Medication Adherence: Digital Health Interventions

METHOD:

The review was performed in line with the a priori protocol registered with PROSPERO, the international systematic review registry, hosted by the National Institute for Health and Care Research (NIHR) (registration number CRD42024601867). The methods used in this review were structured according to the Cochrane Handbook for Systematic Reviews of Interventions, version 6.2. 26 However, the meta-analysis was performed using JBI SUMARI software due to changes in institutional availability and the inclusion of randomized controlled trials (RCTs) and observational studies.⁽²⁹⁾

1. Inclusion criteria:

We included a study if it fulfilled the following inclusion criteria met: (1) the study described a patient-centered Internet intervention, (2) the study described an intervention for patients(3) who take prescription medication for a chronic condition, adherence was at least one of the outcome measures, (4) the study was quantitative, and (5) published in either the English or Dutch.⁽⁹⁾ Extracted data were descriptively synthesized and the historical evolution of the field was analysed. An initial new taxonomic approach was derived based on the different conceptual approaches identified in the literature review and discussed internally within the ABC project team in June 2009 in Aberdeen, UK. The taxonomic approach was then reviewed in light of the papers identified and redefined in June–August 2009.(²⁸⁾ Studies comparing the intervention with standard care (i.e. no DHI) were included in this review. This involved written, verbal, and pictorial instruction and pamphlets. Outcomes Studies assessed the outcome of medication adherence by patients (e.g., self-report, pill count, prescription retrieval), or defined from included studies. Outcomes at 1, 3 and 6 months were included as available from included studies data. Types of study The study designs included RCTs, quasi-experimental trials, case-control studies and cohort studies.⁽²⁷⁾

Identification & Screening of Studies

Records identified through database searching

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Records with duplicates removed

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Title and abstract screened Records

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Full-text articles assessed for eligibility

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Studies that meet inclusion criteria

✓ Internet intervention that is patient-centered

✓ Intervention administered to patients

✓ Patients on chronic condition prescription medication

✓ medication adherence was an outcome measure

✓ Research study (quantitative)

✓ Gepubliceerd in het Engels of Nederlands

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Studies included for review

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2. Search Criteria:

Electronic databases (PubMed, Embase, CINAHL, SScopus and Web of Science) were searched from inception to 14 May 2025. Keywords, synonyms, MeSH terms, Emtree and CINAHL subject headings were combined using Boolean operators (File S1) for three concepts: ‘older people’, ‘digital health interventions’ and ‘medication adherence’. We also hand-searched reference lists of related articles and reviews. d. Only original peer-reviewed articles in English were included. d. Citations were uploaded to Covidence systematic review software (Veritas Health Innovation, Melbourne Australia); duplicates were removed and screening was completed in two stages. Two reviewers independently screened title and abstracts and full texts. Differences were settled by discussion. ⁽¹³⁾

The resources we use to sreach is , example

1. Research gate

2. Science direct

3. Pune mad

4. Google scholar

5. Willey online library

6. International journal of pharmaceutical science

3. Eligibility Criteria:

Studies that met the eligibility criteria included the following: (1) a randomized controlled trial featuring a standard care control group; (2) implementation of an interactive eHealth intervention targeting either the patient or their caregiver; (3) medication adherence identified as the main outcome; (4) an overall participant count of a minimum of 50 adult individuals utilizing long-term medication as specified by Zwikker et al.⁽¹⁰) Research involving solely individuals below the age of 65 or lacking distinct age specifications. Investigations where digital health was not the primary focus, or merely served as an auxiliary element, to guarantee that the influence of the digital aspect on adherence to medication could be distinctly evaluated. Studies that did not include a comparison group. Methodological outlines, presentations from conferences, viewpoints, editorial writings, critiques, or literature reviews.⁽¹⁵⁾

4. Data Extraction:

Data were collected independently by two reviewers (LCA and AK) through a tailored data extraction form. Baseline features of participants were gathered, which included factors like age and gender. Information regarding medication adherence was sourced either from published studies or by reaching out to the authors of the original research via email. In instances where two different adherence measurements were provided, the one deemed most relevant to the outcome metrics of the other studies included was selected for the meta-analysis. For instance, one research project measured adherence in terms of how many times a patient used an electronically monitored dispenser and the weight of the dispensing container during follow-up. The reviewers concurred that the dispensing action was more aligned with measures from the other studies incorporated.⁽¹⁷⁾

