication adherence, patient outcomes, and mobile health applications. Keywords from a limited vocabulary (such as Medical Subject Headings (MeSH)) and synonyms were utilised to create a structured search string that enhances retrieval sensitivity and specificity. Reviewing relevant literature reference lists is part of the search process in case database searches miss something. The technique enhances coverage and reduces selection bias by progressively locating and incorporating all relevant material.

Study selection process

Study selection employs a multi-stage screening process to include only relevant and high-quality research. The initial screening evaluates titles and abstracts for relevance using specified criteria. During a full-text review, two reviewers assess the methodological quality and relevance to the research goals of articles that pass the initial screening criteria. In the event of study selection conflicts, a third independent reviewer was consulted. It shows how many studies were located, reviewed, and included or excluded from the analysis. The review follows worldwide evidence synthesis criteria and is more transparent and repeatable using this process. Figure 1 shows the PRISMA flow diagram for ready reference.

This scoping review adhered to the PRISMA-ScR criteria to ensure methodological transparency and rigour. Before searching the literature, qualifying criteria were defined: (i) Studies must have been published in English in peer-reviewed journals or conference proceedings between 2022 and 2024; (ii) they must have addressed medication adherence or management utilising mobile health-based treatments; and (iii) targeted acute or chronic patient populations. Editorials, letters, theses, comments, and articles without empirical or implementation-related outcomes were excluded. Following the rapid adoption of mobile health platforms during and after the COVID-19 pandemic, when mobile health applications gained popularity in remote patient care, the period from 2022 to 2024 witnessed the most recent digital health technology breakthroughs.

The study looked for related articles in PubMed, Scopus, Web of Science, and IEEE Xplore. It used keywords and Boolean operators to create search terms like “mHealth” (“mobile health” OR “digital health”) AND (“medication adherence” OR “treatment adherence” OR “drug compliance”) AND (“app” OR “application” OR “AI” OR “real-time monitoring”). Filters limited the results to English-language articles published during the period. While academic databases were examined, relevant grey literature, including WHO publications, national health agency briefings, and conference proceedings, was manually screened where relevant. These sources were excluded because peer-reviewed research was prioritised.

The study ensured rigour and reduced selection bias throughout the research screening and selection process by employing a multi-step dispute resolution approach. Beginning with a systematic eligibility checklist based on specified inclusion and exclusion criteria, two reviewers assessed all abstracts and titles. The two reviewers had a formal discussion to resolve disagreements about the study’s relevance or design. If consensus was not attained, a third senior reviewer was consulted. The third reviewer examined controversial issues independently and justified their inclusion or exclusion. Decisions with reasons were documented in a shared review log to provide clarity. This systematic technique prevented research from being arbitrarily included or excluded, thereby contributing to methodological quality assurance.

Data extraction and synthesis

Structured data extraction frameworks enable consistent and rigorous data extraction and synthesis. Detailed study characteristics, intervention information, population demographics, adherence indicators, and patient outcomes were retrieved for comparison. A standardised form is used to extract data, reducing bias and ensuring reviewer uniformity. Categorising data into themes enables systematic synthesis across multiple study contexts. Table 2 highlights key data from each study concerning study and intervention parameters.

Theme synthesis helps organise data by identifying standard intervention procedures, success factors, and implementation challenges. Data analysis evaluates patient engagement, behavioural modification methods, and provider integration to assess whether mobile health therapies improve prescription adherence. The study examines implementation challenges, including privacy, digital literacy, healthcare system integration, and user experience. These results address gaps in understanding the advantages and disadvantages of mobile health treatment and inform future research and policy. Using a panel of experts to assess data before extraction and synthesis ensures approach consistency and reliability. Reviewers double-verify the retrieved data to ensure accuracy and reliability and evaluate the interpretations for proof. A consensus-based approach resolves disputes. The final synthesis offers qualitative and quantitative insights through a narrative presentation of key outcomes, tabular summaries, and graphical representations. This scoping study employs a thorough and systematic approach to demonstrate how mobile health applications can improve medication adherence and patient outcomes. The findings aid digital health innovation, clinical practice improvement, and evidence-based policymaking. This study’s comprehensive literature synthesis helps healthcare providers, lawmakers, and tech developers build better digital health solutions to encourage patients to take their medications as prescribed.

