Medication knowledge, adherence and predictors among people with heart failure and chronic...

ORIGINAL ARTICLE doi: 10.1111/j.1752-9824.2010.01077.x

Medication knowledge, adherence and predictors among people with

heart failure and chronic obstructive pulmonary disease

Robyn Gallagher PhD, RN

Associate Professor of Chronic and Complex Care, Faculty of Nursing, Midwifery & Health, University of Technology, Sydney,

Lindfield, NSW, Australia

Melannie Warwick RN, BN

Research Assistant, Faculty of Nursing, Midwifery & Health, University of Technology, Sydney, Lindfield, NSW, Australia

Lynn Chenoweth PhD, RN

Professor of Aged and Extended Care Nursing, Faculty of Nursing, Midwifery & Health, University of Technology Sydney,

Lindfield, NSW, Australia

Jane Stein-Parbury PhD, RN

Professor of Mental Health Nursing, Faculty of Nursing, Midwifery & Health, University of Technology Sydney, Lindfield,

NSW, Australia

Kathleen Milton-Wildey PhD, RN

Senior Lecturer, Faculty of Nursing, Midwifery & Health, University of Technology, Sydney, Lindfield, NSW, Australia

Submitted for publication: 18 August 2010

Accepted for publication: 4 December 2010

Correspondence:

Robyn Gallagher

Associate Professor of Chronic and Complex

Care

Faculty of Nursing

Midwifery & Health

University of Technology

Sydney

Lindfield

NSW

Australia

Telephone: 61 2 9514 4833

E-mail: [email protected]

GALLAGHER R, WARWICK M, CHENOWETH L, STEIN-PARBURY J & MILTON-GALLAGHER R, WARWICK M, CHENOWETH L, STEIN-PARBURY J & MILTON-

WILDEY K (2011)WILDEY K (2011) Journal of Nursing and Healthcare of Chronic Illness 3, 30–40

Medication knowledge, adherence and predictors among people with heart failure

and chronic obstructive pulmonary disease

Background. Although medicines are a key component in the self-management of

chronic illness, lack of adherence is a common problem.

Aim. To describe medication adherence and predictors in relation to the Multidi-

mensional Adherence Model among older adults with chronic illness.

Method. During a home interview, we collected data from 118 patients with chronic

illnesses (chronic obstructive pulmonary disease and heart failure), following a

recent illness exacerbation, to determine self-reported medication adherence,

medication knowledge and capacity for self-management of their illness. We used

the Medication Adherence Model as an organising framework and performed

multivariate analyses to determine the independent predictors. We conducted the

study between April 2005–June 2006.

Results. Participants had an average age of 75Æ54 years (SD 8Æ38), with mar-

ginally more men (56Æ8%) than women, and were prescribed an average 4Æ68 (SD

2Æ11) medications for their primary diagnosis of either chronic obstructive pul-

monary disease or heart failure. Most participants (75Æ2%) were adherent to

30 � 2011 Blackwell Publishing Ltd

their prescribed medicines; however, medication knowledge was low [mean score

47Æ61 (SD 18Æ73) out of a potential 100]. Predictors of better adherence to

medicines were patient-related: female gender and higher self-management

capacity, and condition-related: heart failure diagnosis. Socioeconomic and

treatment-related factors were not identified as independent predictors of medi-

cation adherence. Predictors of better medication knowledge were higher capacity

for self-management, more concurrent conditions, younger age and taking fewer

medicines.

Conclusion. Assessment of self-management capacity, targeting interventions to-

wards patients with chronic obstructive pulmonary disease and men, rather than

relying solely on increasing medication knowledge, is essential to improve med-

ication adherence. The Multidimensional Adherence Model requires further

investigation to determine its use in patients with chronic illness in general.

