The impact of non-pharmacological interventions on adherence to medication and persistence in dyslipidaemia and hypertension: a systematic review

ABSTRACT

Introduction

Suboptimal medication adherence is common among patients with cardiovascular diseases. We sought evidence on non-pharmacological interventions used to support adherence for patients with hypertension and/or dyslipidemia.

Methods

We searched MEDLINE, EMBASE, MEDLINE In-Process, ClinicalTrials.gov, EUCTR, and conference proceedings from July 2011 to July 2021 to identify trials evaluating effects of health education, phone reminders, or digital interventions on medication adherence or persistence of adult patients with hypertension and/or dyslipidemia. Risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool v2.

Results

Of 64 studies, 62 used health education approaches (e.g. educational interviews, motivational meetings, advice from physicians, and mobile health content), 16 phone reminders (e.g. text reminders, electronic pill-box linked reminders, bi-directional text messaging), and 10 digital applications as interventions (e.g., various self-management applications). All studies assessed medication adherence; only two persistence. Overall, 30 studies (83%) assessing health education approaches alone and 25 (78%) combined with other strategies, 12 (75%) phone reminders and eight studies (80%) digital applications combined with other strategies reported improved medication adherence. Two studies assessing health education approaches reported improved persistence.

Conclusions

Our findings indicate non-pharmacological interventions may positively impact adherence. Therefore, ‘beyond the pill’ approaches could play a role in preventing cardiovascular diseases.

KEYWORDS:

• Medication adherence
• medication persistence
• health education
• digital applications
• phone reminders

1. Introduction

Sustained adherence to prescribed therapy is the primary determinant of treatment success [1–3]. This medication-taking behavior encompasses both ‘medication adherence,’ defined by the World Health Organization (WHO) as ‘the extent to which patient behavior corresponds with agreed recommendations from their healthcare provider’ [4], and ‘medication persistence,’ the accumulated time from initiation to discontinuation of therapy [3]. Non-adherence may worsen therapeutic and economic outcomes due to an increased risk of cardiovascular events, increased frequency of healthcare provider consultations, higher rates of hospitalization, and increased healthcare costs [1,4–6]. Non-adherence among patients with chronic conditions, including cardiovascular conditions, is estimated at 40–50% [4,7].

The underlying determinants of adherence are complex [8]. Adherence to long-term treatment for chronic conditions may be influenced by socioeconomic and healthcare system-related factors (e.g. quality of provider-patient communication, healthcare access), factors related to the condition being treated (e.g., disease severity), therapy-related factors (e.g., cost, mode of administration, number of medications being taken, dosing frequency, side effects), and patient-related factors (e.g., patients’ own beliefs, constraints of everyday life, swallowing problems) [8–11]. Furthermore, adherence behavior can be affected by deliberate and non-deliberate actions, such as inability to take medications due to misunderstanding instructions [4,12].

Numerous interventions have been used to improve patient adherence to therapy in chronic disease [13,14]. A range of strategies have been investigated in cardiovascular disease prevention and control, including education brochures, reminder tools, telephone follow-up, individualized telephone feedback, patient web portals, and smartphone/wearable device applications, among others [15–20]. The approaches can be implemented as simple, ‘single-component’ interventions, or as a combination of strategies (i.e., ‘multi-component’ interventions). Strategies employed within a multi-component intervention may include components of the same broad type (e.g., different forms of health education) or components of more than one type (e.g., health education plus reminders to take medication).

There is a growing body of literature on non-pharmacological interventions to improve medication adherence [21]. However, to date, reviews on digital applications, phone reminders, and educational programs that included patients with hypertension/dyslipidemia have not specifically focused on these patient populations, were focused only on interventions delivered by pharmacists, or are outdated [5,6,22–25].

In this review, we therefore aimed to identify and synthesize evidence on health education, digital applications, and phone reminder interventions to improve medication adherence and/or persistence in adult patients with hypertension and/or dyslipidemia.

2. Methods

This systematic review was reported according to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 Statement [26]. This study was not registered in a public systematic review registry. The full review protocol is available upon request.

2.1. Search strategy and eligibility criteria

A systematic search was performed in MEDLINE, EMBASE (via Embase.com; Elsevier), MEDLINE, and MEDLINE In-Process (via PubMed; National Institute of Health National Library of Medicine) from database inception to 6 July 2021 (Table S1). In addition, we searched trial registries (ClinicalTrials.gov, EU Clinical Trials Register), and Google Scholar in September 2021 for relevant studies. We also searched conference proceedings for eligible studies published in the last three years (Table S2). Bibliographies of included publications were scrutinized for additional relevant studies.

We initially formulated broad eligibility criteria to identify evidence published in English on the impact of non-pharmacological interventions on adherence and persistence in adult patients with dyslipidemia, hypertension and/or angina. The review was then focused on studies with a randomized controlled trial (RCT) design because this represents the highest quality of study design. RCTs that enrolled < 50 patients, or published prior to July 2011 were excluded (Table S3).

2.2. Study selection

Selection of eligible publications was conducted in two stages: 1) screening of titles and abstracts, and 2) screening of full texts of publications deemed potentially relevant in the first step. Both stages were conducted by two reviewers in parallel. Discrepancies were resolved by discussion with a third reviewer.

2.3. Data extraction

A single reviewer extracted data from included publications into a standardized Excel® form. Data on study design, patient demographics, setting, interventions and comparators, medication adherence, and persistence were extracted. As interventions to increase medication adherence and persistence can be complex [27], we categorized the types of interventions and their components as shown in Table S4.

2.4. Risk of bias assessment

One reviewer assessed the risk of bias using version 2 of the Cochrane Risk of Bias Assessment Tool (RoB 2) [28]. Another reviewer independently validated the risk of bias assessments (low risk, some concern, high risk) for 25% of the studies.

2.5. Data synthesis

We summarized data in tabular form, and further synthesized the data according to intervention categories and outcomes to develop an effect direction plot [29]. Meta-analysis was not considered appropriate due to the variation in study designs and in the context, types, and delivery of interventions.

3. Results

The electronic database search yielded 4,654 records, from which 848 duplicates were removed. After screening titles and abstracts, 368 reports were retrieved for full-text review. According to the final selection criteria, 57 publications were included. An additional seven reports were included from the other searches. Therefore, 64 publications, each reporting on a different study, were included in this review (Figure 1).

Figure 1. PRISMA flow diagram showing the literature screening process. Abbreviation: RCT: randomized controlled trial. *Studies published before July 2011 (n = 50); studies with number of patients < 50 (n = 20) of which seven were published before 2011. †Sixteen registered studies had no results published.

The study characteristics are listed in Supplementary Table S5. Fifty-two studies were RCTs and 12 were cluster RCTs. Studies were conducted in the Asia-Pacific region (n = 24), North America (n = 21), Europe (n = 11), South America (n = 6), and Africa (n = 2). Most studies were conducted in the United States (n = 21), China (n = 8), or Spain (n = 6).

Sixty studies assessed patients with hypertension and four assessed patients with dyslipidemia. No relevant studies on patients with angina were identified. The study sample size ranged from 52 participants [30] to 25,388 participants [31]. The length of follow-up ranged from one month [32–34] to 60 months [35].

All 64 studies evaluated medication adherence. The most frequently used methods to assess medication adherence were: the Morisky Medication Adherence Scale (MMAS; n = 30 studies), followed by the High Blood Pressure Compliance Scale (HBCS)/Hill-Bone Medication Adherence Scale (HBMAS; n = 5), the proportion of days covered (PDC; n = 4), Medication Adherence Rating Scale (MARS; n = 3), Medication Adherence Self-Efficacy Scale (MASES; n = 2), Medication Possession Ratio (MPR; n = 2) and the Brief Medication Questionnaire (BMQ; n = 2) (Table S6). Sixteen studies did not specify the method used to assess adherence. Two studies assessed persistence [36,37], defined as mean treatment duration [37] or treatment discontinuation [36].

Health education was assessed either as a single intervention type, or with other types of intervention components, whereas phone reminders or digital applications were always included with other types of intervention approaches. Health education was the most frequently evaluated intervention (n = 62). The health education components included educational interviews, sessions with trained healthcare workers or researchers, group activities, educational mobile health content, phone-based sessions, online training, and printed materials. Twenty-nine studies assessed the impact of health education alone, in either single (n = 11) or multicomponent (n = 18) interventions. Thirty-three studies assessed interventions that included health education in addition to components from other category(ies), which included phone reminders, digital applications, blood pressure (BP) monitor, electronic medical record (EMR) platform, and other interventions.

Altogether, 16 studies assessed the impact of phone reminders as part of multiple intervention categories, which included health education, digital application, BP monitor, and electronic pill bottles. The components of phone reminders included text reminders for medication intake, prescription refills, and bi-directional text messaging requiring a confirmation from the patient.

Ten studies assessed the impact of digital applications as part of approaches that employed more than one of the intervention categories, which included health education, phone reminders, or BP monitor use. The components of digital applications included a range of self-management applications with varying functionalities.

Thirty studies [34,36,38–65] explicitly mentioned an underlying theory or model, most often motivational interviewing (n = 7) [36,38,47,49,52,57,61], the trans-theoretical model of behavioral change (n = 5) [36,44,51,52,61], social cognitive theory (n = 4) [41,51,52,63], health belief model (n = 3) [34,49,55], self-management theory (n = 3) [45,47,65], or the chronic care model (n = 3) [50,53,64].

Of the 64 included studies, 11 were classified as having a low risk of bias, 33 with some concerns of risk of bias, and 20 at high risk of bias (Figure 2).

Figure 2. Effect direction plot summarizing medication adherence and persistence in all studies. Abbreviations: BP: blood pressure; DAPP: digital application; EMR: electronic medical record; HE: health education; PR: phone reminders; RCT: randomized controlled trial; CRCT: cluster randomized controlled trial. Effect direction: upward arrow ▲= positive health impact, downward arrow ▼= negative health impact, sideways arrow ◄►= no change/mixed effects/conflicting findings. Sample size: final sample size (individuals) in intervention group; large arrow▲ >300; medium arrow▲100–300; small arrow ▲ <100. Study quality: denoted by row color:green = low risk of bias; amber = some concerns; red = high risk of bias.

Medication adherence and persistence reported by all included studies are summarized in the effect direction plot in Figure 2. Of the 62 studies assessing the impact of health education, 52 (84%) reported improved medication adherence. All 11 studies (100%) that assessed health education as a single-component interventions showed improved medication adherence. Of the 18 studies evaluating the impact of more than one health education component within an intervention, 13 (72%) reported improved medication adherence. Of the 32 studies assessing the impact of health education as part of an intervention with other intervention categories, 25 (78%) reported improved medication adherence. Among most of the studies measuring adherencing with self-reported questionnaires, the improvements were singificnat for studies assessing health education. Out of all studies four reported adherence after less than 3 months of follow-up. Across three studies health education was given as a single component intervention and results showed significantly improved adherence. One study investigating health education as a multiple component intervention non-significant improvement were reported in the study.

Of the 16 studies assessing phone reminders as part of the intervention, 12 (75%) reported improvement in medication adherence, regardless of the methods used to measure adherence. Among the 10 studies assessing digital applications as part of the intervention, eight (80%) reported improved medication adherence and the majority measured adherence using MMAS.

Only two studies [36,37] reported on medication persistence. One study [36] conducted among US patients enrolled in the Medicare Advantage Plan reported that patients who received a motivational interviewing-based telephone intervention were less likely to discontinue statin therapy in 6 months follow-up compared to controls who did not receive the interventions (adjusted odds ratio [OR]: 0.38; 95% CI: 0.19–0.76). Another study [37] was a phase IV trial that investigated a smartphone-based patient support tool to promote adherence in patients who were newly prescribed rosuvastatin. This tool allows patients to receive feedback on their treatment and disease support, as well as information on treatment and lifestyle choices. This study reported that patients using the support tool had a longer treatment duration than those using a smartphone-based control application that was limited to data collection only with no patient support (mean 157 days vs 146 days; p = 0.0019).

4. Discussion

This systematic review describes a decade of published research on non-pharmaceutical interventions and suggests that health education, digital applications, and phone reminders can have a positive impact on the medication adherence and persistence in patients with hypertension or dyslipidemia, regardless of the delivery approach (as a single intervention category or part of multiple intervention categories). Some authors have suggested that combining multiple types of approach may be more successful than a single intervention category (e.g., health education) or in some cases necessary because of the complexity of adherence behavior [66–68]. However, several studies identified in this review reported that interventions based on only one intervention category led to significant improvement in medication adherence compared to usual care [31,36,38,43,45,61,64,69–73].

Health education was the most widely used intervention and was frequently found to result in significant improvement compared to usual care. The identified evidence broadly supports the value of educational interventions on medication-taking behavior. Eighty-seven percent of all included studies showed improvement in adherence of the intervention groups over the comparator groups and more than 92% included health education as part of the intervention. Two previous reviews [74,75] focused broadly on healthcare professional-led or verbal education approaches reported similar findings in patients with hypertension, dyslipidemia, or diabetes mellitus. Authors of both reviews suggested that educational sessions improved health literacy, which is important for optimal disease management including adherence.

Among the 16 studies that assessed phone reminders, 80% (n = 12) reported improved medication adherence. Three studies [57,58,76] reported no significant improvement and one [77] reported a conflicting result. Similar findings were reported in the review by Vervloet et al. [24], that included 13 controlled clinical trials evaluating the effectiveness of interventions using electronic reminders in improving medication adherence in patients with chronic conditions. This review provided evidence for short-term effectiveness (follow-up period <6 months) of electronic reminders, especially Short Message Service (SMS) reminders, but concluded that long-term effects (follow-up period ≥6 months) of phone reminders were unclear. In a meta-analysis by Tao et al. [25] that included most of the studies from Vervloet et al. [24], electronic reminders were associated with a significant improvement in medication adherence. Another recent review [78] reported that SMS reminders were most effective for BP management when they involve two-way communication, content tailored to the individual patient, and a combination of other support practices.

All the studies that assessed digital application interventions reported a trend toward improved medication adherence. The extent of improvement in adherence may depend on numerous factors, including specific characteristics of the application and its delivery [79]. Other factors, such as the involvement of healthcare providers in the intervention administration and the characteristics of the target population, might also influence the success of any particular intervention. Findings of this review are in accordance with a previous review [80] that included studies with varying study designs and outcomes and provided evidence of the benefits of mobile health interventions (based on sending text messages or using a smartphone application). This review states that 65% of the studies reported improved adherence and concluded that sending medication reminders and health education messages together was more beneficial than sending only medication reminders. A recent systematic review and meta-analysis [81] published in 2021 reported similar findings that included 16 RCTs assessing patients with cardiovascular diseases. This review found that nine of the included RCTs reported statistically significant improvements in medication adherence.

Difficulty in reliably measuring adherence has been highlighted in previous publications [8,82]. There is currently no recognized ‘gold standard’ approach for assessing medication adherence. One suggested means to address this is using at least two different methods to evaluate adherence within the same study [83]. However, we found that 45 of 64 studies only used one method. Challenges in measuring adherence were also highlighted by studies in which several methods were used, as our review identified four studies [39,57,77,84] reporting conflicting findings when using more than one tool to measure adherence. Altogether, the identified evidence emphasizes the lack of consensus regarding the most appropriate means to measure adherence.

In this review, only one study [62] out of 34 studies published since 2018 reported medication adherence in accordance with the International Society for Medication Adherence (previously named ESPACOMP) Medication Adherence Reporting Guideline (EMERGE) checklist [85], which became available in 2018 [85]. We also noted other studies published since 2018 reported at least some details on the implementation process of the intervention under investigation [32,33,35,37,38,40–43,47,56,58,59,61,62,64,70–73,86–92] or the relevance to their application into general practice [32,38,43,62,64,69–73,88–91,93–96]. Nevertheless, insufficient details were reported by many studies on the technical implementation processes and message delivery, which are required to have a precise understanding of an intervention and its implementation [97].

Not all studies stated whether the interventions were based on a particular theory, model, or framework. When reported, most of the theories and models targeted individuals rather than systems, families, or communities. Most also focused on cognitive factors such as perceptions, beliefs, knowledge, attitudes, or expectations. Although many articles noted that interventions were developed based on theories, generally insufficient details were provided to ascertain their implementation of the theories. However, our findings are broadly aligned with Conn et al. [98] who found that theory- and model-linked interventions can have a modest but significant effect on medication adherence.

Despite the promising future of mobile Health (mHealth) in emerging economies, where smartphones are increasingly widely used [99], most of the evidence on the use of digital applications or phone text message reminders was from high-income countries. Five studies from developing countries [100] included a digital application as part of an intervention [37,40,59,86,101]. The limited evidence from these few studies supports the benefits of a range of mHealth approaches in improving medication adherence in developing countries.

This review has several strengths and limitations. We performed a structured, systematic search, focusing on randomized controlled trials, which are considered sources of high-quality evidence according to the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) guidelines [102]. Limitations include restricting to English-language publications and limiting to studies published from July 2011 onwards. Furthermore, heterogeneity in definitions of adherence and methods to measure medication adherence limited comparability across identified studies, as did the wide range of intervention types and contexts in which studies were conducted [103–108].

Our review provides a global picture of the evidence generated in the last 10 years on the use and impact of non-pharmacologic interventions on medication adherence and persistence. A ‘beyond the pill’ approach, employing non-pharmacological interventions to support medication adherence can play a crucial role along with pharmacological therapies to treat chronic diseases. Future research is warranted on non-pharmacological interventions, in particular in the form of studies with high-quality design, large sample sizes, and standardized conduct in terms of definitions and reporting.

Non-standard abbreviations and Acronyms

BMQ	=	Brief Medication Questionnaire
BP	=	Blood pressure
EMR	=	Electronic medical record
GRADE	=	Grading of Recommendations, Assessment, Development and Evaluations
HBCS	=	High Blood Pressure Compliance Scale
HBMAS	=	Hill-Bone Medication Adherence Scale
MARS	=	Medication Adherence Rating Scale
mHealth	=	Mobile health
MMAS	=	Morisky Medication Adherence Scale
MPR	=	Medication Possession Ratio
PDC	=	Proportion of days covered
PRISMA	=	Preferred Reporting Items for Systematic Review and Meta-Analysis
RCT	=	Randomized controlled trial
RoB2	=	Risk of Bias 2 tool
SMS	=	Short Message Service
WHO	=	World Health Organization

Declaration of interest

JB Brière is an employee of Servier. IA Gudiña, X. Jiang, P. Kodjamanova, and L. Bennetts are employees of Amaris Consulting, which received funding from Servier for the study. ZM Khan is a paid consultant for Servier. AP Kengne received an honorarium for support in contributing to the study protocol and interpretation of results. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Author contributions

Manuscript concept and design: JB Brière, A. Kengne, I. Asensio Gudiña, P. Kodjamanova, and L. Bennetts; Drafting of manuscript: S. Ridhurkar; Critical reviews: A. Kengne, JB Brière, ZM Khan, and L. Bennetts; all authors approved and agree for the final version of the manuscript to be published.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgments

The authors thank Sudipta Ridhurkar, PhD, from Amaris Consulting, Barcelona, Spain for providing medical writing assistance. This assistance was funded by Servier.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14737167.2024.2319598.

Additional information

Funding

This paper was funded by Servier.