In line with the scoping review aims, the methodological quality and bias risk of the included studies were not evaluated. This review followed PRISMA-ScR guidelines by mapping the literature, describing intervention characteristics, and identifying knowledge gaps rather than evaluating trial internal validity. A panel of domain experts was engaged throughout the protocol creation and data charting process to explain eligibility criteria and ensure subject relevance, thereby avoiding the exclusion of research due to methodological rigour or bias. All studies that satisfied the inclusion criteria, regardless of quality, were kept for the review’s comprehensiveness and exploratory nature. It further corrected or removed references that imply an official quality review to avoid confusion.

Study design distribution

Randomised controlled trials (RCTs) were the most prevalent research technique, accounting for 48% (153 out of 319 studies) of the investigations. The remaining study comprised 35% (112 out of 319) observational and 17% (51 out of 319) mixed-methods studies. RCTs compared digital health treatment groups to control groups to demonstrate the effectiveness of mobile health interventions. In contrast, observational research examined mobile health app use and results in natural settings. A mixed-methods study employed both quantitative and qualitative data to investigate patient engagement and challenges associated with new technology. Table 3 summarises the study design distribution for ready reference.

Effectiveness of mobile health applications on medication adherence

Over 85% (271 out of 319 articles) of the studies revealed that mobile health interventions increased medication adherence. These advantages were most closely linked with personalised medication reminders, behavioural reinforcement, and real-time involvement from healthcare practitioners. Studies utilising social support, gamification, and AI-driven adherence monitoring have reported higher adherence rates and increased patient involvement. Most studies used self-reported adherence, i.e., 66% (211 out of 319), followed by pharmacy refill data, i.e., 50% (159 out of 319) and electronic monitoring, i.e., 30% (96 out of 319). Electronic monitoring devices and smart pill bottles give more objective adherence data. However, observational studies often used self-reported adherence. Table 4 presents the data collection methods and their respective advantages and disadvantages.

Impact of mobile health applications on patient outcomes

The study examined how mobile health therapies improved adherence, clinical results, QoL, and patient satisfaction. In 72% (230 out of 319) of studies, blood pressure, glucose, and cholesterol levels improved, particularly for long-term health concerns such as diabetes, hypertension, and cardiovascular disease. In addition to medication management, 58% (185 out of 319) of studies showed that lifestyle modifications and education enhanced QoL. Eighty per cent (255 out of 319) of studies found that patients were satisfied with their mobile health applications. Studies examined numerous patient outcomes. The most common outcomes were patients’ self-reported health status, QoL metrics, satisfaction ratings, and clinical biomarkers (e.g., haemoglobin A1c, blood pressure), followed by medication adherence rates. In addition to adherence, health behaviours, psychological well-being, and long-term disease management are all influenced by mobile health applications, as indicated by the spectrum of outcome measures. Table 5 shows a breakdown of patient outcomes across studies.

Although promising evidence exists, several challenges must be overcome before mobile health applications can be widely adopted. Many studies (42%) cited app usability, device compatibility, and data privacy as barriers. Finding patients to engage actively was another challenge. Research revealed that 35% of users quit using apps because they weren’t interested, the design was unappealing, or they struggled to integrate them into their daily lives. The health system’s inability to interface with electronic health records (EHRs) and healthcare practitioners’ Lack of training are significant concerns against the use of mobile health apps in clinical practice.

Role and challenges of AI in mobile health-based medication adherence

AI/ML integration in mobile health interventions

Although approximately a quarter (n = 79) of the included studies utilised AI or ML in mobile health therapies, the quantity and quality of these approaches differed. AI is used in healthcare to predict how patients will adhere to their prescription regimens, provide personalised alerts and reminders, and monitor and deliver real-time feedback on data. However, just 21 studies (6.6%) included empirical data to validate or evaluate AI models. These studies provide accuracy, sensitivity, precision, and Area Under the Curve (AUC) performance measures, indicating a strong implementation of AI. AI was regularly referenced as a design component. However, only 4.1% (n = 13) addressed algorithmic transparency, which includes bias mitigation and explanation. The ethical use of AI in online healthcare is a significant issue. Figure 2 presents a summary of AI applications in the examined mobile health studies.