Relevance to clinical practice. The most effective interventions to improve

medication adherence are less likely to be those focused on patient education,

and more likely to be those tailored to increasing patients’ self-management

capacity. Programmes will need to involve formal and informal carers. Simple

strategies designed to improve medication adherence, such as medication

reminders, daily routines, and multi-dose packs have been reported to be effective

in systematic reviews and by patients’ themselves. We recommend all of these

strategies to improve medication adherence in persons with chronic illnesses.

Key words: chronic illness, chronic obstructive pulmonary disease, heart failure,

medication adherence, medication knowledge, self-management

Introduction

Chronic illnesses have been identified as the primary con-

tributor to the burden of disease worldwide (World Health

Organisation [WHO] 2004). Moreover, two of the most

prevalent of these chronic illnesses globally are chronic

obstructive pulmonary disease (COPD) and heart failure

(HF). Moderate-to-severe COPD affects 80 million people

and symptomatic HF affects 15 million people worldwide

(WHO 2004). The long-term prognosis for individuals with

both illnesses is poor; however, optimal disease management

helps to control symptoms, slow illness progression, and

improve health-related quality of life (Global Initiative for

Chronic Obstructive Lung Disease [GOLD] 2008, Jessup

et al. 2009).

One key aspect of disease management in COPD or HF is the

progressive introduction of medicines, which must be taken

long-term to offset deteriorating respiratory and cardiac

function effectively. Optimal self-management, in particular

adherence to these medication regimens, is critical to ensure

that patients receive the full benefit of the prescribed medicines

and that their effectiveness can be accurately assessed (George

et al. 2007; Restrepo et al. 2008). However, non-adherence to

medicines is a well-recognised problem among individuals on

any long-term medication, including those with chronic

illnesses such as HF or COPD (DiMatteo 2004, WHO

2004). Estimates of medication adherence vary from

67–90% (Gonzalez et al. 2004, Van Der Wal et al. 2006,

Wu et al. 2008a), in people with HF, and from 27–64%

(DiMatteo et al. 2002, George et al. 2007, Restrepo et al.

2008, Laforest et al. 2010), in people with COPD, with

estimates dependant upon how adherence is determined and

the specific nature of the samples.

Adherence is difficult to define and quantify accurately in

patients with multiple, related medications, particularly when

the medication regimens change in response to exacerbations

and when pharmacist dispensed prefilled packs are commonly

used (Choudry et al. 2009). A definition of adherence is

required which is concurrent with measurement and accounts

for prefilling strategies. In this study, adherence is defined as

the proportion to which participant’s medication taking

corresponds with the prescriptions on the current medicine

container or the pharmacist’s list accompanying prefilled

packs (Sabate 2003). Adherence would therefore be 100%

and any proportion less than 100% could be considered non-

adherence.

Original article Medication adherence in chronic illness

� 2011 Blackwell Publishing Ltd 31

Non-adherence results in serious consequences for pa-

tients with chronic illnesses, markedly reducing their chance

of optimal outcomes (DiMatteo et al. 2002). For example,

in people with HF or COPD, non-adherence can lead to an

increased risk of premature mortality (Vestbo et al. 2009),

emergency department presentations (Hope et al. 2004),

preventable hospital admissions (Murray et al. 2009, Vestbo

et al. 2009) and longer length of stay in hospital (Balkrish-

nan & Christensen 2000). While the consequences of non-

adherence to medicines in the treatment of chronic illnesses

have been identified, understanding its contributing factors

is multidimensional and complex (Osterberg & Blaschke

2005, Lehane & McCarthy 2009).

A multiplicity of factors have been identified as influenc-

ing medication adherence in patients with chronic illnesses,

including age (Steiner et al. 2009, Chan et al. 2010,

Sundberg et al. 2010), gender (Wong et al. 2009, Chan

et al. 2010, Sundberg et al. 2010), ethnicity (Wu et al.

2008a, Steiner et al. 2009, Chan et al. 2010), social support

(Wu et al. 2008a, Steiner et al. 2009), medication knowl-

edge (George et al. 2005, Van Der Wal et al. 2006, Turner

et al. 2009), illness severity (Wu et al. 2008a), comorbidity

(Van Der Wal et al. 2006, Steiner et al. 2009) and difficul-

ties with taking medicines (George et al. 2005, Lam et al.

