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Medication adherence and predictive factors in patients with
cardiovascular disease: A cross-sectional study
Ali Hussein Alek Al-Ganmi BSc, Msc, PhD, RN Nursing
Abdulellah Alotaibi BSc, Msc, PhD, RN Health Sciences
Leila Gholizadeh BSc, Msc, PhD, RN Nursing
| Lin Perry MSc, PhD, RN Nursing
College of Nursing, University of Baghdad,
Faculty of Health, University of Technology
Sydney (UTS), Sydney, Australia
Faculty of Applied Health Science, Shaqra
University, Shaqra, Saudi Arabia
South East Sydney Local Health District,
Ali Hussein Alek Al-Ganmi, Faculty of Health,
University of Technology Sydney (UTS),
P.O. Box 123, Broadway, NSW 2007, Sydney,
Adherence to cardiac medications makes a significant contribution to avoidance of
morbidity and premature mortality in patients with cardiovascular disease. This quanti-
tative study used cross-sectional survey design to evaluate medication adherence and
contributing factors among patients with cardiovascular disease, comparing patients
who were admitted to a cardiac ward (n= 89) and those attending outpatient cardiac
rehabilitation (n= 31) in Australia. Data collection was completed between October
2016 and December 2017. Descriptive and regression analyses were conducted to
identify medication adherence and determine factors independently predictive of med-
ication adherence. Participants from cardiac rehabilitation had significantly lower
adherence to cardiac medications than those recruited from the cardiac ward (58.1 vs
64.0%, respectively). Self-efficacy was significantly associated with participants' medi-
cation adherence in both groups. The ability to refill medications and beliefs about car-
diac medications were independently significantly predictive of cardiac medication
adherence. These findings indicate areas where clinical nurses could expand their role
to improve cardiac patients' medication self-management.
beliefs, cardiac rehabilitation, cardiovascular disease, medication adherence, medication refill,
nursing, predictive factors, self-efficacy
Cardiovascular disease (CVD) is a major cause of mortality and disability
worldwide and was the main cause of mortality in 2017 in Australia,
accounting for 10 514 (13%) and 8076 (10%) deaths among males and
females, respectively (Australian Institute of Health and Welfare, 2019).
Adherence to cardio-protective medications is critical for primary and
secondary prevention of CVD, but it is estimated that up to 50% of
medications are not taken as prescribed by patients with chronic dis-
ease, increasing rehospitalization rates and premature mortality
(Burnier & Egan, 2019).
The World Health Organization defines adherence to medications
as “taking more than eighty percent of medicines as prescribed”
(Sabaté, 2003, p. 3). Medication adherence typically decreases over
time; this is a major challenge in all chronic diseases (Usherwood,
2017). For instance, a Canadian study found that only 36% of elderly
patients with CVD adhered to their cholesterol-related treatments
2 years after diagnosis (Jackevicius, Mamdani, & Tu, 2002). An Ameri-
can study found that 10–25% of patients newly diagnosed with car-
diac disease had stopped taking their medications by their 24-week
follow-up (Sokol, McGuigan, Verbrugge, & Epstein, 2005). In Australia,
cardiovascular medication nonadherence rates ranged between
14 and 43% from 2010 to 2014 (McKenzie, McLaughlin, Clark, & Doi,
2015). Despite the increasing potential for cardiac medications to
impact patients' health, the level of medication adherence in patients
with CVD remains problematic (Leslie, Pell, & Mccowan, 2018). For
Received: 15 July 2018 Revised: 30 November 2019 Accepted: 8 December 2019
454 © 2020 John Wiley & Sons Australia, Ltd Nurs Health Sci. 2020;22:454–463.wileyonlinelibrary.com/journal/nhs
example, a recent randomized controlled trial conducted in Australia
showed medication adherence among patients with CVD ranging from
only 18.8 to 29.4% for intervention and control groups, respectively
(Santo et al., 2019). Poor adherence to medication regimens in
patients with CVD is associated with frequent rehospitalization and
mortality (Al-Ganmi, Perry, Gholizadeh, & Alotaibi, 2016). Given that
medication adherence is an important health behavior worldwide, at
all points in the treatment of CVD, it is essential to determine adher-
ence to cardioprotective medications and potential predictive factors
to initiate behavioral changes as needed.
1.1 |Literature review
Adherence to medication prescriptions is necessary to receive the full
benefits of medications but is a complex and dynamic process. This is
particularly challenging for patients with CVD who are predominantly
discharged from hospital with long-term polypharmacy (Pandey,
Clarus, & Choudhry, 2018). Multiple factors may be influential, includ-
ing patient-related behaviors that are difficult to objectively measure
and monitor (Boudreaux, Baumann, Camargo, O'Hea, & Ziedonis,
2007). Patient knowledge or understanding of a medication regimen,
perceived adherence barriers or facilitators, low self-efficacy, and lack
of belief in the necessity of medicines may influence medication
adherence (Morrison et al., 2015).
Sociodemographic factors are relevant. Studies of age have returned
inconsistent results: while some found those over 65 years particularly
nonadherent, possibly because of cognitive impairment, physical disabil-
ity, and lack of social support (Pamboukian et al., 2008), others found
older patients more likely to be adherent than younger patients (Park,
Howie-Esquivel, Whooley, & Dracup, 2015). Gender may affect medica-
tion adherence (Gazmararian et al., 2006), but again findings are contra-
dictory, perhaps because of the potential for error in self-reporting,
which may produce spurious results. Low medication adherence has
been found to occur in more male than female patients with CVD and
heart failure (Viana et al., 2014). In contrast, female patients with CVD
were shown to be less likely to adhere to cardioprotective regimens than
male patients (Kolandaivelu, Leiden, Gara, & Bhatt, 2014). On the other
hand, Doll et al. (2015) found neither age nor gender was linked to medi-
cation nonadherence in patients with CVD attending cardiac rehabilita-
tion. Ethnicity has been shown with similarly inconsistent results but has
been reported as predictive of medication adherence among patients
with myocardial infarction (Zhang, Baik, Chang, Kaplan, & Lave, 2012).
Employment status has also been found to have an effect, with higher
medication nonadherence rates from unemployed people, particularly
those without healthcare coverage (Lee et al., 2013).
Other influences include health literacy, where poor literacy has
been linked to reduced medication adherence (Gazmararian et al.,
2006). Social support has generally been linked to better medication
adherence, positively affecting CVD outcomes (Wu et al., 2013). One
observational study found that 59.2% of patients with CVD attending
cardiac rehabilitation with support from family and staff were adher-
ent and better able to refill cardiac medications than those with no
support (Molloy, Perkins-Porras, Bhattacharyya, Strike, & Steptoe,
2008). Mood has also been shown to influence patients' self-efficacy,
their beliefs about medications, and medication adherence (Cha, Erlen,
Kim, Sereika, & Caruthers, 2008).
The problem of medication nonadherence has been repeatedly
reported among patients with various chronic diseases and disease
stages (Park, Seo, Yoo, & Lee, 2018). Cardiac patients experience a num-
ber of stages in their disease progression and treatment, including acute
events and hospital admissions, referrals, and attendance at cardiac reha-
bilitation and community self-management (Doucette, 2017). Admission
to hospital for an acute health event has been associated with changes
favoring more health-oriented behaviors even in the absence of any spe-
cific health behavioral intervention (Boudreaux et al., 2007). Among
patients attending cardiac rehabilitation, self-efficacy and beliefs about
the benefits of exercise have been associated with medication adherence
(Pandey et al., 2018). Few studies consider differences in medication
adherence among patients with CVD at different stages of treatment,
although such information could be used to develop strategies for differ-
ent stages. Investigating the dynamics of medication nonadherence may
identify specific factors at specific points, offering the potential to
enhance medication adherence at different stages (King-Shier et al.,
2017). Two such pivotal points in CVD are when an acute event leads to
hospitalization and subsequent rehabilitation. There has been no evalua-
tion of the factors leading to medication nonadherence at either stage.
Overall, poor medication adherence remains a significant challenge in the
management of patients with chronic diseases, including those with
CVD, with a wide, locally relevant but broadly inconsistent variety of pre-
dictive factors. The underlying conceptual framework of medication
adherence and factors that affect patients' adherence to their prescribed
medications warrant further exploration.
1.2 |Study aim
This study aimed to evaluate medication adherence and contributing
factors among patients with CVD and to compare these between
patients admitted to a cardiac ward and those attending cardiac reha-
bilitation in Australia.
This study is part of a larger study exploring medication nonadherence
among patients with CVD, part of which has been published previ-
ously (Al-Ganmi, Perry, Gholizadeh, & Alotaibi, 2018). In brief, this
quantitative study used cross-sectional survey design to examine self-
reported medication adherence and factors potentially predictive of
medication adherence in two groups of patients with CVD with a his-
tory of prescribed cardiac medications. One group had been admitted
to hospital for acute conditions such as acute coronary syndrome and
revascularization interventions; the other was attending cardiac
AL-GANMI ET AL.455
rehabilitation after discharge from hospital following similar acute car-
diac conditions. Potentially predictive factors included beliefs about
medication, medication adherence self-efficacy, social support, and
the ability to refill medication. Two stages in the disease trajectory,
the acute phase and rehabilitation, were chosen to determine if pat-
terns of stage-specific predictive factors could be identified, which
might lead to development of targeted nurse-led interventions to
improve medication adherence at each stage.
