Introduction
The number of adults with hypertension worldwide increased from 594 million in 1975 to 1.28 billion in 2019, with most of the increase occurring in low- and middle-income countries.1 However, only 21% of the 1.4 billion individuals aged 30 to 79 are able to effectively control their hypertension.2 Hypertension is managed with pharmacological treatment and lifestyle modifications. Pharmacological treatment involves the administration of essential antihypertensive drugs, namely angiotensin receptor blockers (ARB), calcium channel blockers (CCB), angiotensin-converting enzyme inhibitors (ACEi), and thiazide diuretics.3 Lifestyle modification to lower BP include weight loss, regular exercise, avoiding tobacco consumption, and maintaining a healthy, low-salt diet. However, several factors can affect the desired outcome of the therapy, including patient’s medication adherence, drug-related problems, and the patient’s overall health condition.4
Previous studies have demonstrated that interventions initiated by pharmacist can enhance patient’s medication adherence and BP management in patients taking antihypertensive medication.5,6 In this digital era, pharmacist-led DHI presents a promising approach to improve patient health. DHI refers to the use of digital equipment complemented with wireless systems such as text-messages reminders, telephone monitoring, and web-based intervention, to enhance healthcare outcomes.7 However, there are currently no reviews specifically assessing digital interventions for hypertensive patients that are led by pharmacists, despite their numerous potential benefits for both patients and pharmacists. These interventions offer several advantages, including cost-effectiveness, resource efficiency, and improved accessibility.8
The aim of this study was to assess the characteristics of pharmacist-led digital health interventions for patients with hypertension from published articles of RCTs. Such understanding may help identify key features that make these interventions effective and inform future strategies to optimize hypertension management through digital pharmacist-led care.
Material and Methods
Search Strategy
This study adhered to the PRISMA guidelines to ensure full disclosure of methods and findings. Relevant studies were identified through PubMed Database, spanning publications from December 1996 to May 2024. The literature retrieval process incorporated key concepts including: i) digital health interventions; ii) pharmacist and pharmaceutical care; and iii) hypertension. The following keywords were used: (Telemedicine[MeSH] OR telepharmacy[tiab] OR clinical decision support[tiab] OR automated medication system[tiab] OR automated pharmacy system[tiab] OR bar coding[tiab] OR electronic medication order entry[tiab] OR electronic medication management system[tiab] OR automated dispensing[tiab] OR computerized reminder system[tiab] OR information technology[tiab] OR medication ordering entry[tiab] OR electronic medication ordering and administration system[tiab] OR remote consultation[MeSH] OR electronic consult*[tiab] OR digital technolog*[tiab] OR teleconsult*[tiab] OR mhealth[tiab] OR m-health[tiab] OR multimedia[tiab] OR virtual[tiab] OR mobile health[tiab] OR telemedicine[tiab] OR electronic health record[tiab] OR telehealth[tiab] OR telecare[tiab] OR telehealth care[tiab] OR mobile health intervention*[tiab] OR mobile applications[tiab] OR mobile telemedicine[tiab] OR mcare[tiab] OR m-care[tiab] OR mobile communication[tiab] OR mobile technolog*[tiab] OR multimedia technolog*[tiab] OR mobile devic*[tiab] OR app[tiab] OR apps[tiab] OR mobile app*[tiab] OR website*[tiab] OR internet consultation*[tiab] OR internet monitoring[tiab] OR video consultation*[tiab] OR video monitoring[tiab] OR telephone*[tiab] OR mobile phone*[tiab] OR smart phone*[tiab] OR smart-phone*[tiab] OR text messag*[tiab] OR text messaging[tiab] OR SMS[tiab] OR short messag*[tiab] OR multimedia messag*[tiab] OR multi-media messag*[tiab] OR website platform[tiab] OR web-based medication platform[tiab] OR web-based application[tiab] OR web-based tool[tiab] OR electronic health[tiab] OR ehealth[tiab] OR e-health[tiab]) AND (Pharmacist*[tiab] OR pharmaceutical care[tiab]) AND (hypertension[tiab] OR blood pressure management[tiab]).
Inclusion and Exclusion Criteria
Inclusion to studies was done to RCTs conducted on adult patients with hypertension that assessed how pharmacist-initiated DHIs affect the wellbeing of the patients. The assessment focused on clinical outcomes, specifically on the reduction of BP, improvement in medication adherence, reduction in adverse drug reactions (ADRs) and medication errors. Exclusion criteria comprised trials that: (a) did not utilize DHI, (b) were not RCTs, (c) did not assess hypertension-related outcomes, (d) were not pharmacist-led, (e) had a different primary intervention method, (f) were not published in English, and (g) were literature reviews/systematic reviews, case reports/case series, or conference abstracts.
Screening and Data Extraction
Two investigators (GS and A) independently screened and evenly divided all retrieved records, evaluating both titles and abstracts based on predefined criteria. Conflicting screening assessments were reconciled through consultation with WNI as a third reviewer. Following the title and abstract screening phase, a full-text review was conducted for eligible articles to proceed with the final inclusion assessment. A modified PRISMA flowchart was used to visualize the screening and selection methodology. Relevant data from qualifying studies were extracted using predefined form, documenting general study characteristics, study design, and key findings.
Risk of Bias Assessment
Cochrane Risk of Bias (RoB) 2.0 tool was used to evaluate methodological quality and detect potential bias affecting internal validity. The assessment covered the following domains: (D1) Bias arising from the randomization process; (D1b) Bias arising from the timing of identification and recruitment of participants; (D2) Bias due to deviations from intended intervention; (D3) Bias due to missing outcome data; (D4) Bias due to measurement of the outcome; (D5) Bias in selection of the reported results. The assessment covered the following domains applicable to RCTs. However, D1b was only applied to cluster-RCTs, as this design involves participant recruitment after cluster allocation. Each domain was judged as having low, some concerns, or high risk, followed by an overall RoB judgment for each study. Studies with a low RoB were considered more methodologically robust and therefore preferable in the overall interpretation of findings.
Results
Literature Search and Selection Process
The process of article selection is displayed by Figure 1 through a flowchart. Two hundred and two articles retrieval from PubMed was done in the initial search. Only 21 articles proceeded to full-text assessment after titles and abstracts screening phase, while 181 studies did not. Seven studies were further excluded throughout the full-text review based on specific criteria, resulting in 14 articles that fulfilled the eligibility requirements and were included into the final analysis.
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Table 1 General Characteristics of the Studies
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Figure 1 Flow Diagram of Systematic Review.
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Characteristic of the Study
Table 1 summarizes the findings from the 14 analyzed studies. The United States of America accounted for the majority of the studies (n = 10),12,13,15–22 while the remaining were conducted in Ghana (n = 1),9 Denmark (n = 1),10 United Arab Emirates (n = 1),11 and China (n = 1).14 The population varies significantly in size, ranging from 31 to 4078 patients. The DHI used in these studies included telephone-based (n = 6), web-based (n = 5), and mobile-based intervention (n = 3). Five studies used combination of these methods. The majority of the studies focused on assessing BP (n = 13)9–20,22 and medication adherence (n = 4),9,11,14,21 while one study assessed DRPs.11
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Table 2 Main Findings of the Studies
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Table 2 presents a detailed analysis of the 14 studies that were part of this review. It outlines key aspects of each study, including the comparison of DHI, the follow-up period, and the main findings of the studies.
Telephone-Based Intervention
Out of fourteen studies reviewed, six studies utilized telephone-based monitoring as an intervention.9,10,15,16,21,22 Five of the studies employed combined methods, either telephone and web-based or telephone and mobile-based interventions.13,14,17,19,20 However, telephone calls were used less frequently compared to web or mobile interventions. Two studies found that telephone-based interventions significantly decreased SBP and DBP.9,22 One study found that BP was effectively lowered, but comparative analysis revealed that the result among intervention group (IG) and control group (CG) did not differ substantially.16 Another study showed no significant decrease except for DBP.10 However, three studies show a significant improvement in medication adherence.9,10,21 Besides that, this method also effective in promoting therapeutic lifestyle modifications, as evidenced by the significant increase in participants adopting healthier habits.9
Web-Based Intervention
Throughout the analysis, we found five studies that used web-based interventions.12,13,18–20 While three out of five studies were combined with other methods, the entirely web-based studies allow the patients to receive training in utilizing not only the home blood pressure monitor (HBPM) but also the website in order to fulfill their health-related necessities.12 All of the studies showed that web-based interventions led to a significantly greater result in reaching the controlled BP state compared to UC or the CG. These results are also related to some key factors including medication adjustments, patient–pharmacist relationship, and treatment plans.20
Mobile-Based Intervention
We found three studies that used mobile-based interventions in which all of them are combined with telephone-based method.11,14,17 The interventions were not done purely with one mobile application. Communication with the patients were mostly done with phone calls, while data collection such as the BP readings, demographic information, medical history, antihypertensive medications, and laboratory values are obtained through mobile application and/or electronic health record (HER). All of the studies showed that mobile-based interventions led to the greater decrease of BP compared to the CG. Two of the studies also showed that not only medication adherence, but also the knowledge of the patients were significantly improved.11,14
Risk of Bias
Cochrane RoB tool was used to ensure the validity or quality of the included RCT and cluster-RCT studies. Among five RCTs, two studies9,10 showed a low overall risk of bias, while one study14 exhibited some concerns, and two studies17,22 demonstrated high overall risk of bias. Three biases comprised bias arising from the randomization process (D1), bias due to deviations from intended intervention (D2), and bias due to missing outcome data (D3) occurred the most, observed in 60% of RCTs. Bias due to measurement of the outcome (D4) was low in all RCTs. From nine cluster-RCTs, seven studies11–13,15,18,20,21 exhibited low overall risk of bias, while two16,19 posed some concerns. All bias generally demonstrated low risk of bias, with bias arising from the randomization process (D1) as observed in 22,22% of the cluster-RCTs. Further details on bias assessment are available in Tables 3 and 4.
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Table 3 Risk of Bias Assessment of RCT Studies
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Table 4 Risk of Bias Assessment of Cluster-RCT Studies
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Discussion
To our knowledge, this is the first systematic review examining the impact of pharmacist-led DHI on treatment outcomes in hypertensive patients. Overall, the findings are predominantly positive. Eight DHI studies (57.14%) reported both SBP and DBP were reduced significantly over conventional therapy.9,11,12,17–20,22 One study (7.14%) found a significant improvement only in DBP, with no meaningful change in SBP.10 One study (7.14%) showed the methods can effectively lower BP, but results of IG and CG did not differ substantially.15 One study (7.14%) also finds that odds ratio (OR) of BP control in IG is 3.64 times higher than in CG.13 All of the studies that assessing medication adherence (n = 5, 35.71%) showed an increase in the IG compared to CG.9,11,14,21 One study (7.14%) showed enhanced DRP identification by pharmacists in the IG, eg, unnecessary drug therapy, drug–drug interaction, and ADR than in CG.11
Telephone-based intervention that is used in six out of fourteen studies (35.71%) were shown to be the most common method. This is consistent with previous systematic reviews evaluating pharmacist-led digital health interventions for patients with diabetes, which found that telephone intervention methods were more frequently utilized compared to web- and mobile-based interventions.23 Despite being the most commonly used, telephone-based interventions are highly flexible and can be effectively combined with other approaches, such as web- and mobile-based platforms. This combination provides a more flexible and thorough approach, adapting to the most compatible approach for the patients. For instance, telephone-based interventions provide direct human interaction, which can enhance patient engagement and trust, while web and mobile platforms offer convenience and real-time monitoring capabilities.24
While most of the studies with telephone-based interventions (n = 4, 66.7%) showed notable reductions in SBP and DBP, one of them showed no significant decrease in SBP, with only DBP showing improvement. A potential explanation for this discrepancy is that unlike other studies, this particular study included patients who had already achieved their target BP levels.8 This limited the potential for further improvement, as there was a smaller margin for change.23 One study also reported a decrease in BP but no significant difference compared to CG. This could be attributed to the reduced intervention frequency, as delays in replacing and training clinical pharmacy specialists led to fewer patient encounters. Moreover, many participants had well-controlled risk factors at baseline, which may have minimized the intervention’s measurable impact similar to the previous study.13
Compared to the telephone-based intervention method, both web-based and mobile-based interventions are more effective, as all studies using these methods successfully achieved the BP goal or maintained controlled BP. Although not as straightforward as telephone-based monitoring, these methods have been proven effective, provided that patients receive initial training on using the website and/or mobile application and are willing to engage with the pharmacist. A few of the studies also elaborated that through websites and mobile applications, pharmacists can conveniently provide patients with more personalized medication adjustments and/or frequent reminders.11,17 Thus, the patient’s clinical outcomes can be significantly improved without the need for frequent visits to clinics or hospitals. Meanwhile, pharmacists have a greater opportunity to identify DRPs for each patient.11
After a year of intervention, the results of IG and CG’s mean SBP did not differ substantially in one of the studies. While this suggests a higher control difficulty for SBP compared to DBP, it may also be influenced by the frequency of virtual meeting between the pharmacists and the patient. The virtual meeting’s frequency decreased from once every two weeks in the first six months to once a month for the remaining six months, which might affect patients’ medication adherence. This issue can be solved by maintaining a consistent follow-up schedule and/or tailoring treatment plans to specifically address SBP control.11
A study by Wang et al reported an interesting finding, where medication adherence in the IG reached 90% after three months.14 In comparison, another study conducted over six months achieved a slightly higher adherence rate of 96.5%, indicating that the three-month adherence rate was already quite high.25 However, a different study reported a significantly lower adherence rate of 42.7% after twelve months of intervention.26 The total sample sizes for these studies were 80, 116, and 4,078 participants, respectively. These findings suggest that shorter intervention periods may lead to high adherence rates initially, but maintaining adherence over longer durations, especially in larger populations, presents a greater challenge. Such results highlight the essential role of intervention design that not only can achieve high initial adherence but also sustain it over time, particularly in a real-world, large-scale settings. This is crucial as low adherence to hypertension treatment raises the potential of damaged organs and adverse cardiac issues.27
The results of this study underscore the potential of pharmacists’ involvement in DHI exclusively to hypertensive patients. While our findings indicate that most pharmacist-led DHI benefit patients, other studies have also explored the outcomes of these methods in populations with different diseases. Goldfien et al revealed that of patients with gout of IG attained a higher percentage compared to CG in reaching target serum uric acid (sUA) level ≤6.0 mg/dL (35% vs 13%, p = 0.03).28 However, Adams et al demonstrated that tobacco cessation attempts of IG and the CG did not differ substantially.29 Eldeib et al also found that medication adherence of IG and the CG for all cycles of metastatic colorectal treatment are not notably distinct.30 There are several factors that contributed to the lack of contrast between the IG and CG’s results, including the brief duration of the counseling session that led to patients’ lack of motivation in adhering to the designed therapy and the standard care’s quality for the CG. When the standard care is considered good enough for the patients, it would be difficult for the IG to demonstrate greater results.29,30 Compared to a review by Christy et al which focuses on the diabetes mellitus (DM) disease, our review has several differences. Most of the digital health intervention methods from both studies are the same, but one additional intervention method, text-message reminder, is not found in our study. The follow-up periods from the review by Christy et al are larger in variety, from 2 to 24 months, while ours are from 3 to 12 months. Apart from the hypertension and DM-specific clinical outcomes, patient’s knowledge is addressed only in our study. Additionally, the pharmacist-led DHI for hypertension has shown greater results compared to DM’s because most of the included studies indicated positive results. Meanwhile, only around half of the studies from Christy et al indicated positive results.23
While the included studies primarily focused on conventional DHI, recent advances in digital health technologies present exciting opportunities to complement existing pharmacist-led interventions. Emerging innovations like artificial intelligence (AI)-driven tools and wearable devices may further enhance hypertension care. For instance, AI-powered chatbots for medication reminders and machine learning algorithms to analyze blood pressure trends could help personalize care while reducing pharmacist workload.31 These tools can optimize resource allocation by automating routine monitoring tasks and identifying patients who require more intensive support. Moreover, the interactive nature of AI-powered platforms may help increase patient engagement and encourage sustained adherence to therapy. Likewise, wearable technologies capable of real-time BP monitoring may support more timely and proactive interventions. Although these tools were not featured in the studies included in this review, they represent promising directions for future research and integration into pharmacist-led care models.32,33
In addition to technological advancements, the global applicability of pharmacist-led DHI could be strengthened by expanding research beyond high-income countries. Most of the included studies (10 out of 14) were conducted in the United States, reflecting insights from well-resourced healthcare systems. Future investigations in low- and middle-income countries (LMICs) could explore how pharmacist-led DHI perform in different healthcare environments, particularly those with varying levels of digital infrastructure, literacy, and access. Adapting interventions with offline-capable platforms or incorporating community health workers could enhance feasibility and effectiveness in these settings.34,35 Such efforts would broaden the evidence base and support the global scalability of pharmacist-led DHI for hypertension management.
This study showcases its strengths through the use of comprehensive keywords and a thorough analysis of pharmacist-led DHI for hypertension. Our review highlights the potential benefits of these interventions, including improved BP control and reduction, as well as enhanced medication adherence. Additionally, Cochrane RoB tool was used to evaluate the potential of bias. While the primary focus is on pharmacist-led DHI, the study also considers various clinical outcomes, such as BP levels, medication adherence, and the identification of DRPs. There are several limitations to this study. First, the heterogeneity of the included studies, including variations in DHI and follow-up durations, prevented direct comparisons through meta-analysis. Secondly, the inclusion of only English-language articles may introduce selection bias. Lastly, most of the studies are conducted in United States of America, which is a developed country, thus further studies in developing countries are surely encouraged to improve generalizability.
Conclusion
This review of 14 RCTs found that pharmacist-led DHI for patients with hypertension, utilizing telephone, web, and mobile-based interventions, yielded predominantly positive outcomes. Most of these studies (57.1%) demonstrated improvements in BP control, while 35.7% showed enhanced medication adherence, and one study highlighted better identification of DRPs. Web and mobile-based interventions are recommended to be implemented in future enhanced service that is led by pharmacist, as all studies using these methods achieved the BP goal or maintained controlled BP. Interventions’ effectiveness is influenced by several factors, including intervention frequency, patient engagement, and personalized communication. However, generalizability may be limited, as majority of the studies (n = 10) were conducted in developed countries. Further research is needed to refine these approaches, ensuring their scalability and sustainability in diverse, real-world settings to enhance patient’s state of health.
Abbreviations
ACEi, Angiotensin-Converting Enzyme inhibitors; ADR, Adverse Drug Reaction; AI, Artificial Intelligence; ARB, Angiotensin Receptor Blockers; BP, Blood Pressure; CCB, Calcium Channel Blockers; CG, Control Group; CI, Confidence Interval; CKD, Chronic Kidney Disease; CVD, Cardiovascular Disease; DBP, Diastolic Blood Pressure; DHI, Digital Health Interventions; DM, Diabetes Mellitus; DRPs, Drug-related Problems; HBPM, Home Blood Pressure Monitoring; IG, Intervention Group; KCPO, Kaiser Permanente Colorado; LDL, Low-density Lipoprotein; LMICs, Low- and Middle-Income Countries; mmHg, Millimeters of Mercury; MPR, Medication Possession Ratio; OR, Odds Ratio; PCP, Primary Care Professional; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT, Randomized Controlled Trial; RoB, Risk of Bias; SBP, Systolic Blood Pressure; sUA, Serum Uric Acid; UC, Usual Care; vCCC, Virtual Collaborative Care Clinic.
Disclosure
The authors report no conflicts of interest in this work.
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