2007, Wu et al. 2008a, Turner et al. 2009) including the

complexity of the medication regimen (Osterberg & Blas-

chke 2005, Lam et al. 2007, Steiner et al. 2009, Laforest

et al. 2010). Many of these factors are potentially interre-

lated, for example, older age can be associated with the

development of cognitive and functional impairments (Jow-

sey et al. 2009) as well as the development of additional

chronic illnesses (DiMatteo 2004), thus impacting on the

patient’s ability to understand the complex requirements

surrounding a medication regimen (Restrepo et al. 2008,

Murray et al. 2009). It is not surprising therefore that

advancing age is reported to increase the risk of non-

adherence to medicines in patients with either HF (Murray

et al. 2009, Steiner et al. 2009, Chan et al. 2010) or COPD

(George et al. 2007, Restrepo et al. 2008).

Given the number of factors and potential complexity of

the relationships between these factors and medication

adherence, the use of an organising framework enables

systematic investigation. Furthermore, an organising frame-

work is necessary to target the most relevant variables to

include in the multivariate analyses required, as low

explanatory power is a common limitation in the literature

reviewed (DiMatteo 2004, Kim et al. 2007, Restrepo et al.

2008, Wu et al. 2008b, Chan et al. 2010). One such

framework is the WHO’s Multidimensional Adherence

Model (MAM) (Sabate 2003), which organises the factors

that potentially affect treatment adherence into five catego-

ries. These are classified as patient-related, condition-

related, therapy-related, healthcare system-related and

socioeconomic factors.

While derived from empirical evidence, the model has had

limited testing; however, a study by Wu et al. (2008a) found

evidence to support the conceptual relationships in the

model, with the exception of healthcare system-related

factors, in patients with HF. There is evidence, however,

that some patient-related aspects may have precedence. For

instance, the effects of age are not as evident when the

overall capacity to self-manage medications, including the

patients’ ability to understand and manage their medicines,

are taken into consideration both in COPD (George et al.

2005, Laforest et al. 2010) and HF (Reid et al. 2006, Chen

et al. 2007, Wu et al. 2008b). Indeed, a patient’s limited

medication knowledge may act as a barrier, as a poor

understanding of medicines is frequently identified as such

in patients with chronic illnesses (George et al. 2005,

Osterberg & Blaschke 2005, Gordon et al. 2007, Turner

et al. 2009). Moreover, a lack of understanding, confusion,

or misperceptions about medicines increases the likelihood

of non-adherence, even when patients’ intention is to adhere

(Chen et al. 2007). Regardless of the reason, it is clear that

when an older person lacks the capacity to self-manage,

then their medication adherence declines and the role of

resources, including formal and informal assistance with

their medicines, becomes more important in achieving

adherence (Osterberg & Blaschke 2005, George et al.

2007, Lam et al. 2007, Kuzuya et al. 2008). Importantly,

unlike many other factors, self-management capacity, med-

ication knowledge and assistance with medication regimens

are amenable to intervention.

The MAM was used as an organising framework for this

study as it included these key patient-related, condition-

related, therapy-related, and socioeconomic factors. No

study was found which used this model among patients

with COPD. Finally, given the barriers that older adults

with COPD and HF face with relation to medication

adherence following an acute illness exacerbation, such as

hospital admission (Moser et al. 2005, Mansur et al. 2008,

Restrepo et al. 2008), this study focuses on older patients

recovering from such events.

Aim

The aim of this study was to describe medication adherence

and the related predictors in relation to the MAM in older

adults following an exacerbation of a chronic illness (HF or

COPD).

R Gallagher et al.


Method

The current study presents findings from a cross-sectional

analysis of data taken from a larger study on self-manage-

ment in multiple chronic illness groups (Gallagher et al.

2008). This study employed a prospective, descriptive design

and was conducted in the South Eastern Sydney and

Illawarra Area Health Service in Australia between April

2005–June 2006. In the study context, all participants with

HF and COPD were offered support to self-manage their

condition through specialty HF or COPD disease-specific

support programmes, which included hospital-based rehabil-

itation programmes and community-based coordinated care.

The study was approved by the human research ethics

committees of both the area health service and the affiliated

university.

Participants

A convenience sample of patients with either HF or COPD

was recruited through bi-weekly screening of hospital

admission records and hospital and community-based dis-

ease-specific support programmes. Patients were considered

eligible if they: were aged over 55 years and living in the

community, had had a recent exacerbation of their illness

(either requiring hospital admission or additional home visits

by the staff of specialty disease-specific support programmes),

were primarily responsible for their own disease management

and activities of daily living, were able to understand, give

consent and respond to surveys in spoken or written English,

and had moderate severity of illness. The eligibility criterion

relating to those aged more than 55 years was used, as

statistics show that 25% of Australians aged over 55 years

have been diagnosed with a serious or profound chronic

condition(s), with this population accounting for 62% of all

hospital bed days for investigation and/or treatment of these

conditions (Australian Institute of Health and Welfare

[AIHW] 2008). Moderate severity of illness was classified

in individuals with HF as a New York Heart Association

Class II or III using the Specific Activity Scale (Goldman et al.

1981), and in individuals with COPD as a total score of 10 to

34 on a modified version of the St George’s Respiratory

Questionnaire (Jones et al. 1991). Patients were excluded

from the study if they had been admitted to hospital with

either HF or COPD on more than one occasion in the three

months prior to the commencement of the study or had a

major psychiatric illness.

The sample size was calculated for the multiple regression

analysis on medication adherence using an Alpha of 0Æ05, a

medium effect size, and a power of 0Æ8 with 11 variables

entered, namely age, gender, marital status, ethnicity,

primary diagnosis of HF or COPD, illness severity, number

of comorbid conditions, participation in disease management

programmes, number of primary diagnosis-related medicines,

assistance with medicines, and medication knowledge. A

sample of 122 participants was determined to be required

(Soper 2009). A total of 133 patients were approached and

121 were recruited; however, three participants were with-

drawn between recruitment and interview resulting in a final

sample size of 118, creating a slight shortfall.

Data collection

We used multiple instruments to collect data in accordance

with the MAM factors: patient-related, condition-related,

therapy-related and socioeconomic; these instruments are

described later in this section. Data on patient-related factors

included age, gender, medication knowledge [measured by

the Self-Care Behaviours Medication Checklist (Colinet et al.

2003, Jerant et al. 2008)], and self-management capacity

[measured by the Partners in Health Scale (Battersby et al.

2003)]. Data on condition-related factors included the

number of comorbid conditions, self-rated health [measured

by the Self-Rated Health Scale (Lorig et al. 1996)], and

symptom severity [measured in patients with HF by the

Specific Activity Scale (Goldman et al. 1981) and in patients

with COPD by the St Georges Respiratory Questionnaire

(Jones et al. 1991)]. Data on therapy-related factors included

the number of medicines prescribed for either HF or COPD

[measured by the Self-Care Behaviours Checklist (Colinet

et al. 2003)] and participation in a specialty disease support

programme. Socioeconomic factors included ethnicity, edu-

cation, marital status and assistance with medicines [mea-

sured by Self-Care Behaviours Checklist (Colinet et al.

2003)].

Following recruitment and informed consent, we collected

clinical and demographic data from the patient’s medical

records or from the participants themselves during an

interview. This information included age, gender, marital

status, ethnicity, disease severity, concurrent health condi-

tions, HF or COPD related hospital admissions and partic-

ipation in COPD or HF specialty disease management

programmes. We then interviewed participants at home

using a survey package, which included the Self Care

Behaviours Medication Checklist (Colinet et al. 2003), the

Partners in Health Scale (Battersby et al. 2003), and the Self-

Rated Health Scale (Lorig et al. 1996). Interviews were

usually of 45 minutes to one hour in duration, and were

conducted by three clinical nurse specialists specifically

trained in using the research instruments to ensure



standardised techniques throughout. It was important for

interviews to be conducted by nurses who had expert

knowledge of HF and COPD medications to implement the

Self Care Behaviours Medication Checklist accurately.

The survey package was pilot tested on a group of 10

patients with HF and COPD. The pilot indicated that

interviews could become very tiring for participants with

more severe symptoms, multiple conditions and complex

medication regimens. In particular, it became obvious that

having to convey information about all their medicines

potentially affected the accuracy of recall and therefore only

the medicines related to the patient’s primary diagnosis of

either COPD or HF were reviewed.

Instruments

Self care behaviours medication checklist

This checklist is a predominately self-report tool and was

selected because electronic medication monitors were not

feasible as many participants used blister-packed medicines,

pharmacist-prepared dosage packs, and self-filled dosette

boxes. Furthermore, whilst self-report measures are subject

to recall and social desirability bias, they are considered to

have sufficient reliability to predict functional outcomes in

patients with chronic illnesses (George et al. 2007, Jerant

et al. 2008).

Medication type and prescription, dosage and frequency

were determined by asking participants to present all their

medicines. The pharmacy label of all relevant medicines was

then reviewed and the related dosage and frequency recorded

on the checklist. If participants used prefilled multi-dose

packs, then the pharmacist-prepared medication list supplied

with the packs was reviewed to determine the prescription,

with reference made to the contents of the pack during the

interview. The number of the participant’s medicines was

noted and each medicine was reviewed for participant

knowledge, followed by a review of their adherence to the

prescribed regimen one medicine at a time.

Medication knowledge was then determined by asking the

participant to name independently each prescribed medicine,

the main action of that medicine, the dosage, daily frequency,

main side effect and an action they would take should that

side effect be experienced. A score point was given for every

correct answer and then a score was calculated based on the

overall percentage of correct answers.

Medication adherence was determined by asking partici-

pants of the manner in which they took individual medicines.

A score point was given for each medicine taken at the

prescribed dose and daily frequency, so that both intentional

and non-intentional adherence was captured, but not differ-

entiated. A medication adherence score was then calculated

based on the overall percentage of medicines taken as

prescribed for all medication doses and frequencies. Both

medication knowledge and adherence scores therefore had a

potential range of 0–100, with higher scores representing

better knowledge and adherence.

Assistance with taking their medicines was determined by

asking participants to describe any assistance that they

received, from formal and informal caregivers, to manage

their medicines and the form this assistance took. Prompts

were used as necessary. For instance, if there were visible cues

of assistance with medicines such as pharmacist prepared

dosage packs or dosette boxes, these were discussed with the

participant. Additional prompts included whether partici-

pants were provided with support by way of reminders, filling

of dosette boxes, checking stocks, and filling prescriptions by

formal or informal carers. Participant responses were

recorded verbatim.

We measured medication knowledge and adherence, assis-

tance with medicines and number of primary diagnosis-

related medicines using an interview guided by the Self Care

Behaviours Medication Checklist (Colinet et al. 2003, Jerant

et al. 2008). The checklist is completed during an interview

and is used to structure questions and record responses to

specific medicine-related questions.

Partners in health scale

We assessed capacity for self-management of chronic illness

using the original Partners in Health Scale (Battersby et al.

2003). This self-report questionnaire measures generic self-

management capacity in chronic illness using 11 items, which

include understanding of and ability to monitor symptoms

and respond to symptom changes, disease-related knowledge,

sharing in decisions, taking medicines, and scheduling and

attending appointments. Respondents rate their ability on

each item using a nine-point Likert-type scale ranging from

zero (very good) to eight (very poor). Scores are then

combined to give a total ranging from 0 to 88, with higher

scores representing poorer self-management. In this study, the

scale had internal consistency reliability (Cronbach’s Alpha

Coefficient) of 0Æ84.