2.2 |Participants, sampling, and sample size
Participants were patients with cardiac disease who were admitted to
a hospital in Sydney for an acute cardiac event such as myocardial
infarction or percutaneous coronary intervention or who had been
referred to and attended a cardiac rehabilitation program (Al-Ganmi
et al., 2018). The acute cardiac patient population of the hospital com-
prised approximately 125 patients with CVD admitted per month to
the cardiac ward, with 18% uptake of referrals to the cardiac rehabili-
tation center (unpublished hospital data). The inclusion criteria speci-
fied adults aged 18 years and above with a diagnosis of CVD,
currently treated with one or more cardiac medicines, and personally
responsible for taking them, prior to hospital admission or when
attending cardiac rehabilitation. Participants needed to understand
written and spoken English to complete the survey. Patients with
vision and hearing impairment, cognitively impaired patients, those
with history of psychiatric diseases, and patients who were not taking
any cardiac medication were excluded (see Appendix 1).
A sample size of 85 participants was calculated to demonstrate a
moderate sized effect (α= 0.05, 5% level of significance) and
power = 0.80 based on medication adherence as reported by Ma,
Zhou, Zhou, and Huang (2014) and calculated based on a formula by
Viechtbauer et al. (2015). Taking into account the possibility of as
much as 50% nonresponse or incomplete response, given the busy
clinical environments in which recruitment occurred, the sample was
increased to 129 participants. In total, 120 participants were
2.3 |Study setting
The study setting was one hospital in Sydney, Australia, which has
inpatient diagnostic and interventional cardiac services including a
cardiothoracic intensive care and subacute surgical ward, and a coro-
nary care and subacute cardiology ward.
2.3.1 |Routine medication care
The Australian Cardiac Rehabilitation Association recommends that rou-
tine care for cardiac patients include education on the importance of
adherence to cardiovascular medicines including the reasons for their
use, barriers to medication adherence, medication doses and frequency,
and monitoring medication adherence using a tested tool (Woodruffe
et al., 2014). Nurses and pharmacists are mainly responsible for providing
medication education to patients with CVD during their stay in the car-
diac ward and as a part of their cardiac rehabilitation.
2.4 |Ethical considerations
The study was approved by the relevant health services and university
human research ethic committees (reference numbers:16/085-HREC/
2.5 |Data collection
Between October 2016 and December 2017, 120 completed ques-
tionnaires were obtained from 89 inpatients and 31 outpatients
(Appendix 1). For inpatients, the questions referred to medication
adherence in the period immediately before to hospital admission.
Data collection took place in the cardiac ward for inpatients and dur-
ing attendance at cardiac rehabilitation in the waiting room.
2.5.1 |Study instruments
The survey comprised questionnaires about patients' adherence to
cardiac medication, their beliefs, and behavioral and psychological fac-
tors linked with medication nonadherence in previous studies (Horne,
Weinman, & Hankins, 1999; Morisky, Green, & Levine, 1986), identi-
fied as potentially predictive of medication adherence and offering
opportunities for nurse-led interventions to enhance adherence. The
survey was piloted with five participants; no problems were identified.
Completion took 10–15 min. The language was deemed manageable
for those with poor literacy skills.
Sociodemographic and health-related factors
Data collected in this section included age (years), gender (male, female),
employment status (employed, unemployed, retired), living arrangement
(lives alone or with spouse/partner/others), and marital status (married/
cohabiting or not in a relationship). It also asked for years of full-time
education, ethnic background (Australian/New Zealander, others), pres-
ence of comorbidities such as hypertension, diabetes mellitus, or respira-
tory or renal disease, and number of cardiac medications taken daily.
Comorbidities were included because increasing numbers of diseases are
linked to increasing numbers of medications and both have been linked
to medication adherence. Comorbidities comprised kidney disease, respi-
ratory disease, and diabetes mellitus. Diabetes was identified separately
because of the high occurrence and high burden of medication manage-
ment of this disease in people with CVD.
Questionnaires addressing medication adherence
The validity and reliability of the study instruments to detect medica-
tion nonadherent behaviors in patients with CVD in various cardiac
456 AL-GANMI ET AL.
settings have been established (Al-Ganmi et al., 2018). The Medica-
tion Adherence Questionnaire (MAQ) (Morisky et al., 1986) is a four-
item scale used to assess medication adherence and adherence deter-
minants such as forgetfulness, carelessness, efficacy, and adverse
effects. The MAQ questionnaire has been validated in various patient
populations and patients with cardiac conditions such as heart failure,
CVD, and dyslipidemia (Nguyen, Caze, & Cottrell, 2014). It has dem-
onstrated acceptable internal consistency of α= 0.61, sensitiv-
ity = 0.81, and specificity = 0.44 in patients with hypertension (Lavsa,
Holzworth, & Ansani, 2011). The MAQ score was found to be a signif-
icant independent predictor of cardiovascular medication non-
adherence in a multivariate logistic regression model (Shalansky,
Levy, & Ignaszewski, 2004).