Key themes identified

Pharmacological medication compliance effects

The most significant finding was that mobile health applications enhanced medication adherence. In 85% of trials (271 out of 319 studies), mobile health therapies were found to increase drug adherence. The most often recognized feature was medication reminder systems, which increased adherence in chronic illnesses, including HIV, hypertension, and diabetes, when integrated. Studies that combined healthcare professional contacts with cellphone reminders had adherence rates 30–40% higher than baseline. Gamification and rewards further increased adherence. In studies using these methods, adherence rates increased by 15–20%, especially among younger participants. Tailoring feedback and reminders to patient data, such as symptom reports and medication history, increased adherence. Personalising therapy for each patient has a more lasting impact on adherence. Figure 3 presents key statistics on mobile health program adherence. Table 6 presents key statistics on mHealth program adherence.

Table 6:

Impact of mHealth interventions on medication adherence.

Type of intervention	Number of studies (n = 271)	Adherence improvement (%)
Medication reminders	192	30–40%
Behavioral reinforcement	94	15–20%
Personalized interventions	85	25–35%

DOI: 10.7717/peerj-cs.3190/table-6

AI vs conventional approaches to medication adherence

Customisation, scalability, and real-time reactivity distinguish AI-driven medication adherence therapies from conventional methods. Over time, manual pill counts, self-reported diaries, and regular provider follow-ups have become inefficient, memory-bias-prone, and unable to keep up with patients’ changing habits. However, AI solutions utilise predictive analytics and machine learning algorithms to tailor adherence aids to each patient’s risk profile, behaviour, and medical history (Li et al., 2024). AI models may predict non-adherence using physiological data, app usage habits, and contextual cues (e.g., mood, time of day) (Robinson et al., 2024). For instance, preemptive alerts and relevant actions are possible. These technologies, combined with mobile health systems, offer prescription reminders, dosage adjustments, and chatbot-assisted counselling. Despite its speed and scalability, AI has data reliance, algorithmic bias, a lack of transparency (“black-box” models), and legal uncertainty. Classic approaches can perform better in areas with little digital infrastructure. Thus, human-led care delivery with AI-enhanced insights may be the most effective approach (Bhavana & Mithra, 2025). Future research should compare the two strategies’ patient satisfaction, long-term adherence rates, and clinical outcomes to determine which is more effective and viable in different healthcare settings.

Practical implications for healthcare and mobile health developers

These findings are beneficial for healthcare providers and mobile health app developers. When paired with automated reminders, health monitoring, and individualised therapies, mobile health apps can enhance medication adherence. This makes them useful for treating chronic diseases. The findings suggest that physicians should strongly recommend these apps, particularly for patients with chronic conditions that need continual monitoring. Using mobile health apps, physicians and patients can monitor each other’s health and adherence to medications. Apps provide patients with timely feedback on their progress. The results indicate that mobile health app developers should prioritise user-centric designs, incorporating data security and targeted feedback. AI integration for individualised therapies improves adherence and patient engagement. Thus, it must be prioritised. The statistics reveal that apps should consider patient preferences, including simplicity, usability, and compatibility with wearable devices, to ensure long-term usage and optimal performance. With the emergence of patient-centric solutions, mobile health developers must focus on applications that support disease management, wellness, and medication reminders.

Theoretical contributions and policy implications

This analysis advances digital health treatment theory by providing evidence of the effectiveness of mobile health apps in improving medication adherence and health outcomes. The study provides a deeper understanding of how app features, such as reminders, monitoring, and AI interventions, impact health outcomes. These findings may impact the design and deployment of mobile health technology research and contribute to the literature on behavioural health and digital health therapies. This has significant implications for the regulation of mobile health interventions and digital health policy. Legislators must regulate mobile health apps to protect patient data and security as they become more incorporated into healthcare systems. AI with real-time monitoring raises concerns about data security; explicit norms and legislation are necessary to protect patients’ data. Public health policy should also promote the use of mobile health apps to enhance drug adherence and improve health outcomes. In resource-constrained nations with limited healthcare access, this is crucial. This also suggests that national health systems invest in mobile health app infrastructure, particularly in low-resource areas. Training healthcare professionals to utilise mobile health technology may enhance the scalability of these therapies, particularly for managing chronic diseases.

Limitations of the review

This review highlights the effectiveness of the mobile health app, although it has certain limitations. First, even with strict inclusion criteria, publication bias remains a possibility because research with favourable outcomes is more likely to be published. Thus, the evaluator may have missed unfavourable or confusing research. Since many studies have not been followed up, it is difficult to determine whether improvements in medication adherence and health outcomes are permanent. Most mobile health research on chronic illness management focused on short-term outcomes; hence, long-term data is scarce.