Self-rated health scale

General health status was assessed using the Self-Rated

Health Scale (Lorig et al. 1996), a self-reported single-item

scale where participants respond using a five-point semantic

differential ranging from one (excellent) to five (poor) to

R Gallagher et al.


indicate their perception of their current health. The scale had

been developed and tested among a variety of chronic illness

populations including COPD, heart disease, arthritis and

stroke (Lorig et al. 1996, 2001a; Lorig et al. 2001b). It is

reported to be reliable with a test-retest correlation coefficient

of 0Æ92 (Lorig et al. 1996).

Data analysis

The data were analysed using the Statistical Package for the

Social Sciences (SPSS) version 17 (Statistical Package for the

Social Sciences Incorporated 2007). Frequencies, percentages,

means, standard deviations (SD) and medians were used to

describe the data. Disease severity was delimited for HF

patients at two categories of NYHA II or III (Goldman et al.

1981) and disease severity scores for COPD were dichoto-

mised around the mean for the analyses. Data on assistance

with medicines were reviewed by two members of the

research team to determine the level of assistance participants

were receiving with their medicines. Responses were cate-

gorised into high and low levels of assistance based on

whether the patient was receiving assistance in some form for

most doses of medicines. A high level of assistance therefore

included responses that indicated that most doses were

dispensed and prepared by a pharmacist or carer using

prefilled multi-dose packs or supervising most doses in some

form. All categorisation of participants was then re-checked

until consensus was reached.

We used multiple linear regression analysis to identify the

factors that were independent predictors of medication

knowledge as medication knowledge was normally distrib-

uted. The predictors of medication adherence, however, were

determined by logistic regression analysis. Logistic regression

was used as medication adherence scores were strongly

skewed and usual transformation techniques were not useful.

Consequently, these scores were dichotomised so that any

score of 100 was categorised as adherent and any score less

than 100 was categorised as non-adherent. The variables

included in both regression analyses were patient-related:

age, gender, self-management capacity (and medication

knowledge for medication adherence); condition-related:

HF or COPD diagnosis, illness severity, self-rated health

and number of comorbid conditions; treatment-related:

number of medicines and participation in disease support

programme; and socioeconomic: ethnicity, marital status,

assistance with medicines. The backwards method of reduc-

tion was chosen to produce the most parsimonious model

whilst accommodating complex and potentially interrelated

variables (Peduzzi et al. 1996, Katz 1999). The p level was set

at 0Æ05 for model predictors.

Results

Sample characteristics

Participants (n = 118) had an average age of 75Æ54 years (SD

8Æ38), with marginally more men (56Æ8%) than women, as

detailed in Table 1. The majority of the sample was married

(59Æ3%), retired (93Æ2%), and Caucasian (87Æ3%). Slightly

less than half of the participants had a primary diagnosis of

HF (42Æ4%) and slightly more than half (56Æ4%) had been

admitted to hospital within the past 12 months. Comorbid

conditions were common, with 73Æ7% having at least two

additional diagnoses. Participants reported their overall

health as fair, with a mean score of 3Æ75 (SD 0Æ94), with a

high capacity for self-management (mean 17Æ86; SD 13Æ81).

There was a high rate of participation in specialty disease

management programmes (72Æ8%).

Medication characteristics

Participants were prescribed an average of 4Æ68 (SD 2Æ11)

medications for their primary diagnosis of either HF or

COPD, with 28Æ8% receiving a high level of assistance with

their medicines. Medication knowledge was low with an

average score of 47Æ61 (SD 18Æ73) out of a potential 100.