5. Quality Assessment:

The possibility of bias was evaluated by two evaluators, utilizing the quality rating instrument from the Cochrane Handbook for Systematic Reviews of Interventions. We classified the likelihood of bias as high, low, or uncertain for each of these areas: random sequence generation, allocation concealment, blinding of both participants and personnel, blinding of outcomes assessment, incomplete outcomes data, selective reporting of outcomes, and other types of bias.⁽¹⁴⁾

6. Study Design:

A randomized two-arm pre-posttest control group approach was utilized with a follow-up duration of 12 weeks. All individuals involved received standard treatment, whereas those in the intervention group also installed and utilized the Medisafe application on their personal smartphones throughout the research.⁽¹⁸⁾

7. Risk of Bias Assessment :

Cochrane's tools for evaluating risk of bias were utilized to evaluate the methodological caliber of the research, specifically employing the RoB 2 tool for randomized controlled trials, which also includes the version tailored for cluster-randomized trials. For non-randomized studies, the ROBINS-I version 2 was utilized.

Two reviewers conducted an independent evaluation of each study, and any differences in opinion were settled through discussion. The Cochrane RoB 2 Excel tool was implemented for the assessment of RCTs, while a modified Excel tool based on earlier research was used for ROBINS-I evaluations. A diagram illustrating the RoB assessments, known as the traffic plot, was created with the use of the robvis Shiny App. ⁽²⁾

8. Study Outcomes:

The results of the research highlighted the approval and practicality of MPO and PRA, as well as their integration, from the viewpoints of both participants and caregivers by utilizing mixed methods. The quantitative evaluation of usability used the System Usability Scale (SUS), which was gathered separately for both MPO and PRA. Assessments of acceptability and feasibility were conducted through guided group discussions (FGD) with participants and detailed interviews (IDIs) with caregivers. One of the exploratory endpoints involved evaluating modifications in medication adherence through the Medication Adherence Rating System-5 (MARS-5) and counting pills.⁽²¹⁾

RESULT:

The search of literature led to a total of 527 entries from various databases. Subsequently, 340 items were removed from consideration due to their repetitive nature during the first screening process. Out of the remaining 187 records, titles and abstracts were evaluated, which narrowed it down to just 77 for comprehensive text examination. Ultimately, 67 entries were ruled out, and only 10 that fulfilled the inclusion criteria were taken into account.⁽²⁶⁾ We presented a descriptive summary of the insights gathered from the studies that were selected. The findings were assessed through thematic content analysis. The review group examined the significance of the data results to understand their relevance to the research goals and objectives.⁽²⁴⁾

DIGITAL HEALTH INTERVENTION:

Digital mental health interventions for self-help have been proposed as a cost-effective and scalable solution to enhance access to mental health care. ⁽³⁰⁾

Digital health tools include a wide variety of resources designed to assist in health management and improve outcomes. Key technologies encompass:   ⁽¹¹⁾

a. Mobile Health Applications (mHealth Apps) 

b. Information and Communications Technology (ICT) 

c. Telehealth Services 

d. Wearable Devices 

e. Remote Patient Monitoring (RPM) 

f. Artificial Intelligence (AI) and Machine Learning

• In the appropriate amount 
• At the suitable moment 
• In the correct manner 

• Mobile Health Devices:

Nearly every application examined included some form of a reminder feature to support medication compliance, with a total of 387 apps representing 92.1% (387 out of 420) of those analyzed. The predominant subcategory was Push Notifications, employed by 80.2% (337 out of 420) of the apps. In second place were Alarms, which were featured in 134 applications, while a mere 1.4% (6 out of 420) of the apps integrated SMS Reminders.^(6).