Another issue is that research uses diverse intervention designs, patient groups, and methodologies. Due to this variability, generalisations about the effectiveness of the mobile health app are challenging. Patient demographics and the type of chronic disease may also impact the success rates of mobile health apps. Future studies should employ more consistent methods and incorporate diverse patient groups to understand app performance characteristics accurately. The study found a strong correlation between reminders, health monitoring, AI interventions, and adherence; however, more research is required to determine the optimal channel. Future studies should compare app features to discover what makes these therapies work.

These scoping review findings are reliable due to their rigorous and transparent process. PRISMA-ScR compliance facilitated a comprehensive and repeatable search across multiple databases, thereby enhancing internal validity. Two reviewers screened and extracted data to prevent bias, and the inclusion criteria were clearly defined. The synthesis was reinforced by using only real-world data from randomised controlled trials, observational research, and mixed-methods designs. The diversity of individuals, situations, and intervention types makes outcomes more externally valid and generalizable, even if contextual considerations should be taken into account.

Despite the best efforts, it may have overlooked relevant studies in specialist healthcare periodicals not indexed in the study databases. PubMed, Scopus, Web of Science, and IEEE Xplore were included in our assessment due to their broad coverage, comprehensive indexing of academic works, and substantial weighting in the fields of medicine and technology. PubMed, which focuses on clinical and biomedical research; Scopus and Web of Science, which index social sciences, health informatics, and digital health; and IEEE Xplore, which captures literature on health informatics, digital health, and AI-driven technologies, were included. In addition to the study’s main search, the researchers manually screened reference lists and grey literature, including WHO reports and conference proceedings, to avoid missing crucial material from more specialised sources.

Research and development recommendations

While the findings were promising, further research is necessary to address several outstanding issues. The long-term consequences of mobile health therapies are a key area of research. Future longitudinal studies should investigate how habit-building, app fatigue, and patient demands influence long-term usage habits. Additionally, determining whether patients continue to use mobile health applications beyond initial adoption would be necessary. A comparative investigation is needed to determine the optimum health aspects. Mobile health applications may benefit from a more systematic assessment of AI, gamification, social support, and behavioural nudges. Additionally, research must evaluate hybrid therapies combining mobile health apps with traditional healthcare models, including telemedicine consultations and pharmacist-led prescription reviews.

Demographic and socioeconomic aspects that affect mobile health efficacy should also be studied. Most research excludes disadvantaged groups, including older people, low-income residents, and those with minimal digital literacy, in favour of mobile and internet users. Future research should investigate how healthcare inequities, cultural beliefs, and socioeconomic status influence mobile health usage and effectiveness. Researchers should also develop simpler medication adherence apps to make them more accessible to a wider audience. Regulators and policymakers need further study to set standards and best practices for mobile health app development and integration into healthcare systems. AI-driven health solutions are developing rapidly, necessitating stringent data privacy regulations to safeguard patient data. Due to the increasing volume of sensitive health data collected and analysed by mobile health apps, future research should prioritise cybersecurity improvements. Policymakers want healthcare providers and patients to adopt mobile health apps, particularly in public health campaigns, so they should consider payment laws and subsidisation models.

Most research on mobile health applications for medication adherence has focused on short-term benefits, generally a few weeks to several months. The Lack of longitudinal data makes it challenging to evaluate long-term clinical outcomes, adherence, and behavioural changes across patient groups. The difficulty and cost of conducting long-term cohort or randomised controlled trials, as well as the effects of user fatigue and attrition on app engagement over time. The rate of technological change renders mobile health tools outdated or drastically improved before such evaluations can be finished. The lack of integration between app usage data and electronic health records makes it difficult to measure the health impacts of all understudied areas. Future research should focus on longitudinal and hybrid implementation-effectiveness studies to address these obstacles. Study designs should adapt to app updates. Explore EHR-connected real-world data to track patient trajectories following the initial intervention. Funding methods and regulatory incentives may support long-term investigations of mobile health solutions in real-world healthcare settings.

Ultimately, policymakers, software developers, and healthcare providers must collaborate to innovate digital medication management. Future research should integrate public health, medicine, behavioural science, and AI to develop patient-centred digital health solutions. As mobile health technologies continue to advance, medication adherence may become a digitally optimised, patient-driven success story.