The Adherence to Refills and Medications Scale (ARMS)
(Kripalani, Risser, Gatti, & Jacobson, 2009) is a 12-item scale used to
assess patients' ability to self-administer and refill their medications.
The ARMS subscales are highly correlated with the MAQ-4 items
questionnaire and with medication refill adherence (Kripalani et al.,
2009). The ARMS has been validated in patients with CVD and multi-
ple chronic conditions and has demonstrated high internal consistency
using Cronbach's alpha, with α= 0.814 (Kripalani et al., 2009).
The Belief about Medicine Questionnaire (BaMQ) (Horne et al.,
1999) consists of eight questions used to evaluate patients' beliefs
about the necessity of medications plus concerns, medication overuse,
and general harm. The BaMQ has been shown to be a valid and reli-
able tool, correlated significantly with other medication adherence
questionnaires such as MAQ and the Medication Adherence Rating
Scale (MARS-5) (Gatti, Jacobson, Gazmararian, Schmotzer, & Kripalani,
2009). Each BaMQ subscale has been evaluated for internal consis-
tency using Cronbach's alpha, with αvalues of specific-neces-
sity = 0.77, specific-concerns = 0.76, general-overuse = 0.60, and
general-harm = 0.78 (Horne et al., 1999). The four BaMQ categories
have been shown to correlate highly with patients' beliefs about the
adverse effects of medication and specific-concerns as assessed by
the Sensitive-Soma Scale administered to general medical and cardiac
groups to demonstrate the criterion validity of BaMQ (Horne
et al., 1999).
The Medication Adherence Self-Efficacy Scale-Revised (MASES-
R) (Fernandez, Chaplin, Schoenthaler, & Ogedegbe, 2008) is a 13-item
questionnaire to evaluate patients' confidence in taking their medica-
tions as part of everyday routine. The MASES-R has been shown to
correlate significantly with electronic medication adherence records
(MEMS) at 3 months, confirming its predictive validity. The MASES-R
has been evaluated for internal consistency using Cronbach's alpha,
with α= 0.91 (Fernandez et al., 2008). The concurrent validity of the
MASES-R has also been confirmed.
The Medication Specific Social Support (MSSS) scale (Lehavot
et al., 2011) is an eight-item scale to evaluate how often patients
receive support with their medication from family, friends, or
healthcare providers (Lehavot et al., 2011). The MSSS has demon-
strated high internal consistency using Cronbach's alpha, with
α= 0.85 (Lehavot et al., 2011). Permission to use these questionnaires
was obtained from their authors.
2.6 |Data analysis
Nine of the 129 questionnaires were incomplete due to missing infor-
mation regarding medication adherence. These questionnaires were
not included in the analysis (Appendix 1). Complete data were
obtained from 120 patients (Appendix 1). The questionnaires were
analyzed using IBM SPSS version 23. Descriptive statistics were con-
ducted to analyze patients' sociodemographic characteristics, medica-
tion adherence values, medication adherence self-efficacy, beliefs
about medication, and social support. Among sociodemographic and
health- and medication-related data, continuous variables were pres-
ented as means and standard deviations, and categorical variables by
frequencies and percentages (Table 1). Independent samples t-tests
were used to analyze differences between patients with CVD from
the cardiac rehabilitation and cardiac ward groups with continuous
variables, and chi-squared (χ
) tests were used for categorical vari-
ables. Medication adherence was categorized as high, medium/low,
and chi-squared (χ
) was used to compare the patient groups. Bivari-
ate analyses were used to examine the association between potential
medication adherence factors and medication adherence (MAQ scale)
using Spearman's rank correlation coefficient (rho). Variables signifi-
cantly associated with medication adherence in these analyses were
examined using logistic regression, reporting the odds ratios and con-
fidence intervals for predictive variables. The level of significance was
set at less than 0.25 for entry into regression models and less than
0.05 for the logistic regression test. The forced entry method was
used, in which all potential predictors were forced into the model in
the first step then sequentially removed. Two-sided tests were con-
ducted with significance set at 0.05.
3.1 |Participants' characteristics
Participants' characteristics are presented in Table 1. Most
sociodemographic and health data did not differ significantly between
participants in the two study settings. Compared to participants from
the cardiac ward, cardiac rehabilitation participants took significantly
fewer cardiac medications per day (Table 1). They tended to be better
educated, more often employed, and younger, although these differ-
ences between the groups were not statistically significant (Table 1).
3.2 |Medication adherence
Based on the four-item MAQ scores, 62.5% of patients in both groups
were classified as high (scoring 0) and 37.5% as medium/low adherent
(scores 1–4). Since only one patient reported low medication adher-
ence, we dichotomized the dependent variable into two groups: high
adherence (score 0) and medium/low adherence (score 1–4).
Medium/low adherence rate was higher among participants recruited
from cardiac rehabilitation rather than the cardiac ward (41.9 vs
AL-GANMI ET AL.457
36.0%; respectively; P= 0.001). Cardiac rehabilitation participants
were also more likely to forget the names of their medications than
ward participants (30.8 vs 16.1%, respectively; P= 0.04). Forgetful-
ness was the most commonly reported reason for medication non-
adherence (32.3 vs 31.5% in outpatients and inpatients, respectively).
Medication-related variables (ARMS, MASES, BaMQ, MSSS) did not
differ significantly between the groups (Table 2).
None of the 12 sociodemographic or health variables were signifi-
cantly associated with medication adherence, although the ability to
refill medications (ARMS), medication adherence self-efficacy
(MASES-R), and beliefs about medications (BaMQ) were all signifi-
cantly associated with medication adherence (MAQ), demonstrating
positive moderate-strong correlations which explained 45, 15, and
11% of patients' medication adherence, respectively (Table 3). Pat-
terns of the associations revealed similar findings when the analyze
were conducted separately for each group. In both groups, there was
a significant link between MAQ with ARMS and MASESR (for ward
patients, Rho = 0.655, P< 0.001; Rho = 0.355, P< 0.002, respectively;
for rehabilitation patients, Rho = 0.716, P< 00.1; Rho = 0.498,
P< 0.001, respectively). For ward-based participants, the association
between MAQ and BaMQ just missed statistical significance
(Rho = 0.200, P< 0.06), which was demonstrated for rehabilitation
participants (Rho = 0.696, P< 0.001).
These variables were entered in the sole binary logistic regres-
sion model that was created; see Table 4 (Al-Ganmi et al., 2019).
The results of the analysis indicated that ability to refill cardiac
medications and beliefs about cardiac medications were significant
predictors of cardiac medication adherence: participants with
greater ability to refill cardiac medications (odds ratio = 0.463,
P= 0.001), and patients with more positive beliefs about their med-
ications were more likely to report better medication adherence
(odds ratio = 1.142, P= 0.04). The logistic regression analysis
recorded a significant Omnibus test for the model (significance
<0.001). The Pseudo R
statistic indicated that the model, as a
whole explained between 38.7% (Cox & Snell R
52.8% (Nagelkerke R
= 0.528) of the variance in medication
TABLE 1 Sociodemographic and health characteristics of patients recruited from the cardiac rehabilitation department and the cardiac ward
(n= 31), No. (%)
(n= 89), No. (%)
Test df P-Values
Male 19 (61.3%) 59 (66.3%) 0.25 1 0.62
Female 12 (38.7%) 30 (33.7%)
Employed 10 (32.3%) 21 (23.6%) 7.18 2 0.03*
Unemployed 6 (19.4%) 5 (5.6%)
Retired 15 (48.4%) 63 (70.8%)
Lives alone 4 (12.9%) 23 (25.8%) 2.21 1 0.14
Lives with spouse/partner/others 27 (87.1%) 66 (74.2%)
Married/ co-habiting 23 (74.2%) 54 (60.7%) 1.83 1 0.18
Not in a relationship 8 (25.8%) 35 (39.3%)
Australian/New Zealander 18 (58.1%) 57 (64.0%) 0.35 1 0.55
Others 13 (41.9%) 32 (36.0%)
None 26 (83.9%) 68 (76.4%) 0.76 1 0.39
Any 5 (16.1%) 21 (23.6%)
No 21 (67.7%) 56 (62.9%) 0.23 1 0.63
Yes 10 (32.3%) 33 (37.1%)
t-test df P-values
Age, mean (SD) years 66.6 (11.9%) 69.9 (11.5) −1.38 118 0.17
Years of full-time education; mean (SD) 13.9 (3.6) 11.9 (3.5) 2.63 118 0.01**
Number of cardiac medications taken per day; mean (SD) 2.8 (1.1) 3.2 (0.9) 2.57 118 0.01**
Abbreviations: SD, standard deviation.
*Significant at 0.05.
**Significant at 0.01.
458 AL-GANMI ET AL.
adherence. ARMS + BaMQ explained 39.6% of variance but ARMS
alone explained 36.6% of variance in MAQ.
More than one-third of both groups had medium/low cardiac medica-
tion adherence. More participants recruited from cardiac rehabilitation
reported medium-low medication adherence than those recruited as
cardiac ward in-patients (41.9 vs 36.0%). Medication-related variables
(ARMS, MASES, BaMQ, MSSS) did not differ significantly between
participants recruited from the two settings.
Our findings are slightly better but broadly consistent with older
data from the World Health Organization (2003), which highlighted
medication nonadherence at 50%, with differing predictive factors in
patients with chronic and acute cardiac diseases. At 36%, these find-
ings from acute cardiac patients lie within the rates reported for simi-
lar patients elsewhere: in studies of patients with acute myocardial
infarction, where 53.6% were found to be nonadherent to cardiac
medications (Choudhry, Setoguchi, Levin, Winkelmayer, & Shrank,
2008). Patients referred to cardiac rehabilitation improved their
adherence to cardiac medication from 43 to 55% (Shah et al., 2009);
in contrast, in this study adherence rate were worse in the rehabilita-
Consistent patterns of poor medication adherence in different
patient groups suggest there may be common underlying problems,
perhaps of inadequate patient education, or at least of patients' inade-
quate understanding of the importance of medication adherence,
whether at the initiation of therapy or latter (Woodruffe et al., 2014).
Perhaps this is the result of faulty beliefs or practical or attitudinal
barriers among patients attending cardiac rehabilitation programs
after acute cardiac events (Verburg, Selder, Schalij, Schuuring, &
Treskes, 2019). Understanding patients' medication adherence behav-
iors is the first step toward enhancing their self-management and
improving patients' outcomes. This study showed unacceptable adher-
ence to cardiac medication in both groups, and in those recruited from
the cardiac ward, this may have contributed to their hospital admis-
sion. The poor medication adherence reported by those undertaking
TABLE 2 Medication adherence-related variables in patients recruited from the cardiac rehabilitation department and the cardiac ward
No. (%) n= 120
No. (%) n=31
No. (%) n=89
Test df P-Values
High (0) 75 (62.5%) 18 (58.1%) 57 (64.0%) 28.033 1 0.001*
Medium/low (1–4) 45 (37.5%) 13 (41.9%) 32 (36.0%)
Can remember all medications 83 (69.2%) 83 (69.2%) 26 (83.9%) 4.24 1 0.04**
Cannot remember all medications 37 (30.8%) 37 (30.8%) 5 (16.1%)
t-test df P-values
ARMS mean (SD) 45.10 (3.1) 45.72 (2.9) −1.000 118 0.32
MASES mean (SD) 34.06 (6.6) 35.6 (5.5) −1.282 118 0.20
BaMQ mean (SD) 30.3 (6.1) 31.9 (4.4) −1.474 118 0.14
MSSS mean (SD) 10.6 (5.6) 11.5 (7.5) −0.577 118 0.56
MAQ individual items scores
mean (SD) 3.5 (0.72) 3.5 (0.8) −0.06 118 0.95
Forget to take
Yes, No. (%) 38 (31.7%) 10 (32.3%) 28 (31.5%)
No, No. (%) 82 (68.3%) 21 (67.7%) 61 (68.5%)
Careless at timesYes, No. (%) 13 (10.8%) 3 (9.7%) 10 (11.2%)
No, No. (%) 107 (89.2%) 28 (90.3%) 79 (88.8%)
Sometimes stop taking when feel better
Yes, No. (%) 5 (4.2%) 1 (3.2%) 4 (4.5%)
No, No. (%) 115 (95.8%) 30 (96.8%) 85 (95.5%)
Sometimes stop taking when feel worse
Yes, No. (%) 9 (7.5%) 3 (9.7%) 6 (6.7%)
No, No. (%) 111 (92.5%) 28 (90.3%) 83 (93.3%)
*P< 0.001.; **P< 0.01.; Note: MAQ level: (0) = all answers with “No,”(1–4) = one to four answers with “Yes.”
Morisky medication adherence scale, higher scores reflect nonadherence.
Note: ARMS, Ability to Refill Medication & Self-Management; BaMQ, Belief about Medication; df, degree of freedom; MASES, Medication Adherence
Self-Efficacy; MSSS, Medication Social Support; n, number of participants; SD, standard deviation.
AL-GANMI ET AL.459
rehabilitation indicates risk of recurrent cardiac disease and future
The logistic regression results showed that the ability to refill and
administer medications was significantly predictive of adherence to
cardioprotective medications for both inpatient and rehabilitation
groups. Findings are congruent with those of Kripalani et al. (2009),
who found that patients with CVD who were better able to refill their
medications and self-manage had better adherence to regimens.
Other studies suggest that patients' ability to refill medications may
be linked to younger age, the number of daily medications, and
patients' perceptions about the complexity of their medicines and
ability to self-manage (Magnabosco et al., 2015). The ability to refill
medications in a timely manner can be affected if patients have lower
physical activity and mobility or if younger patients lack understanding
of their susceptibility to CVD-related complications (Magnabosco
et al., 2015). Gazmararian et al. (2006) indicate that individuals with
multiple medications manage refills better, as they are more focused
on management of their health and resources for managing complex
polypharmacy are readily available. These and other findings while
sometimes inconsistent and even contradictory, make it clear that,
while patients' ability to refill medications predicts medication adher-
ence, other factors also exert significant influence in various groups.
These findings flag the importance of examining individual patients at
inpatient and outpatient settings and looking for factors, such as abil-
ity to refill medications, that may affect their medication adherence.
Like some other studies, our study found that patients with CVD
from both inpatient and outpatient settings who held more positive
beliefs about cardiac medications were more adherent to their medi-
cations. Several studies have found that patients' beliefs about the
necessity of their medication were significantly predictive of medica-
tion adherence, for example, inpatients with acute coronary syndrome
(Allen LaPointe et al., 2011) and outpatients attending cardiac rehabili-
tation (Cooper, Weinman, Hankins, Jackson, & Horne, 2007). Uncer-
tainty about the necessity of taking medications predicts lower
medication adherence, specifically when patients have concerns about
side effects (Horne et al., 1999). Patients who hold less than strong
beliefs about their medications and have concerns about side effects
reported forgetting or deliberately skipping prescribed doses (Sabaté,
2003). Our findings from both settings, that positive beliefs about car-
diac medications were linked to medication adherence, are in line with
other findings and highlight an important opportunity for
According to Bandura (2004), the central determinant affecting
an individual's specific behaviors is self-efficacy, which influences
motivation and affects other determinants. Bivariate analyses revealed
TABLE 3 Associations between medication adherence (MAQ) and
potentially predictive variables in both participant groups
Variables Rho PValue
Age −.023 0.803
Gender −.132 0.803
Location of recruitment .030 0.745
Employment status .046 0.620
Living arrangement .121 0.188
Marital status −.111 0.229
Ethnicity −.155 0.092
Number of full-time years of education −.131 0.41
Comorbidity −.055 0.554
Diabetes mellitus −.129 0.160
Medications recall .099 0.281
Total number of pills/day −.079 0.660
Ability to refill medication and
Medication adherence self-efficacy (MASES-R) .392
Beliefs about medication (BaMQ) .335
Medication specific social
*Correlation is significant at the P-value = 0.001 level (2-tailed).
Spearman's rank correlation rho (odds ratio).
Note: ARMS, Ability to Refill Medication & Self-Management; BaMQ,
Belief about Medication; MAQ, Medication Adherence Questionnaire;
MASES-R, Medication Adherence Self-Efficacy; MSSS, Medication specific
TABLE 4 Binary logistic regression model examining predictors of cardiac medication adherence (Al-Ganmi et al., 2019)
Error Wald df Sig.
95% CI EXP (B)
Age .795 .621 1.638 1 .201 2.215 .655 7.485
.150 .631 .056 1 .813 1.162 .337 4.004
ARMS 0.366 0.499 −.771 .170 20.520 1 .001*.463 .332 .646
MASES .000 .060 .000 1 .998 1.000 .889 1.124
BaMQ 0.396 0.539 .133 .065 4.178 1 .041*1.142 1.005 1.298
MAQ 0.387 0.527
Constant 30.220 6.990 18.689 1 <0.001 133 138 346 410.104
Variable(s) entered on step 1: age, ARMS, Ability to Refill Medication & Self-Management; BaMQ, Belief about Medication Questionnaire;
MAQ, Medication Adherence Questionnaire; MASES, Medication Adherence Self-Efficacy Scale.
460 AL-GANMI ET AL.
that medication self-efficacy was significantly associated with medication
adherence, a finding consistent with the results of two cross-sectional
studies of patients attending cardiac rehabilitation (Greer, Milner, Mar-
cello, & Mazin, 2015). However, differing from the results of Morrison
et al. (2015), the impact of medication self-efficacy as an independent
predictor of medication adherence disappeared in our regression analy-
indicate opportunities for cardiac ward and rehabilitation nurses and
pharmacists to develop stage-specific strategies to promote medication
self-efficacy as a way to enhance medication adherence.
This study, like that of Wu et al. (2013), did not find sociodemographic
factors to be significantly predictive of medication adherence. However,
other studies have found age, for example, to be significantly predictive,
and significantly better medication adherence to hypertension medica-
tions has been noted among older people (Pamboukian et al., 2008). Given
that the patients with CVD recruited for our study from cardiac rehabilita-
tion were young and likely to be employed and married, we might surmise
that these patients had limited time and perhaps other activities took pre-
cedence over regular adherence to medications. Other studies have also
been inconsistent about the effect of sociodemographic factors on medi-
cation adherence (Park et al., 2015).
Medication adherence, not just prescription, should be recognized
as an essential focus for cardiac patients and for policy-makers. Medi-
cation adherence education and counselling should be prioritized for
cardiac patients in all hospital settings, with education sessions and
face-to-face counseling integrated into care plans. The reinforcement
of medication importance during cardiac rehabilitation and in routine
follow-up visits will also improve medication refill compliance and
enhance cardiac patients' beliefs in the necessity of taking their medi-
cations. Establishing medication adherence plans for patients in car-
diac care settings will help nurses identify those who are likely to be
nonadherent and target individual patients who need extra help with
More attention is required to the role of cardiac nurses in
assessing cardiac patients' self-management and ability to refill medi-
cation and in promoting innovative forms of follow-up that enhance
their role in cardiac rehabilitation. An effective care provider–patient
relationship will be an important component in building an encourag-
ing environment to achieve treatment plan goals. Tailoring educational
interventions to target cardiac patients' beliefs about cardiac medica-
tion may be an effective approach to enhance patients' beliefs about
the efficacy of these medications and to increase adherence to them.
Simple interventions such as electronic prompts, interactive packages
of education, or reminders through text messages or phone calls using
smartphone and tablet devices, all easily manageable by nurses, have
been found useful in improving adherence to medication in patients
with CVD (Ferdinand et al., 2017).
4.1 |Study limitations
Because we recruited at a single site, study results may not be gener-
alizable. The study was conducted in busy clinical cardiac settings
where the cardiac patients were asked to complete questionnaires
shortly after a rehabilitation session or (in the ward) when they were
deemed clinically stable. Tiredness may have affected the attention to
the survey questions, and the reported medication nonadherence
rates may not have been completely accurate. Cardiac rehabilitation
was a particularly challenging location for recruitment, with staff and
cardiac patients time-pressured and preoccupied. Despite an ade-
quate study size, careful consideration must be paid when comparing
the results for different cardiac patient groups. Future studies could
take into account the impact of clustering and sampling.
Use of self-report questionnaires may have biased the study find-
ings, with more socially acceptable but incorrect responses entered,
as is the case in many studies. Use of standardized surveys meant par-
ticipants could only provide ranked responses to the questions, and a
qualitative enquiry providing a deeper understanding of cardiac
patients' perspectives would be valuable. Differences between site
sample sizes may have limited our ability to determine significant
intersite differences. Any differences in the prescribed cardiac medi-
cations between acute inpatient and rehabilitation participants were
not considered, which may have impacted patients' adherence. Finally,
as with many medication adherence studies, lack of a gold standard
assessment introduces an element of uncertainty into the reported
medication adherence assessments.
There is a need to enhance adherence to medication in patients with
CVD through consideration of factors significantly associated with and
predictive of medication adherence. This cross-sectional study demon-
strates that ability to refill medications and positive beliefs about medica-
tion are independently predictive of greater cardiac medication
adherence in patients with CVD. This suggests, first, that strategies are
urgently required to improve the poor medication adherence demon-
strated in this and other studies. Second, these strategies should be tai-
lored to the factors that deter timely medication refill and to negative
beliefs about medication adherence. Such interventions could include
innovative educational interventions and counselling sessions by clinical
nurses. Cardiac nurses have an opportunity to enhance their roles in
assessing and improving cardiac patients' ability to refill medication and
their beliefs about those medications which, in turn, should improve
medication adherence and outcomes.
We express our gratitude to the Cardiac Rehabilitation Clinical Nurse
Consultant and the Clinical Pharmacist of the study site for their sup-
port. We acknowledge the contributions of all patients who took part
in the study. Thanks to Dr. Mohammed Baqer Habeeb Abd Ali, statis-
tician, College of Nursing, University of Baghdad for assistance with
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
AL-GANMI ET AL.461
Permission to use the Medication Adherence Questionnaire, the
Adherence to Refills and Medications Scale, the Belief about Medicine
Questionnaire, the Medication Adherence Self-Efficacy Scale-Revised,
Medication Specific Social Support and Table 4 has been granted.
Study design: A.A., L.P., and L.G.
Data collection: A.A.
Data analysis: A.A., L.P., L.G., and A.A.
Revisions for important intellectual content: A.A., L.P., L.G.,
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How to cite this article: Al-Ganmi AHA, Alotaibi A,
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