An application like Perx can be utilized in clinical environments outside of research for routine practice. Users with smartphones can install and utilize this application; however, adding additional languages and features for screen reading will enhance its accessibility. It is essential to implement measures that prevent the app from exacerbating health disparities among individuals who lack access or face language obstacles. Furthermore, integrating the app with pharmacy and general practice services may enhance its overall effectiveness. To evaluate and guarantee the app's feasibility and effectiveness on a broader level, it could be beneficial to consider more qualitative and implementation research, including cost-effectiveness analyses from the perspective of healthcare providers.⁽⁷⁾

• Telehealth Service:

Telehealth refers to the provision of healthcare services through advanced communication technologies from a distance. The synergy of breakthroughs in medical devices, software development, data management, and communication methods has transformed telehealth into an invaluable resource. This encompasses various practices and specialties, as exchanges between healthcare providers and patients occur through phone calls, emails, video conferences, the Internet, and remote monitoring tools. By minimizing the necessity for travel and lengthy waiting times, telehealth enhances operational efficiency without raising overall costs. It also extends healthcare access to those residing in isolated or underrepresented regions and ensures that patients receive care that is of equal or superior quality. Moreover, the convenience, improved access to services, and reduced anxiety associated with telehealth can lead to higher levels of patient satisfaction.⁽¹¹⁾

• Smartwatch and Wearable Devices:

Smartwatches like the Apple Watch and Fitbit are increasingly integrated into systems that remind users to take their medications. The advantages are clear. Notifications, alerts, and visual cues are sent directly to a user’s wrist. No need for a phone. For many, this simplifies things—resulting in fewer distractions from devices and quicker reminders. A randomized controlled trial explored the usage of a medication-alert wristwatch among older adults. What did they find? Over a span of 12 weeks, participants showed considerably improved adherence to their medication routines and gained increased confidence in managing their prescriptions. Additionally, the average systolic blood pressure improved to around 120 mm Hg, in contrast to about 142 mm Hg in individuals without the device (p < 0.001). Various scoping reviews support this overall trend—wearable technology appears effective in aiding individuals across different health issues through those consistent nudges—vibrations, sounds, flashes—delivered to a location that is hard to overlook.⁽¹²⁾

• The future of digital health:

Healthcare providers and policy makers carry a significant obligation to actively engage patients in the collaborative process of care design and decision making, as well as to assist them in navigating a vast array of digital health tools. If this involvement is lacking, patients might resort to unproven alternatives or digital solutions that they cannot fully understand on their own. By fostering shared responsibility, doctors can also alleviate the strain associated with selecting appropriate treatments and managing the impacts of those choices. Disruptive innovations have the potential to eliminate the monotonous aspects of their roles, allowing more room for personalized attention and focus on patient interactions. Skills that are difficult to replicate, such as compassion, social support, and the human connection, are central to delivering quality care. (²²⁾

ARTIFICIAL INTELLIGENCE:

Mobile applications driven by Artificial Intelligence (AI) utilize logical algorithms that can learn from information and autonomously make decisions based on broadly applicable principles (Zavaleta-Monestel et al., 2025). These apps have demonstrated their usefulness in tracking and enhancing medication adherence (Zavaleta-Monestel et al., 2025). A research project carried out by Labovitz et al. (2017) led to the creation of an AI-centric smartphone application designed for stroke survivors undergoing direct oral anticoagulant therapy. This app employed a neural network to recognize the patient and the medication prescribed, verify ingestion via the phone's camera, and issue reminders for medication. The findings indicated that users of the app exhibited a 100% adherence rate, in stark contrast to just 50% within the control group, alongside receiving favorable feedback from patients. Nonetheless, due to its small sample size (n = 28), additional studies involving larger cohorts are essential to assess its long-term impact. In a similar vein, Bain and their research team (Bain et al., 2017) employed an AI system that featured facial recognition and drug verification for the real-time observation of schizophrenia patients throughout a 24-week clinical investigation. Results showed that adherence rates in the group monitored by AI surpassed those in the control group—receiving modified direct observation therapy—by 17.9%. Additionally, a voice-driven conversational AI application was tested in a clinical trial to assist patients with type 2 diabetes (Nayak et al., 2023). The findings revealed that insulin adherence in the AI-voice group outperformed the standard care group by 32.7%. ^(32).

ROLE OF HEALTH CARE SERVICES: