Journal of Psychosocial Nursing and Mental Health Services

Original Article 

Prevalence, Barriers, and Interventions Related to Medication Adherence Among Patients With Major Depressive Disorder: A Scoping Review

Anas H. Khalifeh, RN, MSN, ANS-PMHN; Ayman M. Hamdan-Mansour, PhD, RN, MSN

Abstract

The current scoping review aimed to identify the prevalence, contributing factors, methods of measurement, and interventions related to medication adherence among patients with major depressive disorder (MDD). A total of 37 articles met inclusion criteria. The prevalence of medication adherence among patients with MDD ranged from 10.6% to 85.4%. Approximately 67% of studies used self-report data collection. Illness-related factors (e.g., onset of illness, duration of illness, symptoms, illness severity), medication-related factors (e.g., adverse reactions, duration of treatment, cost of treatment), and patient-related factors (e.g., beliefs, attitudes, knowledge, self-stigma) were the most reported factors associated with medication adherence. In addition, multi-faceted interventions were recommended over single-element interventions to enhance medication adherence. There is a need to integrate appropriate and effective assessment measures of medication adherence that lead to better health care outcomes, lower risk factors, and improved interventions related to medication adherence. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx–xx.]

Abstract

The current scoping review aimed to identify the prevalence, contributing factors, methods of measurement, and interventions related to medication adherence among patients with major depressive disorder (MDD). A total of 37 articles met inclusion criteria. The prevalence of medication adherence among patients with MDD ranged from 10.6% to 85.4%. Approximately 67% of studies used self-report data collection. Illness-related factors (e.g., onset of illness, duration of illness, symptoms, illness severity), medication-related factors (e.g., adverse reactions, duration of treatment, cost of treatment), and patient-related factors (e.g., beliefs, attitudes, knowledge, self-stigma) were the most reported factors associated with medication adherence. In addition, multi-faceted interventions were recommended over single-element interventions to enhance medication adherence. There is a need to integrate appropriate and effective assessment measures of medication adherence that lead to better health care outcomes, lower risk factors, and improved interventions related to medication adherence. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx–xx.]

Despite decades of research, medication adherence is a well-known and ongoing problem for health care systems, health professionals, researchers, and patients (Cheen et al., 2019). The literature on medication adherence has focused primarily on its pervasiveness and significant impact on health care outcomes (Vrijens et al., 2012). According to the World Health Organization (WHO), medication adherence is defined as “the extent to which a person's behaviour— taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider” (Sabaté, 2003, p. 3). Although the literature acknowledging the benefits and positive impacts of medication adherence on patients' health, nonadherence to medication has been reported to cause negative impacts (Brown et al., 2016). In general, most literature reports patients do not adhere strictly to drug regimens recommended by their health care providers (Jafari Oori et al., 2019).

Although an essential role of health care professionals while preparing discharge plans is to improve medication adherence, nonadherence remains widely reported, leading to negative health outcomes and poor prognosis (Cheen et al., 2019). According to Jafari Oori et al. (2019), 50% of patients with chronic illnesses have poor medication adherence. Recent studies across multiple chronic conditions reported that nonadherence to medication ranges from 7.5% to 96% (Cheen et al., 2019; Mongkhon & Kongkaew, 2017; Uchmanowicz et al., 2019). For patients experiencing psychiatric disorders, nonadherence has been reported with higher significance compared to other medical disorders. Gebeyehu et al. (2019) reported that 55% of patients with psychiatric disorders have a high level of medication nonadherence, inferring poor prognosis and early relapse.

Depressive disorders are the most common psychiatric conditions, affecting 300 million individuals worldwide (WHO, 2018). Major depressive disorder (MDD) is the most severe form of depressive disorder and is becoming a common chronic psychiatric disorder (Greenberg et al., 2015). MDD is characterized by impairments of social, occupational, educational, and cognitive functioning (American Psychiatric Association [APA], 2013). MDD is recognized as a growing global disease burden and one of the leading causes of morbidity, mortality, and economic burden (Greenberg et al., 2015). Annually, 6% of adults experience MDD worldwide (Bromet et al., 2011). By 2010, MDD was the second leading contributor to global disease disability (Ferrari et al., 2013). By the year 2030, MDD will affect at least 350 million people and is expected to be the leading cause of disability around the world (World Federation for Mental Health, 2012).

Despite effective treatments of MDD, responses remain sub-optimal (Ho et al., 2016). Adherence to medications in MDD has been an intractable clinical challenge over the past 20 years (Brijnath & Antoniades, 2016). Medication nonadherence among patients with MDD has been reported with high rates due to variations in measuring medication adherence. Various forms of measurement include self-report, pill counts, pharmaceutical claims, biomarkers, physiological measures, and electronic medication monitoring (Brown et al., 2016; Lam & Fresco, 2015). Moreover, adherence to medication has been influenced by complex and multidimensional factors that contribute negatively to successful treatment of MDD, such as patient-, therapy-, health care system–, condition-, and socioeconomic-related factors (Sabaté, 2003; Sirey et al., 2017). Several factors may cause high rates of medication nonadherence, such as low educational level, adverse side effects, uncertainty about treatment benefits, lack of education about medication, erroneous beliefs about illness or medication, costs of medications, stigma, forgetfulness, lack of social support, and a negative relationship with health care providers (Abegaz et al., 2017; Alekhya et al., 2015; Al-Jumah et al., 2014; Ho et al., 2017; Yau et al., 2014). The interference among medical, psychological, and environmental factors is a proposed underlying reason for medication adherence/nonadherence among patients with MDD.

Patients with MDD with medication nonadherence have a high risk of relapse, rehospitalization, increased severity of depression, and increased suicidal ideation (Ho et al., 2016; Yau et al., 2014). Nevertheless, research has shown that adherence to medication is improved by implementation of effective medication adherence–enhancing interventions (Abegaz et al., 2017; Michel, 2014; WHO, 2017). Supportive interventions have been used to enhance medication adherence (Chong et al., 2011; Torras & Pousa Tomàs, 2018), including psychoeducation; motivational interviewing; psychosocial, behavioral, and cognitive interventions; and cognitive-behavioral approaches (Chong et al., 2011; Ehret & Wang, 2013; Haddad et al., 2014; Nieuwlaat et al., 2014). Such interventions are provided by specialized mental health professionals, including psychiatric nurses, psychiatric nurse practitioners, psychiatrists, and clinical psychologists. However, these interventions are mainly directed toward patients with schizophrenia (Farooq & Naeem, 2014), which may explain the high rates of nonadherence among patients with MDD (Cameron et al., 2014; Mert et al., 2015). Although schizophrenia and depressive disorders are considered serious mental illnesses (Ghannam et al., 2017), the literature emphasized adherence among patients with schizophrenia assuming cognitive involvement (i.e., information processing related to communication, activity, or decisions) rather than other psychologically related factors.

Nonadherence contributes to unnecessary switching of medications, unneeded dosage adjustments, initiation of unjustified adjuvant medications, and misdiagnosis of treatment resistance (Olfson, 2015). Moreover, focus on prevalence of adherence, factors affecting adherence, and interventions that improve adherence will help health care organizations inform policymakers and researchers about the needed designs and implementation strategies that enhance medication adherence among patients with MDD (Kleppe, 2016). However, there is a need to expand the body of knowledge regarding medication adherence in MDD (Stein-Shvachman et al., 2013). Previous reports indicated that additional studies are needed to investigate the effect of interventions on medication adherence (Al-Jumah et al., 2014). There is rising demand for scoping reviews, systematic reviews, and meta-analyses related to medication adherence, factors affecting medication adherence, methods to measure medication adherence, and medication adherence interventions among patients with MDD.

MDD can often be treated using psychological and pharmacological approaches (Fiaturi & Greenblatt, 2018; Hamdan-Mansour et al., 2009; Hamdan-Mansour et al., 2020; Okasha et al., 2017). International and national clinical practice guidelines recommend antidepressant medications as first line treatment in MDD as well as continuing these medications from 6 months to 5 years after remission to prevent relapse and recurrence (Okasha et al., 2017; Párraga Martínez et al., 2014). Thus, the purpose of the current scoping review is to highlight the prevalence, factors, measurement methods, and interventions regarding medication adherence among patients with MDD to provide insight to stake-holders, patients, health care providers, and policymakers.

Research Questions

The questions that guided this review were:

  1. What is the prevalence of medication adherence among patients with MDD?

  2. What are the most common methods used to measure medication adherence among patients with MDD?

  3. What factors affect medication adherence among patients with MDD?

  4. What are the implemented interventions that are used to enhance medication adherence among patients with MDD?

Method

Protocol and Registration

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines (Tricco et al., 2018) were used for this review. The study protocol was registered prospectively with the Open Science Framework (access https://osf.io/a2jbr). This methodology is a new approach used to examine the extent of emerging evidence, determine the value of research, summarize and disseminate results, and identify gaps in knowledge (Munn et al., 2018).

Search Strategy

The search strategy was developed by the lead author (A.H.K.), in collaboration with the senior author (A.H.M.), to identify relevant literature. The initial search was performed on September 20, 2019, and the last search was performed on November 30, 2019. Databases searched included PubMed, CINAHL, Medline, and the Cochrane Library. These databases were selected to be comprehensive and cover a wide range of research articles. The search was conducted using the following keywords: medication adherence; compliance; non-compliance; patient compliance; adherence; non-adherence; anti-depressant agents; antipsychotic agents; psychotropic drugs; drug prescriptions; drug therapy; major depressive disorder; and depression. Keyword combinations using Boolean operators (AND & OR), truncation, medical subject headings terms, and text-words were included in the search related to the purpose and research questions. All databases were searched using the same keywords or search terms and subjected to standardized procedures.

Inclusion and Exclusion Criteria

Inclusion criteria were full-text articles published between 2014 and 2019 that were written in English, involved human participants, and included adult patients being treated for MDD as their primary diagnosis. The articles were relevant to MDD, medication adherence, and medication adherence–enhancing interventions. In addition, quantitative, qualitative, and mixed methods studies were included to enhance understanding of the problem from different research methodology perspectives. Articles were original research, peer-reviewed studies, or grey literature, and other relevant studies from the reference lists of the literature selected for review were included.

Exclusion criteria were inability to retrieve full text; articles not available in the English language; studies that did not contribute information on factors, prevalence, or interventions in medication adherence regarding MDD; and MDD secondary to traumatic brain injury, stroke, tumor, heart attack, or other nonpsychiatric illness. Letters, opinion statements, case reports, conference abstracts, editorials, commentaries, personal communications, and book chapters were also excluded.

Screening Process

Forms for the screening process were developed by the lead author and reviewed and validated by the senior author. The first level of screening included the article title and abstract, which were screened independently by both authors for eligibility after deleting duplicate articles. After screening the titles and abstracts, the authors independently screened and assessed full texts of articles for eligibility. Discrepancies regarding inclusion/exclusion of articles were resolved by discussion and consensus. Thirty articles were used to evaluate the screening form and agreement between authors.

Data Charting and Quality Assessment

Data were charted from each article independently by both authors using a data charting sheet developed (A.K.H.) and validated (A.H.M.) by the authors. Fifteen articles were used to evaluate agreement and ensure consistency with the research questions and purpose of the scoping review. Data charting was conducted using Microsoft® Excel and EndNote reference management software. Data gathered and charted included article characteristics (i.e., author[s], year of publication, and study location), study characteristics (i.e., purpose of the study, study setting, number of participants, type of study, prevalence, factors, the method used to measure medication adherence, and medication adherence–enhancing interventions), and study findings (i.e., outcome variables and key findings). Any conflicts about data extracted were resolved by consensus.

The authors used the Mixed Methods Appraisal Tool (MMAT) (Hong et al., 2018) to independently assess quantitative, qualitative, and mixed methods studies, and resolved disagreements by discussion and consensus. The MMAT checklist consists of two screening questions and five closed questions applied in all relevant studies. Twenty-five criteria are used to assess the quality of the five different types of studies (qualitative research, randomized controlled trials [RCTs], non-randomized studies, quantitative descriptive studies, and mixed methods studies); each type of study contains five criteria. The overall quality score can be checked with this tool for each study included. The tool results in a methodological rating of 0, 25, 50, 75, and 100 (with 100 being the highest quality) for each study based on the evaluation of study selection bias, study design, data collection methods, sample size, intervention integrity, and analysis (Scott et al., 2019).

Synthesis of Results

Data were collected and summarized in the form of text, tables, and figures. Initially, the data were summarized based on the general characteristics of the articles, such as the year of publication, study location, and study setting. The results were summarized and described based on the purpose and research questions. Finally, the clinical, policy, administration, and research implications were described.

Results

Selection of Studies

The initial search of the online electronic databases yielded 1,980 relevant articles. The selected articles were examined and revised to identify an additional 17 relevant articles. After the removal of 625 duplicate articles, 1,372 articles remained. Of these articles, an additional 1,243 were removed based on the screening form. Thus, 129 full-text articles were reviewed and assessed for eligibility. Ninety-three additional articles were excluded, with a total of 37 articles meeting inclusion criteria for this scoping review (Figure 1).

PRISMA flow diagram of search and selection process.

Figure 1.

PRISMA flow diagram of search and selection process.

Characteristics of Included Studies

The general characteristics of the included studies are presented in Table A (available in the online version of this article). Of the 37 included articles published between 2014 and 2019, eight studies were conducted in the United States, five in India, and the remaining conducted in Ethiopia, Saudi Arabia, France, Spain, Malaysia, Netherlands, Nigeria, Israel, China, Turkey, Nepal, South Africa, Australia, Iran, Thailand, Singapore, and Taiwan. Regarding study design, the majority of articles (n = 33) used quantitative study designs—24 articles were descriptive (e.g., prospective, non-experimental, observational, retrospective cohort, ex-post facto, cross-sectional) and nine studies were experimental. Furthermore, three studies used qualitative study designs, and one study used a mixed method design. The total sample of patients included in this scoping review was 42,795, with study populations ranging from 18 to 14,135. Age ranged between 18 and 88 years, and the majority of the sample was female. Thirty studies recruited from outpatient settings.

Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)Characteristics of Selected Studies (n = 37)

Table A:

Characteristics of Selected Studies (n = 37)

Methodological Quality of Included Studies

According to the MMAT (Hong et al., 2018), there was a meaningful variation in the methodological quality of the included articles, with scores ranging from 25% to 100%. The score of each type of study is judged within its methodological scope (Table B, available in the online version of this article), and quality of the study designs is presented in Table C (available in the online version of this article). The majority of studies (n = 19, 51.4%) were methodologically adequate (>75%), noting that design and research method were generally appropriate. Thirteen (35.1%) studies received a score of 75%, four studies (10.8%) a score of 50%, and one study (2.7%) a score of 25%.

Quality of Studies by Mixed Methods Appraisal Tool (MMAT) (n = 37)Quality of Studies by Mixed Methods Appraisal Tool (MMAT) (n = 37)Quality of Studies by Mixed Methods Appraisal Tool (MMAT) (n = 37)

Table B:

Quality of Studies by Mixed Methods Appraisal Tool (MMAT) (n = 37)

Study Design and Quality of Included Studies (n = 37)

Table C:

Study Design and Quality of Included Studies (n = 37)

Medication Adherence in Patients With MDD

Of the reviewed articles that investigated medication adherence to anti-depressant medications or concomitant with antipsychotics, anxiolytics, mood stabilizers, and psychostimulants, 34 studies used adherence measurement methods and 10 studies did not report prevalence of adherence.

Descriptions of data collection methods are presented in Table D (available in the online version of this article). Of 34 studies that measured medication adherence, 23 (67.6%) studies used self-report, nine (26.5%) used pharmacy refill and claims databases, and two (5.9%) used pill count. The rate of adherence as assessed using self-report, claims/refill databases, pill count, and health provider's report (self-report per patient files) ranged from 10.6% to 85.4% (Abegaz et al., 2017; Alekhya et al., 2015; Al-Jumah, Ahmad Hassali, & AlQhatani, 2014; Al-Jumah et al., 2014; Aljumah & Hassali, 2015; Baeza-Velasco et al., 2019; Bhat et al., 2018; Burnett-Zeigler et al., 2014; Chatterjee et al., 2017; De las Cuevas et al., 2014a,b; Endale Gurmu et al., 2014; Isa et al., 2018; Klein et al., 2017; Lu et al., 2016; Lucca et al., 2015; Mert et al., 2015; Novick et al., 2015; Serrano et al., 2014; Sirey et al., 2017; Taleban et al., 2016; Vannachavee et al., 2016), 10% to 62.9% (Bushnell et al., 2016; Green et al., 2017; Grover et al., 2018; Holvast et al., 2019; Klang et al., 2015; LeBlanc et al., 2015; Mert et al., 2015; Yau et al., 2014; Zhang et al., 2016), 45% (Hammonds et al., 2015; Pradeep et al., 2014), and 69.2% (Mert et al., 2015; Novick et al., 2015), respectively. This wide range of adherence rates could be due to variations among studies in terms of data collection methods, including measurement tools and period of data collection.

Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)

Table D:

Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)

Twenty-three studies used standardized and structured data collection tools and structured interviews to measure adherence. Five (21.7%) studies used the Morisky Medication Adherence Scale-8 items (MMAS) (Abegaz et al., 2017; Al-Jumah, Ahmad Hassali, & AlQhatani, 2014; Al-Jumah et al., 2014; Aljumah & Hassali, 2015; Chatterjee et al., 2017), four (17.4%) studies used the four-item Morisky Green Levine Medication Adherence Scale (MGLS) (De las Cuevas et al., 2014a,b; Klein et al., 2017; Lu et al., 2016), and four (17.4%) studies used the Medication Adherence Rating Scale (MARS) (Baeza-Velasco et al., 2019; Endale Gurmu et al., 2014; Lucca et al., 2015; Taleban et al., 2016).

Nine studies used electronic data records (pharmacy or medical claims databases) to measure medication adherence using different measures, including the percentage of days covered (PDC), medicine possession ratio (MPR), prescription fill, and missing days. The PDC is the ratio of the number of days the patient is covered by the medication in a period to the total number of days in the period. LeBlanc et al. (2015) considered 80% the cutoff between adherence and nonadherence. The MPR is the number of days in which doses are dispersed divided by the total number of days between the first and last doses. This ratio is dichotomized into >80%, which is considered adherent, and ≤80%, which is considered nonadherent as reported by Holvast et al. (2019), whereas Slabbert et al. (2015) reported that patients are considered adherent if MPR is between >80% and ≤110%.

Regarding prescription fill and missing days, nonadherence is considered discontinuing the medication before the 180 days after the index refill date (Bushnell et al., 2016). Other studies defined adherence as the proportion of compliance during the first 24 weeks from prescription fill (Klang et al., 2015). Yau et al. (2014) measured adherence as filled prescriptions for medications with no lapses of more than 15 days within 6 months after the start of treatment. On the other hand, two studies used pill count by calculating the number of pills taken divided by the expected number of pills taken during the study period and multiplying it by 100 (Hammonds et al., 2015). Another study calculated adherence as the total number of weeks participants took medication (Pradeep et al., 2014).

Factors Affecting Medication Adherence

Several factors contribute to patients' lack of adherence. Most literature reports the following five factors of adherence from the WHO: (a) socioeconomic, (b) health care provider/system, (c) illness, (d) medication, and (e) patient related (Gast & Mathes, 2019; Sabaté, 2003). Later studies added to the aforementioned list of factors to include family/caregiver and other resource factors (Kane et al., 2013). Hung (2014) identified the following factors to be associated with nonadherence to antidepressant treatment: sociodemographic, clinical features of depression, comorbidities, pharmacological, attitudes toward antidepressants, previous experiences of antidepressant treatment, patient– provider relationship, and genetics.

Although there is some overlap in factors affecting medication adherence, most studies relied on those of the WHO. Therefore, we used these five factors in the current scoping review to extract the factors from included studies. Factors that could contribute to medication adherence among patients diagnosed with MDD are summarized and presented in Table E (available in the online version of this article). Of 37 included studies, 27 studies investigated factors that influence medication adherence. The following section will explore commonly reported factors associated with medication adherence.

Factors affecting toward medication adherence (n=27)Factors affecting toward medication adherence (n=27)Factors affecting toward medication adherence (n=27)

Table E:

Factors affecting toward medication adherence (n=27)

Socioeconomic Factors. Language and literacy have been associated with medication adherence level (Green et al., 2017). Using different self-report assessment methods, medication adherence was related significantly to income (Lu et al., 2016; Lucca et al., 2015) and level of education (De las Cuevas et al., 2014a; Lucca et al., 2015). Regarding living conditions, Yau et al. (2014) noted that type of accommodation was associated with noncontinuous use of antidepressant medications, particularly in patients with low socioeconomic status who live in public housing. Endale Gurmu et al. (2014) reported that patients residing in rural areas had a higher risk of nonadherence. Patients' occupation is also associated with medication adherence. Patients working in service and farming industries reported significantly higher adherence rates compared to patients working in business industries and those of housewives and students (Shrestha Manandhar et al., 2017).

Lack of support is also a factor for nonadherence (Grover et al., 2018; Lucca et al., 2015). Studies conducted in different contexts reported that lack of support from family members, spouses, and friends was found to be a barrier, ranging from delaying initiation of medication to continuation of medication (Ho et al., 2017; Srimongkon et al., 2018). In addition, Ho et al. (2017) and Vargas et al. (2015) noted that sociocultural stigma influences adherence. In instances where depression is considered a negative personal characteristic and sign of madness, this view has contributed to the ostracization and alienation of patients with MDD. Moreover, religion and cultural beliefs have been reported as barriers to medication adherence (Ho et al., 2017).

Health Care Provider/System–Related Factors. Accessibility to health care services, poor access to health care locations, long clinic wait times, limited time with a psychiatric provider, and lack of accessible appropriate material have been found to influence medication adherence (Abegaz et al., 2017; Green et al., 2017; Grover et al., 2018; Ho et al., 2017). Patients who travel long distances to a psychiatric clinic are five times more nonadherent to medication compared to patients who live a short distance from the clinic (Abegaz et al., 2017). Ho et al. (2017) reported that patients who have experienced long clinic wait times and lack transportation to health care settings had lower rates of medication adherence. Limited access to different formats of informational materials in psychiatric clinics also influences medication adherence due to lack of verbal and written instructions and information, use of different languages in instructions and information, and inappropriate formats for patients with physical disabilities (e.g., hearing and visual impairment) (Green et al., 2017). Furthermore, the limited time of psychiatric follow-up visits affects adherence to medication (Green et al., 2017).

Seeing different psychiatrists and multiple prescribers at every clinic visit affects patients' confidence and trust toward providers, which later affected their medication-taking behavior (Ho et al., 2017). Nonavailability of psychiatrists during follow up was another reason for nonadherence (Lucca et al., 2015). In addition, patients' satisfaction level with psychiatrists is also related to medication adherence (Alekhya et al., 2015). The frequency of follow-up visits to psychiatric clinics was also found to affect medication adherence (Alekhya et al., 2015; Ho et al., 2017; Mert et al., 2015). Less clinic visits related significantly to a low level of medication adherence (Al-Jumah et al., 2014; Yau et al., 2014).

Ho et al. (2017) reported that poor communication between patients and providers could impede medication adherence. In addition, inadequate information on medications and disorders and health care provider guidance were related to low adherence levels (Green et al., 2017; Lucca et al., 2015; Srimongkon et al., 2018). Despite appropriate information and guidance, access to medication in facilities is more related to availability, which increases adherence rate (Endale Gurmu et al., 2014; Ho et al., 2017; Lucca et al., 2015; Srimongkon et al., 2018).

Illness-Related Factors. Acute onset of illness associated with early withdrawal was experienced in patients with MDD (Grover et al., 2018). Earlier diagnosis of MDD minimized medication's noncontinuous behaviors (Yau et al., 2014). A short duration of illness signified high rates of adherence in one study (Al-Jumah et al., 2014). Alekhya et al. (2015) noted a correlation between patients who experienced illness for >1 year and low adherence. Similarly, risk of medication nonadherence in patients who have been diagnosed with MDD for more than 2 years was found to be high (Abegaz et al., 2017). Furthermore, adherence to medication is influenced by symptoms and severity of the disorder. Lucca et al. (2015) demonstrated that patients reported forgetfulness, no improvement, and deterioration of conditions as factors influencing medication adherence. Similarly, other studies showed that somatic symptoms of MDD are associated with early treatment dropout (Grover et al., 2018). Furthermore, nonadherence is associated with high physical pain (Baeza-Velasco et al., 2019). In addition, in patients with a history of childhood maltreatment, adherence levels were lower (Baeza-Velasco et al., 2019). De las Cuevas et al. (2014a) found that patients with severe depression demonstrated medication nonadherence. However, the opposite association between adherence and depression severity demonstrated that less severe depression was significantly correlated with higher medication adherence rates (Al-Jumah et al., 2014; Baeza-Velasco et al., 2019). In another study, recurring episodes of depression had a significant impact on medication adherence, indicating a positive relationship to increased medication adherence (Lu et al., 2016).

Number of psychiatric hospitalizations also influences medication adherence, with Baeza-Velasco et al. (2019) concluding that more psychiatric hospitalizations are associated with high levels of nonadherence. In addition, comorbid physical illnesses, alcohol dependence, illicit drug use, and concomitant psychiatric illness negatively interfered with medication adherence in patients with MDD (Abegaz et al., 2017; Alekhya et al., 2015; Grover et al., 2018; Ho et al., 2017; Srimongkon et al., 2018). On the contrary, patients with comorbid anxiety exhibited higher medication adherence (Lu et al., 2016). Patients with a family history of depression were also found to be nonadherent (Alekhya et al., 2015). In patients with suicidal ideation and attempts, poor adherence was substantially increased (Alekhya et al., 2015; Baeza-Velasco et al., 2019). Cross-sectional studies of patients with bipolar disorder, schizophrenia, schizoaffective disorder, depression, and other psychiatric disorders showed that the diagnosis had a significant negative effect on medication adherence (Endale Gurmu et al., 2014; Mert et al., 2015).

Medication-Related Factors. According to recent studies, complex treatment regimens impair adherence significantly. Alekhya et al. (2015) found that polypharmacy has a significant impact on adherence; most patients do not adhere to a multiple drug regimen. In addition, patients with comorbidities discontinue medications due to pill burden (Ho et al., 2017). Moreover, previous studies stated that adverse reactions are associated with level of medication adherence (Alekhya et al., 2015; Bhat et al., 2018; Endale Gurmu et al., 2014; Grover et al., 2018; Green et al., 2017; Srimongkon et al., 2018; Vannachavee et al., 2016; Yau et al., 2014). In addition, nonadherence is related to the severity and amount of adverse effects (De las Cuevas et al., 2014a). Ho et al. (2017) reported that patients stop taking medications due to side effects. In other studies, patients with MDD stated that the most common reason for nonadherence was adverse reactions to medications (Lucca et al., 2015; Mert et al., 2015; Shrestha Manandhar et al., 2017).

The duration of treatment can also affect medication adherence. Studies using MPR reported that duration of treatment is statistically and clinically correlated with adherence (Slabbert et al., 2015). However, Lucca et al. (2015) noted that although patients continued to receive treatment during the first 3 months, they also reported a significant rate of medication nonadherence. A qualitative study concluded concerns about the long-term effects of antidepressant medications have a negative effect on implementation of therapy and medication adherence (Srimongkon et al., 2018). Endale Gurmu et al. (2014) reported increases in treatment duration resulted in increased nonadherence. Inconvenient dose regimen may also affect medication adherence (Srimongkon et al., 2018). In other studies, the cost of medication was a factor for nonadherence (Lucca et al., 2015) and correlated substantially with nonadherence (Shrestha Manandhar et al., 2017). Studies that assessed adherence to a prescribed class of medication reported that the type of active ingredients consumed, or formulations, significantly related to level of adherence (Lucca et al., 2015; Slabbert et al., 2015). Discontinuing medication was found to be associated with perception of ineffective response (Novick et al., 2015).

Patient-Related Factors. Patient factors associated with medication adherence represent sociodemographic (e.g., age, gender, race, marital status), psychological (e.g., beliefs, attitudes, satisfaction, knowledge, psychological reactance, locus of control, self-stigma, self-motivation, insight, self-management), and physical (cognitive and behavioral; e.g., forgetfulness, personal obligation, carelessness, confusion) factors.

Age is considered a predictor of adherence to prescribed medications. Adherence to antidepressant medication was assessed in two community mental health centers located on Tenerife Island, Spain, indicating the likelihood of medication adherence for older patients was low (De las Cuevas et al., 2014b). A study comparing level of adherence reported lower adherence levels for patients age >60 years than patients age 18 to 40 years (Slabbert et al., 2015). In contrast, other studies reported older patients had higher adherence rates than younger patients (Al-Jumah et al., 2014; Endale Gurmu et al., 2014). In addition, young age is significantly associated with medication discontinuation (Yau et al., 2014).

Gender is a factor in the discontinuation of medications among patients with MDD, with female patients showing higher levels than male patients (Yau et al., 2014). In addition, male gender was also found to be associated with lack of adherence (Al-Jumah et al., 2014). Race/gender is also associated with adherence. Across four race/gender subgroups (African American women, African American men, White women, and White men), White women had higher rates of adherence (3.1 times) than African American women (Burnett-Zeigler et al., 2014). Marital status also exhibited significant differences in level of medication adherence. According to Baeza-Velasco et al. (2019), high rates of adherence have been found among those who have partners.

Patients' beliefs also influence medication-taking behavior. Believing in the need for medication is positively associated with medication adherence (Green et al., 2017), whereas concerns regarding harmfulness and overuse are related to medication nonadherence (Al-Jumah et al., 2014; Bhat et al., 2018; Chatterjee et al., 2017; Green et al., 2017; Vannachavee et al., 2016). The belief that psychiatric medications are considered harmful has been associated with higher rates of nonadherence (De las Cuevas et al., 2014a). Non-adherent patients displayed a higher degree of concern regarding potential adverse effects of medications, such as dependence, side effects, and accumulation effects (De las Cuevas et al., 2014a). In another study, beliefs that medication does not relieve symptoms and increases the severity of the disease, in addition to denial of one's psychiatric illness, have been reported as reasons for interrupting treatment plans (Endale Gurmu et al., 2014; Grover et al., 2018).

Necessity beliefs were significant predictors of high antidepressant medication adherence. Patients believe that antidepressant medication may protect against exacerbation of depression, thus their mental health status is dependent on adherence (Lu et al., 2016). However, concerns of long-term consequences, being heavily dependent on medication, patients' perception of mastery on medication (i.e., perception of being controlled by the medication), and their perception of life stability may negatively impact their beliefs regarding antidepressant medications (Lu et al., 2016). Srimongkon et al. (2018) reported that patients' beliefs in medication efficacy had a positive influence on adherence, whereas therapeutic side effects had a negative influence on adherence. Qualitative interviews of the experiences of MDD in Latino outpatients concluded that patients had concerns regarding medications and depression that may have been perceived as obstacles to treatment and medication adherence (Vargas et al., 2015).

Concerns regarding fear of dependence on antidepressant medications, physical ramifications of taking antidepressant medications, risk of deteriorating mental health, potential hazardous dosages, irrepressible adverse effects (Vargas et al., 2015), and alternative therapies also impact antidepressant medication adherence (Endale Gurmu et al., 2014; Vargas et al., 2015).

Regarding attitude toward medication, positive attitude is related to adherence (Baeza-Velasco et al., 2019; De las Cuevas et al., 2014a; Green et al., 2017; Serrano et al., 2014), and negative attitude is related to nonadherence (Ho et al., 2017). In addition, low level of adherence to antidepressant medication is associated with low treatment satisfaction (Al-Jumah et al., 2014). Medication adherence can also be influenced by a negative emotional reaction to regulations or recommendations of medication use that affect freedom and autonomy, and beliefs regarding the control over one's health. Psychological reactance and chance external locus of control are positively associated with medication adherence. More reactant patients are less adherent and more externalized, whereas patients who depend on their providers have higher levels of adherence (De las Cuevas et al., 2014b).

Self-stigma also affects medication adherence (Vargas et al., 2015). Self-stigma regarding depression, denial of one's disorder, and nonacceptance of the disorder are major reasons for discontinuing medication (Mert et al., 2015; Srimongkon et al., 2018; Yau et al., 2014). Patients who experience severe depressive symptoms but want to feel better are motivated to adhere to medications. According to Srimongkon et al. (2018), self-motivation and self-management contribute to an increase in medication adherence; however, lack of insight may influence adherence negatively (Ho et al., 2017; Lucca et al., 2015). Moreover, patients revealed that forgetfulness negatively affects medication adherence (Endale Gurmu et al., 2014; Ho et al., 2017; Shrestha Manandhar et al., 2017). Patients' personal characteristics, such as carelessness, confusion (Lucca et al., 2015; Shrestha Manandhar et al., 2017), and being busy (Endale Gurmu et al., 2014), as well as obligations, such as traveling to work and/or health care agencies, negatively influence medication adherence.

Intervention Strategies for Improving Medication Adherence

Ten of the 37 included studies evaluated the effectiveness of various interventions to improve medication adherence among patients with MDD (Table F, available in the online version of this article), ranging from single element interventions to multi-element interventions. These interventions have been categorized into monitored feedback (adherence and disease), reminders, education and information, counseling (cognitive, behavioral interventions), and multifaceted interventions. Multifaceted interventions were defined as interventions including two or more components, such as education with monitored feedback and cognitive education with counseling (Cochrane Effective Practice and Organization of Care Review Group [EPOC], 2002).

Studies with interventions to medication adherence (n=10)Studies with interventions to medication adherence (n=10)Studies with interventions to medication adherence (n=10)Studies with interventions to medication adherence (n=10)Studies with interventions to medication adherence (n=10)

Table F:

Studies with interventions to medication adherence (n=10)

Interventions Based on Monitored Feedback. The use of pharmacy management and community health care services has been shown to positively affect medication adherence (Bhat et al., 2018; Klang et al., 2015; Pradeep et al., 2014). Pharmacist-led, multidisciplinary telemonitoring provided early intervention for patients following antidepressant medication initiation or up-titration to enhance adherence, relieve adverse effects, and minimize suicide risks through education and information and monitored feedback (adherence and disease) (Bhat et al., 2018). Klang et al. (2015) compared community pharmacist management, which used education and information, reminders, and monitored feedback approaches, with treatment as usual to measure treatment adherence after 1 and 6 months. Adherence rate was higher among patients who received community pharmacist management (Klang et al., 2015). In a study that investigated enhanced care home visits using education and information and monitored feedback compared to treatment as usual, higher rates of treatment completion and medication adherence were noted for the intervention group (Pradeep et al., 2014). However, there was no significant difference in outcomes of depression severity at 6-month follow up (Pradeep et al., 2014).

Interventions Based on Reminder Systems. Use of an electronic medication reminder application through a smart-phone increased adherence to antidepressant medications in a sample of college students (Hammonds et al., 2015).

Education and Information Interventions. Information is conveyed in a variety of formats, including verbal, written, or audiovisual. These interventions are designed to educate patients to promote medication adherence by sufficiently describing the way to take medication, discussing any reluctance to take medication, and discussing patients' beliefs and knowledge about their condition and treatment (Aljumah & Hassali, 2015; LeBlanc et al., 2015). These interventions focus on patients, context, and the health care system, adopting patient-centered care and shared decision-making principles (Aljumah & Hassali, 2015; LeBlanc et al., 2015). Providing information about antidepressant medications through pharmacist-led interventions significantly improves medication adherence, treatment satisfaction, general overuse beliefs, and specific beliefs. However, in one study, the severity of depression and health-related quality of life did not differ between intervention and control groups at 6 months (Aljumah & Hassali, 2015). In addition, other studies used depression medication choice as a novel shared decision-making approach compared to usual care and found no differences in medication adherence, depression control, and encounter duration (LeBlanc et al., 2015).

Cognitive-Behavioral Counseling Interventions. Cognitive-behavioral therapy (CBT) has been proven effective in improving self-concept, pessimistic worldview, negative thoughts, and medication adherence (Nieuwlaat et al., 2014). Taleban et al. (2016) used a novel approach to cognitive-behavioral interventions through bibliotherapy (booklet) and booklet associated with text messaging. The study sample was allocated to three groups: control, booklet, and text messaging. Data were collected three times: before the intervention, immediately after the intervention, and 3 months after the intervention. Medication adherence was insignificant within each group at different times; however, the interactive effect of groups was significant (Taleban et al., 2016).

Multifaceted Intervention. A single element approach has limited effectiveness on medication adherence because the factors determining adherence interact and potentiate each other's influence (Nieuwlaat et al., 2014; Sabaté, 2003). Evidence supports a multifaceted approach is the most effective, as it uses more than one factor and more than one strategy (Isa et al., 2018; Sirey et al., 2017; Vannachavee et al., 2016). Examples include the treatment initiation and participation (TIP) program, drug adherence enhancement program, and psychoeducation with basic CBT strategies (Isa et al., 2018; Sirey et al., 2017; Vannachavee et al., 2016). Multifaceted approaches significantly improved level of knowledge of depression and attitudes toward medication adherence, as well as decreased depressive symptoms compared with treatment as usual (Isa et al., 2018; Sirey et al., 2017; Vannachavee et al., 2016).

Discussion

Medication nonadherence is an issue that concerns mental health care professionals, decision makers in health care systems, and patients. The current scoping review summarizes and identifies medication adherence rates, types of measurement methods, factors of medication nonadherence, and types of medication adherence–enhancing interventions among patients with MDD. Findings provide insights into the importance of focusing on the prevalence of adherence, factors affecting adherence, and interventions to improve adherence in patients with MDD. In addition, the variations found in the methods of measurement and rates of adherence evoke attention toward developing a valid and reliable measurement approach that can be used across different populations and settings.

The quality of most included studies was good, with only one study receiving a low score (25%) according to the MMAT. The prevalence of medication adherence ranged from 10.6% to 85.4%, with most studies reporting a rate <60%. The wide range in adherence rate results from the various study designs, adherence measures, and inconsistent definitions of medication adherence. Regarding adherence measures, various methods were used to assess patient adherence to medication; however, none of the measures are universally accepted as the gold standard. Most studies in this scoping review used indirect measures, such as self-report questionnaires and pharmacy refill data, because direct methods, such as biological measures, are not always acceptable, appropriate, feasible, or cost-effective compared to self-report data collection methods (Lam & Fresco, 2015). Self-report was used in 23 studies using 12 different measures. Objective measures, such as electronic data records, were used in nine studies and two studies used pill count.

Although multiple measurement methods were used, each is acknowledged to have its own advantages and limitations. The advantages of indirect measures include flexibility, ease of administration, relative unobtrusiveness, cost-effectiveness, and time-saving. However, limitations include potential bias (e.g., refilling a prescription is not an indication of ingestion of medication), social desirability, and overestimates of adherence (Aljumah & Hassali 2015; De Las Cuevas et al., 2014a; Isa et al., 2018; Klein et al., 2017; Lucca et al., 2015; Novick et al., 2015; Pradeep et al., 2014; Sirey et al., 2017; Slabbert et al., 2015; Yau et al., 2014; Zhang et al., 2016).

Twenty-seven studies investigated factors that influence medication adherence among patients with MDD using the WHO classification (Sabaté, 2003). Illness, medication, and patient-related factors were the factors most associated with medication adherence. These factors explain how patients make decisions and the role of their beliefs regarding the effects, consequences, medication options, and medication benefits, which determine adherence to medication and treatment plans. These findings contribute to the existing body of knowledge in which additional factors have been revealed that influence medication adherence among patients with MDD. Additional factors that need to be addressed include type of prescription, type of health insurance, and treatment protocol. For instance, the use of trade names rather than generic formulations by health professionals may confuse patients if they are not well informed of the different trade names of the same generic medication (Greenberg et al., 2015; Ho et al., 2017; Zhang et al., 2016). In addition, the cost of treatment and variation in health insurance and treatment protocols vary around the world, causing and contributing to variations in medication adherence. Moreover, the current review found that psychological status and patients' cognitive abilities played a significant role in medication adherence. Patients seek to observe the effects of medication not only on signs and symptoms as emphasized by their providers, but also to satisfy their intrinsic needs, such as identity, motivation, relationships, and social skills, regardless of the time needed for the medication to take effect (Ho et al., 2017; Lucca et al., 2015). With an understanding of the factors that influence medication adherence among patients with MDD, interventions can be tailored individually to improve each patient's medication-taking behavior. Nieuwlaat et al. (2014) noted that effective intervention is complex and involves several components, inferring that a multifactorial approach is recommended to positively improve medication adherence. Moreover, European organizations endorse adherence therapy, which is multi-faceted, involving practical and pragmatic approaches based on CBT, motivational interviewing, compliance therapy, and psycho-education (Gray, 2006).

Health care professionals play an essential role in assessing patients with nonadherence and delivering appropriate interventions to support and improve adherence, persistence, and retention in care. Nurses play a key role in screening, assessing, and promoting medication adherence.

Limitations

The following limitations should be mentioned. First, the search was limited to the past 5 years and English language publications. Second, this is not a comprehensive review of all existing medication adherence measures and interventions. However, the review focused on the different types of interventions and approaches available and those most commonly used among patients with MDD.

Implications

The current scoping review has revealed important implications for nurses and nursing practice, administrators, policymakers, education, and future research. Mental health nurses and other mental health care professionals need to assess for factors contributing to medication adherence among patients with MDD. There is a need to identify the most reliable methods of measurement, whether objective or subjective, that best allow for improving adherence rates. Varying rates of nonadherence are an indication to mental health professionals that systematic follow up and adherence interventions need to be created and integrated into discharge planning and follow-up care. Moreover, mental health nurses and mental health professionals are recommended to use medication adherence– enhancing multifaceted interventions to improve patients' beliefs and behaviors to achieve better medication adherence rates among patients with MDD. There is also a need to provide in-service training programs at mental health institutions that target innovative and advanced technological devices that can help improve medication adherence and follow-up care among patients with MDD. To successfully assess medication-taking behavior, routine assessments and interdisciplinary support are required.

Health care managers and administrators need to be aware that medication nonadherence may negatively impact quality of care provided and health care outcomes for patients with MDD as well as patients with other mental illnesses. Developing guidelines for medication adherence measures, medication adherence–enhancing interventions, and follow-up strategies are recommended, as well as collaborative efforts among mental health care professionals, clinical researchers, and community-based stakeholders.

Regarding education, nursing curricula, as well as curricula of other mental health care specialties, need to integrate concepts of adherence and related interventions. Nursing students should be aware of the concept of adherence, the outcomes of nonadherence, and interventions to enhance adherence. In addition, nursing care plans need to include assessment of medication adherence and self-monitoring as a standard part of clinical mental health care practices. Therefore, nursing programs should focus on teaching students how to assess for medication adherence. Future research needs to emphasize medication nonadherence among patients with MDD and interventions to promote patients' adherence across settings, diseases, and treatment approaches. Longitudinal studies using structured reliable measures may allow better understanding of factors that contribute to nonadherence to medication and treatment among patients with MDD.

Conclusion

The current scoping review highlights that medication nonadherence among patients with MDD is a major obstacle in reducing public health challenges in developed and developing countries. Evidence shows that patients with MDD have a high rate of medication nonadherence. However, accurate assessments of medication adherence will provide better evidence on the outcomes, risk factors, and interventions to improve medication adherence. The numerous measurements and assessments of medication adherence also need to be tested for validity, reliability, and sensitivity. The selection of measurements should also be based on researchers' or health care professionals' expertise, their treatment and research goals, and available resources in clinical settings. Although none of the measurements can be considered a gold standard, the triangulation of measures is highly recommended. Medication-taking behavior is influenced by several factors, which are important to identify to implement appropriate medication adherence–enhancing interventions. Thus, mental health nurses and mental health professionals are required to work collaboratively with care managers to ensure cost-effective treatment protocols considering type of health insurance, economic status, availability of medications, cost of medication, and patients' psychological willingness. Interventions require several components, and health care professionals must follow a systematic process to assess possible factors of medication adherence.

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  • LeBlanc, A., Herrin, J., Williams, M. D., Inselman, J. W., Branda, M. E., Shah, N. D., Heim, E. M., Dick, S. R., Linzer, M., Boehm, D. H., Dall-Winther, K. M., Matthews, M. R., Yost, K. J., Shepel, K. K. & Montori, V. M. (2015). Shared decision making for antidepressants in primary care: A cluster randomized trial. JAMA Internal Medicine, 175(11), 1761–1770 doi:10.1001/jamainternmed.2015.5214 [CrossRef] PMID:26414670
  • Lu, Y., Arthur, D., Hu, L., Cheng, G., An, F. & Li, Z. (2016). Beliefs about antidepressant medication and associated adherence among older Chinese patients with major depression: A cross-sectional survey. International Journal of Mental Health Nursing, 25(1), 71–79 doi:10.1111/inm.12181 [CrossRef] PMID:
  • Lucca, J. M., Ramesh, M., Parthasarathi, G. & Ram, D. (2015). Incidence and factors associated with medication nonadherence in patients with mental illness: A cross-sectional study. Journal of Postgraduate Medicine, 61(4), 251–256 doi:10.4103/0022-3859.166514 [CrossRef] PMID:26440396
  • Mert, D. G., Turgut, N. H., Kelleci, M. & Semiz, M. (2015). Perspectives on reasons of medication nonadherence in psychiatric patients. Patient Preference and Adherence, 9, 87–93 Advance online publication. doi:10.2147/PPA.S75013 [CrossRef] PMID:25609930
  • Michel, M. (2014). Adherence and treatment outcomes among patients with comorbidity of depression and other mental disorders attending psychiatric hospitals in Rwanda [doctoral dissertation]. Kenyatta University.
  • Mongkhon, P. & Kongkaew, C. (2017). Medication non-adherence identified at home: A systematic review and meta-analysis. Quality in Primary Care, 25(2), 73–80.
  • Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A. & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143 doi:10.1186/s12874-018-0611-x [CrossRef] PMID:30453902
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  • Novick, D., Montgomery, W., Moneta, V., Peng, X., Brugnoli, R. & Haro, J. M. (2015). Anti-depressant medication treatment patterns in Asian patients with major depressive disorder. Patient Preference and Adherence, 9, 421–428 doi:10.2147/PPA.S68432 [CrossRef] PMID:25792815
  • Okasha, A., Alkhadhari, S. & Al Sharqi, A. (2017). Arab treatment guidelines for the management of major depressive disorder. Arab Journal of Psychiatry, 28(2), 97–117 doi:10.12816/0041709 [CrossRef]
  • Olfson, M. (2015). Bringing antidepressant self-discontinuation into view. Psychiatric Services (Washington, D.C.), 66(5), 449–449. doi:10.1176/appi.ps.660501 [CrossRef] PMID:
  • Párraga Martínez, I., López-Torres Hidalgo, J., del Campo del Campo, J. M., Villena Ferrer, A., Morena Rayo, S. & Escobar Rabadán, F.the en representación del Grupo ADSCAMFYC. (2014). Seguimiento de la adherencia al tratamiento antidepresivo en pacientes que inician su consumo. Atencion Primaria, 46(7), 357–366 doi:10.1016/j.aprim.2013.11.003 [CrossRef] PMID:
  • Pradeep, J., Isaacs, A., Shanbag, D., Selvan, S. & Srinivasan, K. (2014). Enhanced care by community health workers in improving treatment adherence to antidepressant medication in rural women with major depression. The Indian Journal of Medical Research, 139(2), 236–245 PMID:24718398
  • Sabaté, E. (2003). Adherence to long-term therapies: Evidence for action. World Health Organization.
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  • Serrano, M. J., Vives, M., Mateu, C., Vicens, C., Molina, R., Puebla-Guedea, M. & Gili, M. (2014). Therapeutic adherence in primary care depressed patients: A longitudinal study. Actas Españolas de Psiquiatría, 42(3), 91–98 PMID:24844808
  • Shrestha Manandhar, J., Shrestha, R., Basnet, N., Silwal, P., Shrestha, H., Risal, A. & Kunwar, D. (2017). Study of adherence pattern of antidepressants in patients with depression. Kathmandu University Medical Journal, 15(57), 3–9 PMID:29446354
  • Sirey, J. A., Banerjee, S., Marino, P., Bruce, M. L., Halkett, A., Turnwald, M., Chiang, C., Liles, B., Artis, A., Blow, F. & Kales, H. C. (2017). Adherence to depression treatment in primary care: A randomized clinical trial. JAMA Psychiatry, 74(11), 1129–1135 doi:10.1001/jamapsychiatry.2017.3047 [CrossRef] PMID:28973066
  • Slabbert, F. N., Harvey, B. H., Brink, C. B. & Lubbe, M. S. (2015). Prospective analysis of the medicine possession ratio of antidepressants in the private health sector of South Africa, 2006–2011. South African Medical Journal, 105(2), 139–144 doi:10.7196/SAMJ.8394 [CrossRef] PMID:26242534
  • Srimongkon, P., Aslani, P. & Chen, T. F. (2018). Consumer-related factors influencing antidepressant adherence in unipolar depression: A qualitative study. Patient Preference and Adherence, 12, 1863–1873 doi:10.2147/PPA.S160728 [CrossRef] PMID:30288027
  • Stein-Shvachman, I., Karpas, D. S. & Werner, P. (2013). Depression treatment non-adherence and its psychosocial predictors: Differences between young and older adults?Aging and Disease, 4(6), 329–336 doi:10.14336/AD.2013.0400329 [CrossRef] PMID:24307966
  • Taleban, R., Zamani, A., Moafi, M., Jiryaee, N. & Khadivi, R. (2016). Applications of text messaging, and bibliotherapy for treatment of patients affected by depressive symptoms. International Journal of Preventive Medicine, 7(1), 46 doi:10.4103/2008-7802.177889 [CrossRef] PMID:27076884
  • Torras, M. & Pousa Tomàs, E. (2018). Interventions to improve therapeutic adherence in subjects with schizophrenia. Papeles Del Psicólogo, 39(1). doi:10.23923/pap.psicol2018.2850 [CrossRef]
  • Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C. & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMAScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473 doi:10.7326/M18-0850 [CrossRef] PMID:30178033
  • Uchmanowicz, B., Jankowska, E. A., Uchmanowicz, I. & Morisky, D. E. (2019). Self-reported medication adherence measured with Morisky medication adherence scales and its determinants in hypertensive patients aged ≥60 years: A systematic review and meta-analysis. Frontiers in Pharmacology, 10, 168 doi:10.3389/fphar.2019.00168 [CrossRef] PMID:
  • Vannachavee, U., Seeherunwong, A., Yuttatri, P. & Chulakadabba, S. (2016). The effect of a drug adherence enhancement program on the drug adherence behaviors of patients with major depressive disorder in Thailand: A randomized clinical trial. Archives of Psychiatric Nursing, 30(3), 322–328 doi:10.1016/j.apnu.2015.12.001 [CrossRef] PMID:
  • Vargas, S. M., Cabassa, L. J., Nicasio, A., De La Cruz, A. A., Jackson, E., Rosario, M., Guarnaccia, P. J. & Lewis-Fernández, R. (2015). Toward a cultural adaptation of pharmacotherapy: Latino views of depression and antidepressant therapy. Transcultural Psychiatry, 52(2), 244–273 doi:10.1177/1363461515574159 [CrossRef] PMID:25736422
  • Vrijens, B., De Geest, S., Hughes, D. A., Przemyslaw, K., Demonceau, J., Ruppar, T., Dobbels, F., Fargher, E., Morrison, V., Lewek, P., Matyjaszczyk, M., Mshelia, C., Clyne, W., Aronson, J. K. & Urquhart, J.the ABC Project Team. (2012). A new taxonomy for describing and defining adherence to medications. British Journal of Clinical Pharmacology, 73(5), 691–705 doi:10.1111/j.1365-2125.2012.04167.x [CrossRef] PMID:22486599
  • World Federation for Mental Health. (2012). Depression: A global crisis. https://www.who.int/mental_health/management/depression/wfmh_paper_depression_wmhd_2012.pdf
  • World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. https://www.who.int/mental_health/management/depression/prevalence_global_health_estimates/en/
  • World Health Organization. (2018). Mental disorders. https://www.who.int/news-room/fact-sheets/detail/mental-disorders
  • Yau, W. Y., Chan, M. C., Wing, Y. K., Lam, H. B., Lin, W., Lam, S. P. & Lee, C. P. (2014). Non-continuous use of antidepressant in adults with major depressive disorders: A retrospective cohort study. Brain and Behavior, 4(3), 390–397 doi:10.1002/brb3.224 [CrossRef] PMID:24944868
  • Zhang, L., Chen, Y., Yue, L., Liu, Q., Montgomery, W., Zhi, L. & Wang, W. (2016). Medication use patterns, health care resource utilization, and economic burden for patients with major depressive disorder in Beijing, People's Republic of China. Neuropsychiatric Disease and Treatment, 12, 941–949 doi:10.2147/NDT.S97407 [CrossRef] PMID:27143895

Characteristics of Selected Studies (n = 37)

Author (year)CountryStudy designStudy settingSample(Participant no. (Interventional, Control) Patient characteristics (age, gender))Purpose of studyOutcome variables
n=sample sizeI:C
Abegaz et al. (2017)EthiopiaQuantitative (Prospective cross-sectional study)Inpatient and outpatient270The purpose to determine the degree of adverse drug reactions of antidepressants and their impact on the level of adherence and clinical outcome.Adverse drug reactions, medication adherence, and clinical outcomes (depression)
Alekhya et al. (2015)IndiaQuantitative (Cross sectional study)Outpatient103Study the treatment and disease factors that influence compliance to the treatment of depression.Medication adherence; and disease and treatment factors
Al-Jumah et al. (2014a)Saudi ArabiaQuantitative (Non-experimental, observational design)Outpatient403The purpose to investigate the relationship between patient treatment satisfaction and adherence to antidepressants, and the role of patient beliefs toward medication in patient treatment satisfaction.Medication adherence, treatment satisfaction, and beliefs about medication
Al-Jumah et al. (2014b)Saudi ArabiaQuantitative (Non-experimental cross-sectional design)Outpatient403The purpose to explore patients' adherence to antidepressant medication, and the factors associated with adherence among patients with depression.Medication adherence and beliefs about medication
Aljumah and Hassali (2015)Saudi ArabiaQuantitative (Prospective randomized controlled study)Outpatient239(119, 120)The purpose to assess whether pharmacist interventions based on SDM improved adherence and patient-related outcomes.Medication adherence, beliefs about medication, clinical outcomes (depression symptoms), patient involvement in decision-making, quality of life, and treatment satisfaction
Baeza-Velasco et al. (2019)FranceQuantitative (Cross-sectional study)Inpatient and outpatient360The purpose to explore medication adherence in patients with a major depression episode, and to identify sociodemographic, clinical, and psychosocial factors related to adherence status.Medication adherence, clinical and psychosocial factors (depressive symptoms, psychiatric antecedents, comorbidities, medication, pain, medication side effects, negative life events, childhood trauma, and attitudes to medication)
Bhat et al. (2018)USAQuantitative (Observational retrospective cohort study)Outpatient258The purpose to evaluate the feasibility of implementing a clinical pharmacist led multidisciplinary antidepressant telemonitoring service, evaluate potential opportunities for clinical pharmacy intervention, and identify which patients with major depressive disorder would be most likely to benefit from this service in primarycare.Medication adherence, adverse effects, suicidal ideations, depressive symptoms, and pharmacist interventions.
Burnett-Zeigler et al. (2014)USAQuantitative (Prospective, observational study)Outpatient186The purpose to examine the associations between treatment attitudes and beliefs with race–gender differences in antidepressant adherence.Medication adherence, demographic variables, illness variables (past antidepressant use, number of prescribed medications, physical health status, mental health status, comorbid anxiety, somatic anxiety, and depression), activities of daily living and executive function, attitudes and beliefs toward depression treatment, and stigma
Bushnell et al. (2016)USAQuantitative (Retrospective cohort study)Inpatient and outpatient8,837The purpose to identify predictors of six-month antidepressant persistence.Antidepressant persistence, demographic, clinical, and psychosocial factors (age, sex, psychiatric and non-psychiatric co-morbidities, healthcare utilization, antidepressant class, prior suicide attempt, high and mid-potency prescription opiate usage, and recurrent MDD diagnosis)
Chatterjee et al. (2017)IndiaQuantitative (Ex-post facto design (criterion-group design))Outpatient60The purpose to explore belief about the medication influences adherence to medication, and influence severity of depression and quality of life of patients with MDD residing at urban and rural areas.Medication adherence, beliefs about medication, depressive symptoms, and quality of life
De las Cuevas et al. (2014a)SpainQuantitative (Cross-sectional study)Outpatient145The purpose to identify potential factors influencing adherence to antidepressant treatment by patients with mood disorders in the community mental health care setting.Socio-demographic characteristics and clinical variables, medication adherence, attitudes toward treatment, beliefs about medication, attitude toward concordance, depressive symptoms, and side effect
De Las Cuevas et al. (2014b)SpainQuantitative (Cross-sectional study)Outpatient119The purpose to examine the relationship of psychological reactance, health locus of control and the sense of self-efficacy on adherence to treatment regimen among psychiatric outpatients with depression.Socio-demographic characteristics and clinical variables, medication adherence, psychological features (psychological reactance, health locus of control, and self-efficacy)
Green et al. (2017)USAMixed-methodOutpatient28The purpose to investigate knowledge and attitudes about antidepressant medication, including risks and benefits, how patients received this information, and how they would prefer to learn about antidepressants.Medication persistence, knowledge and attitudes about antidepressant medication, Depression, Trauma exposure, post-traumatic stress disorder, and side effects
Grover et al. (2018)IndiaQuantitative (Naturalistic, longitudinal, follow-up study)Outpatient140The purpose to evaluate the medication adherence, treatment adherence, and outcome of depression.Medication adherence, treatment adherence, and outcome of depression
Gurmu et al. (2014)EthiopiaQuantitative (Cross-sectional study)Outpatient209The main aim to assess the rate of medication nonadherence among psychiatry patients at University of Gondar Hospital.Medication adherence, clinical characteristics, and factors of nonadherence
Hammonds et al. (2015)USAQuantitative (Randomized, parallel-group clinical trial)Outpatient57(30, 27)The purpose to determine the effect of medication reminding via smartphone app on adherence to antidepressant medications in college students.Medication adherence, depression, social support, stress, and health beliefs
Ho et al. (2017)MalaysiaQualitative (Grounded theory methodology)Outpatient30The purpose to explore the barriers and facilitators of patients' adherence to antidepressants among outpatients with MDD.Barriers and facilitators ofmedication adherence
Holvast et al. (2019)NetherlandsQuantitative (Longitudinal study)Outpatient1,512The purpose to determine the non-adherence rates to antidepressants among older adults in primary care, based on non-initiation, suboptimal implementation or non-persistence.Non-initiation, suboptimal implementation, non-persistence, associated with non-adherence
Isa et al. (2018)NigeriaQuantitative (Pre-post one-group intervention study)Outpatient18The purpose to investigate the effects of psycho-education and basic CBT intervention on depressed medication-treated adolescents.Depressive symptoms, knowledge of depression, hope, attitudes towards treatment adherence, and satisfaction
Klang et al. (2015)IsraelQuantitative (Prospective, nonrandomized, open-label, naturalistic observational study)Outpatient4246(173, 4079)The purpose to effectiveness of CP intervention for patients with MDD.Medication adherence and depressive symptoms
Klein et al. (2017)NetherlandsQuantitative (Descriptive longitudinal study)Outpatient289The purpose to explore beliefs about the causes of depression and recovery and to examine whether they predict antidepressant medication use.Medication adherence, beliefs regarding depression, and antidepressant medication dosage
LeBlanc et al. (2015)USAQuantitative (Cluster randomized trial)OutpatientClinicians (117); patients (297)Clinicians (66, 51); patients (158, 139)The purpose to estimate the effect of DMC on quality of the decision-making process and depression outcomes.Patient knowledge and involvement in decision making, patient and clinician decisional comfort and satisfaction, encounter duration, medication adherence, and depression symptoms
Lu et al. (2016)ChinaQuantitative (Cross-sectional study)Outpatient135The purpose to investigate the variables associated with adherence with antidepressants in elderly Chinese patients, focusing on attitudes and beliefs as potential predictors, as well as sociodemographic characteristics and illness-related variables.Medication adherence and beliefs about medication
Lucca et al. (2015)IndiaQuantitative (Cross-sectional study)Outpatient400 (n=170 patients with depression)The purpose to determine the incidence and factors associated with medication nonadherence among psychiatric outpatients.Medication adherence and reasons for medication nonadherence
Mert et al. (2015)TurkeyQuantitative (Cross-sectional study)Inpatient203 (n=39 patients with depression)The purpose to evaluate factors resulting in medication nonadherence before admission to the psychiatric service for patients with psychiatric disorder.Socio-demographic and clinical variables, medication adherence, and reasons of medication nonadherence
Novick et al. (2015)Six East Asian countries and regions (China, Hong Kong, Malaysia, Singapore, South Korea, and Taiwan)Quantitative (Cross-sectional study, prospective, observational study)Inpatient430The purpose to describe pharmacological treatment patterns in patients with MDD.Medication adherence, reasons for medication nonadherence, depressive symptoms, somatic symptom, and quality of life
Pradeep et al. (2014)IndiaQuantitative (Randomized trial)Outpatient260(122, 138)The purpose to investigate effectiveness of enhanced care in improving treatment seeking and adherence to antidepressant medication in women with depression living in rural India.Medication adherence, number of clinic visits, depressive symptoms, and quality of life
Serrano et al. (2014)SpainQuantitative (Observational and longitudinal study)Outpatient29The purpose to determine the degree of therapeutic adherence in patients with depression, examine factors involved in the adherence process, and observe the clinical outcome.Medication adherence, depressive symptoms, drug attitude, beliefs about medication, and personality
Shrestha Manandhar et al. (2017)NepalQuantitative (Prospective study)Inpatient and outpatient60The purpose to determine the medication adherence pattern in patients with depression and assess the factors associated with non-adherence to the prescribed antidepressant therapy.Medication adherence and medication adherence pattern
Sirey et al. (2017)USAQuantitative (Randomized clinical effectiveness trial)Outpatient231(115, 116)The purpose to test the effectiveness of TIP to improve early adherence among older patients whose primary care physician newly initiated an antidepressant for depression.Medication adherence and depression severity
Slabbert et al. (2015)South AfricaQuantitative (Prospective, descriptive cohort study)Outpatient14,135The purpose to investigate the prevalence of antidepressant non-compliance in the private healthcare sector of South Africa.Medication adherence
Srimongkon et al. (2018)AustraliaQualitative (Phenomenological approach)Outpatient23The purpose to explore factors which facilitate and negatively impact adherence, at initiation, implementation and discontinuation phases of adherence to antidepressant medication.Facilitate and negatively impact adherence
Taleban et al. (2016)IranQuantitative (Randomized clinical trial)Outpatient198Booklet and text messaging group (67), booklet (66), and control (65)The purpose to evaluate the impacts of text messaging interventions, which aimed to inspire the affected patients to peruse bibliotherapy.Medication adherence and depression severity
Vannachavee et al. (2016)ThailandQuantitative (Randomized controlled trial with two parallel-group posttest-only designs)Outpatient56(30,26)The purpose to examine the effect of DAEP on adherence behaviors in patients with first diagnosed major depressive disorder.Medication adherence and depression severity
Vargas et al. (2015)USAQualitative (Ethnography)Outpatient30The purpose to examines salient views of depression and pharmacotherapy among Latinos seeking outpatient antidepressant therapy and suggests possible strategies for engaging patients on these views prior to the onset of treatment.Views of depression and antidepressant medication
Yau et al. (2014)ChinaQuantitative (Retrospective cohort study)Outpatient189The purpose to investigate the rate of noncontinuous antidepressant use, subsequent rate of relapse and recurrence in psychiatric Chinese outpatients, and factors associated with noncontinuous antidepressant use.noncontinuous antidepressant use, factors associated with noncontinuous antidepressant, and subsequent depression relapse and recurrence
Zhang et al. (2016)ChinaQuantitative (Retrospective cohort study)Inpatient and outpatient8,484The purpose to investigate medication usage patterns, health care resource utilization, and direct medical costs of patients with MDD in Beijing, People's Republic of China.Medication usage patterns, health care resource utilization, and economic burden

Quality of Studies by Mixed Methods Appraisal Tool (MMAT) (n = 37)

Qualitative Studies
CriteriaIs the qualitative approach appropriate to answer the research question?Are the qualitative data collection methods adequate to address the research question?Are the findings adequately derived from the data?Is the interpretation of results sufficiently substantiated by data?Is there coherence between qualitative data sources, collection, analysis and interpretation?MMAT score
Author (year)
Ho et al. (2017)100
Srimongkon et al. (2018)100
Vargas et al. (2015)100
Quantitative Randomized Controlled Trials
CriteriaIs randomization appropriately performed?Are the groups comparable at baseline?Are there complete outcome data (80% or above)?Are outcome assessors blinded to the intervention provided?Did the participants adhere to the assigned intervention (80% or above)?MMAT score
Author (year)
Aljumah & Hassali (2015)100
Hammonds et al. (2015)x75
LeBlanc et al. (2015)x75
Pradeep et al. (2014)100
Sirey et al. (2017)100
Taleban et al. (2016)x75
Vannachavee et al. (2016)x75
Quantitative Non-Randomized Studies
CriteriaAre the participants representative of the target population?Are measurements appropriate regarding both the outcome and intervention (or exposure)?Are there complete outcome data?Are the confounders accounted for in the design and analysis?During the study period, is the intervention administered (or exposure occurred) as intended?MMAT score
Author (year)
Bhat et al. (2018)100
Isa et al. (2018)x75
Klang et al. (2015)xx50
Quantitative Descriptive Studies
CriteriaIs the sampling strategy relevant to address the research question?Is the sample representative of the target population?Are the measurements appropriate?Is the risk of nonresponse bias low (60% or above)?Is the statistical analysis appropriate to answer the research question?MMAT score
Author (year)
Abegaz et al. (2017)100
Alekhya et al. (2015)xx50
Al-Jumah et al. (2014a)100
Al-Jumah et al. (2014b)100
Baeza-Velasco et al. (2019)100
Burnett-Zeigler et al. (2013)100
Bushnell et al. (2016)x75
Chatterjee et al. (2017)x75
De las Cuevas et al. (2014a)100
De Las Cuevas et al. (2014b)100
Grover et al. (2018)xx50
Gurmu et al. (2014)100
Holvast et al. (2018)x75
Klein et al. (2017)x75
Lu et al. (2016)100
Lucca et al. (2015)100
Mert et al. (2015)x75
Novick et al. (2015)x75
Serrano et al. (2014)xx50
Shrestha Manandhar et al. (2017)xxx25
Slabbert et al. (2015)x75
Yau et al. (2014)100
Zhang et al. (2016)100
Mixed Methods Studies
CriteriaIs there an adequate rationale for using a mixed methods design to address the research question?Are the different components of the study effectively integrated to answer the research question?Are the outputs of the integration of qualitative and quantitative components adequately interpreted?Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?MMAT score
Author (year)
Green et al. (2017)x75

Study Design and Quality of Included Studies (n = 37)

Study designStudy no. (%)MMAT score
0, 125, 250, 375, 4100, 5
Qualitative Studies3 (8.1)----3
Quantitative Randomized Controlled Trials7 (18.9)---43
Quantitative Non-Randomized Studies3 (8.1)--111
Quantitative Descriptive Studies23 (62.2)-13712
Mixed Methods Studies1 (2.7)---1-
Study no. (%)-1 (2.7)4 (10.8)13 (35.1)19 (51.4)

Medication Adherence Rate and Adherence Measurement Methods of Included Studies (n= 34)

Author (year)Period of data collectionMedication adherence rate (I:C)Adherence measureData collection methods
Abegaz et al. (2017)4 months10.6%MMAS (8-items)Self-Report
Alekhya et al. (2015)6 months30.1%DAISelf-Report
Al-Jumah et al. (2014a)5 months47.1%MMAS (8-items)Self-Report
Al-Jumah et al. (2014b)5 months47.1%MMAS (8-items)Self-Report
Aljumah & Hassali (2015)6 monthsNMMMAS (8-items)Self-Report
Baeza-Velasco et al. (2019)NM29.7%MARSSelf-Report
Bhat et al. (2018)5 months81%Unstructured scale contains one questionSelf-Report
Burnett-Zeigler et al. (2014)4 monthsNMBMQSelf-Report
Bushnell et al. (2016)6 months45%Non-persistent if treatment had been stopped before 180 days after the index antidepressant's dispensing dateMedical and pharmaceutical Claims Database
Chatterjee et al. (2017)NMNMMMAS (8-items)Self-Report
De las Cuevas et al. (2014a)8 months53.8%MGLS (4-items)Self-Report
De Las Cuevas et al. (2014b)4 months49.6%MGLS (4-items)Self-Report
Green et al. (2017)NM54%Medical RecordClaims Database
Grover et al. (2018)1 year3 months (34.3%) 3‑6 months (25%) 6‑12 months (10%)Medical recordClaims Database
Gurmu et al. (2014)One month50.2%MARSSelf-Report
Hammonds et al. (2015)2 years and 7 months45%Percent adherence was calculated by dividing the actual number of pills taken by the expected number of pills taken during the study period and multiplying the total by100Pill Count
Holvast et al. (2019)2 yearsInitiation: 86.5% Optimal implementation: 84.8% Persistence: 62.9%Three measures of non-adherence (Non-initiation (not dispensed by the SFK database within 14 days), suboptimal implementation (MPR), Non-persistence (discontinuation within 294 days after first dispense))Claims Database electronic medical records
Isa et al. (2018)2 monthsNMAttitude to Medication Adherence QuestionnaireSelf-Report
Klang et al. (2015)24 weeksI:55%; C:15.2%The prescription fill method to assess adherence (the proportion of compliance during the first 24 weeks)Claims Database
Klein et al. (2017)NM51.9%MGLS(4-items)Self-Report
LeBlanc et al. (2015)2 yearsI:67.7%; C:65.5%PDCClaims Database
Lu et al. (2016)9 months37.8%MGLS (4-items)Self-Report
Lucca et al. (2015)1 year58.2%MARSSelf-Report
Mert et al. (2015)1 year69.2%Not taking any medicine for at least 1 week during the 6-month term before the study was regarded as medication nonadherence; from first-degree relatives and patient files.Self-Report and patient record
Novick et al. (2015)7 monthsPatient-reported: 42.5%; clinician-reported: 85.4%

Clinicians were asked to provide their opinion on whether the patient had been adherent with the prescribed medication(s) for MDD since the baseline visit.

Patients were asked how regularly they took the medications prescribed for MDD since the baseline visit.

Self-Report
Pradeep et al. (2014)3 yearsI:11.1 ± 10.4; C:3.33 ± 3.79The total number of weeks the subjects took antidepressant medication, pill counts to ensure that patients took medication as prescribed by doctor.Pill count
Serrano et al. (2014)6 months72.4%SMAQSelf-Report
Shrestha Manandhar et al. (2017)4 months37%Structured questionnaireSelf-Report
Sirey et al. (2017)3 yearsThe 5-fold increase in adherence during the first 6 weeks of care.BMQSelf-Report
Slabbert et al. (2015)6 years34%MPRClaims Database
Taleban et al. (2016)NMNMMARSSelf-Report
Vannachavee et al. (2016)3 monthsI: 41.17±2.87; C: 22.58±17.07SMIRSelf-Report
Yau et al. (2014)1 year54%The prescription record and also the electronic and written medical records (filled prescriptions for any antidepressants with no gaps of >15 days within 6 months after initiation of treatment)Claims Database
Zhang et al. (2016)1 year17.8%The duration of time from initiation to discontinuation of therapy.Claims Database

Factors affecting toward medication adherence (n=27)

DimensionFactors in each dimensionAuthor (year) (Studies)
Socioeconomic factors

Cultural beliefs and attitude of social and family towered mental disorders and medications

Stigma of social and family towered mental disorders and medications

Level of education

Literacy

Language

Social support networks system (family, friend, spouse, co-worker)

Religion beliefs

Income

Employment status (Nature of the job, occupation of patients)

Living conditions

Alekhya et al. (2015); De las Cuevas et al. (2014a); Green et al. (2017); Grover et al. (2018); Ho et al. (2017); Lu et al. (2016); Lucca et al. (2015); Shrestha Manandhar et al. (2017); Srimongkon et al. (2018); Vargas et al. (2015); Yau et al. (2014)
Healthcare provider/System-related factors

Accessibility to healthcare services (long distance, lack of accessible appropriate material, limited time and long waiting time at the clinic, and poor access to healthcare locations)

Follow-up visits system (Frequentor infrequent follow-ups clinic visits)

Instruction and information about medications and disorders

Change and multiple prescribers

Supply of medications (availability of medication, frequent medication refill)

Patient-provider relationship

Availability of providers

Abegaz et al. (2017); Alekhya et al. (2015); Al-Jumah et al. (2014b); Bhat et al. (2018); Green et al. (2017); Grover et al. (2018); Gurmu et al. (2014); Ho et al. (2017); Lucca et al. (2015); Yau et al. (2014)
Illness-related factors

Onset of the illness

Duration of illness

Symptoms and illness severity

Number of psychiatric hospitalizations

Comorbid illness

Suicide ideation and attempts

Family and past history

Abegaz et al. (2017); Alekhya et al. (2015); Al-Jumah et al. (2014b); Baeza- Velasco et al. (2019); Grover et al. (2018); Ho et al. (2017); Holvast et al. (2019); Lu et al. (2016); Lucca et al. (2015); Mer et al. (2015); Srimongkon et al. (2018); Yau et al. (2014)
Medication-related factors

Complex treatment regimens

Adverse reactions

Duration of treatment

Cost of treatment

Classes of antidepressant

Alekhya et al. (2015); Baeza-Velasco et al. (2019); Bhat et al. (2018); De las Cuevas et al. (2014a); Green et al. (2017); Grover et al. (2018); Gurmu et al. (2014); Ho et al. (2017); Holvast et al. (2019); Lucca et al. (2015); Mert et al. (2015); Novick et al. (2015); Shrestha Manandhar et al. (2017); Slabbert et al. (2015); Srimongkon et al. (2018); Vannachavee et al. (2016); Vargas et al. (2015); Yau et al. (2014)
Patient-related factorsSociodemographic factors

Age

Gender

Race

Marital status

Psychological factors

Beliefs

Attitudes

Satisfaction

Knowledge

Self-management

Psychological reactance

Locus of control

Insight

Self-stigma

Self-motivation

Physical (cognitive and behavioral) factors

Forgetfulness

The patient's personal obligations

Carelessness

Confusion

Being busy

Abegaz et al. (2017); Alekhya et al. (2015); Al-Jumah et al. (2014a); Al-Jumah et al. (2014b), Baeza-Velasco et al. (2019); Bhat et al. (2018); Chatterjee et al. (2017); De las Cuevas et al. (2014a); De las Cuevas et al. (2014b); Green et al. (2017); Grover et al. (2018); Gurmu et al. (2014); Ho et al. (2017); Holvast et al. (2019); Lu et al. (2016); Lucca et al. (2015); Mert et al. (2015); Serrano et a. (2014); Shrestha Manandhar et al. (2017); Slabbert et al. (2015); Srimongkon et al. (2018); Vannachavee et al. (2016); Vargas et al. (2015); Yau et al. (2014)

Studies with interventions to medication adherence (n=10)

Author (year)CountryInterventionCategoriesOutcomes
Aljumah and Hassali (2015)Saudi ArabiaPharmacist interventions SDMEducation and informationPharmacist interventions based on SDM, intervention group showed significant differences in adherence to medication, treatment satisfaction, general overuse beliefs, specific concern beliefs, and total general beliefs about medicines. However, severity of depression and quality of live were not significantly change between intervention and control group at the end of six months. After 6 months, intervention group patients showed statistically significant in adherence to antidepressants and treatment satisfaction, and a decrease in concern beliefs and general beliefs about medicines.
Bhat et al. (2018).USAA pharmacist-led multidisciplinary telemonitoring serviceMulti-faceted (Education and information; and monitor feedback (adherence and disease))The clinical pharmacist-led multidisciplinary antidepressant telemonitoring service is a resource to monitor patients after antidepressant initiation or uptitration in primary care settings and provided interventions for patients. However, unable to strongly assess the impact of clinical pharmacists' interventions.
Hammonds et al. (2015)USAElectronic medication reminder applicationRemindersParticipants use of a medication reminder app were 3.5 times more likely to adhere to their medication regimen than those in the control group. However, depression symptoms were reduced from baseline, but the magnitude of change was not greater in participants using the medication reminder app.
Isa et al. (2018)NigeriaPsycho-education and basic CBT strategiesMulti-faceted (Cognitive education and information; and behavioral counselling)The effect of a psycho-educational and basic CBT intervention resulted in reduction in depressive symptoms, improvements in knowledge of depression, hope, and attitude towards treatment adherence one week and four weeks after the intervention.
Klang et al. (2015)IsraelCPs managementMulti-faceted (Education and information, reminders, and monitor feedback (adherence and disease))Measure adherence to antidepressant treatment at 6 months. At 1 month, the adherence rate was 71% in the CP group and at 6 months, the rates were high (55%) than control group of 42%. At 1 month, the adherence rate was 57% in the control group and at 6 months, the rate was 15.2%.
LeBlanc et al. (2015)USADMCEducation and informationThe use of DMC by primary care clinicians and patients with moderate to severe depression during clinical encounters was feasible and effectively improved patient knowledge and engagement in the decision-making process, as well as patient and clinician satisfaction with that process. However, use of the decision aid, had no discernible effect on encounter duration, depression control, and medication use and adherence.
Pradeep et al. (2014)IndiaCommunity care supportMulti-faceted (Education and information; and monitor feedback (adherence and disease))The number of clinic related visits and the duration of treatment (as measured by the number of weeks that subjects took antidepressant medication) was significantly greater in the interventional group compared to control group. While there was a significant difference in the treatment adherence pattern between the two groups, there was no significant difference in the outcomes of depression and quality of life at six months follow up.
Sirey et al. (2017)USATIP ProgramMulti-faceted (Cognitive education and information; and behavioral counselling)TIP Program is an effective intervention to improve early adherence to pharmacotherapy. Also, interventional group were 5 times more likely to be adherent at 6 weeks and were 3 times more likely to be adherent to their antidepressant pharmacotherapy at both 6 and 12 weeks. Furthermore, interventional group showed a significant reduction in depressive symptoms.
Taleban et al. (2016)IranBibliotherapy and text messagingCounselling (Cognitive behavioral interventions)Based on treatment compliance not significantly affected through group, but factors interactive effect of group factor and the time factor was statistically significant. Neither of the groups showed significant variations of the treatment compliance in different times comprising before the treatment, after the treatment or during the following up procedure. However, intensity of depressive symptoms was significantly affected through time and group factors as well as time‑group interaction. Neither of the interventional groups differed in intensity of depressive symptoms.
Vannachavee et al. (2016)ThailandDAEPMulti-faceted (Motivational interviewing; and cognitive and behavioral counselling)The participants in the experimental group had more correct drug adherence behaviors in terms of the dosage and timing during the sixth week than that of the participants in the control group. Also, the patients who received the DAEP had better depression scores after the intervention than those who received only the usual care.
Authors

Mr. Khalifeh is PhD Candidate, and Dr. Hamdan-Mansour is Professor, Psychiatric Nursing, School of Nursing, The University of Jordan, and Mr. Khalifeh is also Advanced Nurse Specialist/Psychiatric Mental Health Nurse, Department of Nursing, Prince Hamzah Hospital, Ministry of Health, Amman, Jordan.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

The authors thank The University of Jordan School of Nursing for library facilitation and assistance.

Address correspondence to Anas H. Khalifeh, RN, MSN, ANS-PMHN, PhD Candidate, School of Nursing, The University of Jordan, Queen Rania Street, Amman 11942, Jordan; email: anaskhalifeh@yahoo.com.

Received: May 20, 2020
Accepted: July 28, 2020
Posted Online: October 21, 2020

10.3928/02793695-20201015-05

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