Table 1 Sociodemographic, clinical and medication characteristics

(n = 118)

Number (%) or

mean (SD)

Patient and socioeconomic related characteristics

Age (years), mean (SD) 75Æ54 (8Æ38)

Education (years), mean (SD) 10Æ59 (3Æ18)

Gender, male 67 (56Æ8)

Employment status, retired 110 (93Æ2)

Married 70 (59Æ3)

Ethnicity, Caucasian 85 (87Æ3)

Self-management capacity*, mean (SD) 17Æ86 (13Æ81)

Medication knowledge�, mean (SD) 47Æ61 (18Æ73)

High level of medication assistance 34 (28Æ8)

Condition related characteristics

Primary diagnosis, COPD 68 (57Æ6)

More severe disease (NYHA Class III or

above median on SGRQ)

51 (43Æ6)

Number of comorbid conditions, mean (SD) 2Æ48 (1Æ44)

Self-rated health�, mean (SD) 3Æ75 (0Æ94)

Treatment related characteristics

Number of either HF or COPD medicines 4Æ68 (2Æ11)

Participating in specialist disease

management programme

86 (72Æ8)

Potential range *[0 (very good)–88 (very poor)], �[0 (lack of

knowledge)–100 (high level of knowledge)], �[1 (excellent)–5 (poor)].



Majority of participants (75Æ2%) reported that they were

adherent to all medicine doses and frequencies (Table 2),

although a range of scores were evident (0–100).

Predictors of medication adherence and knowledge

The final models, statistics, and predictors produced from the

multiple regression analyses for medication knowledge and

adherence are reported in Table 3. The predictors of medi-

cation knowledge included age, self-management capacity,

number of medicines prescribed, and number of comorbid

conditions, with the final model accounting for 33% of the

variance in medication knowledge. Participants with better

capacity for self-management and more comorbid conditions

reported higher levels of medication knowledge, whereas,

older participants who were taking more medicines reported

lower levels of medication knowledge.

The independent predictors of medication adherence were

patient-related factors, gender and capacity for self-manage-

ment, and condition-related factors, HF diagnosis, which

explained 16% of the variance in medication adherence.

Participants with a HF diagnosis were almost four times more

likely to report being adherent to the prescribed medication

regimen than those with a diagnosis of COPD, whereas, men

were 64% less likely than women to report being fully

adherent. Participants with poorer self-management capacity

were less likely to report full adherence. No treatment-related

or socioeconomic factors were significant predictors of

medication adherence in this study.

Discussion

Study participants reported a low level of medication

knowledge and a high level of medication adherence. Less

than half of the participants were able to name medicines or

outline the action, dosage, frequency, or main side effect of

each medicine. The lowest levels of knowledge were reported

among older patients who were taking more medicines and

who reported less capacity for self-management. The influ-

ence of these factors is not surprising given that older age is

associated with the development of cognitive and functional

impairments (Jowsey et al. 2009) and increased medication

regimen complexity (Restrepo et al. 2008, Murray et al.

2009). The combination of these factors decreases the

capacity of an older person to understand and associate the

names and actions of prescribed medications (Stoehr et al.

2008). Conversely, the finding that participants with more

comorbid conditions had higher levels of medication knowl-

edge was unexpected, as the accumulation of conditions is

often associated with increasing medication regimen com-

plexity. However, a recent emphasis on the effects of

polypharmacy may have potentially influenced providers

and patients. Participants’ increased knowledge may also

have been influenced by the decision to focus on one group of

medicines related to either COPD or HF.

Medication adherence was unexpectedly high given the

presence of multiple factors in the sample that are known to

limit adherence, including older age and thus the potential for

memory impairment, chronic illness, multiple medicines, and

a recent illness exacerbation or hospitalisation (Barr et al.

2002, George et al. 2005, Moser et al. 2005, Osterberg &

Blaschke 2005, Restrepo et al. 2008, Stoehr et al. 2008, Wu

et al. 2008a,b). In fact, adherence levels were at the high end of

the range of previous reports for patients with either HF

(Gonzalez et al. 2004, Van Der Wal et al. 2006, Wu et al.

2008a), or COPD (DiMatteo 2004, George et al. 2007,

Restrepo et al. 2008, Laforest et al. 2010). There are several

likely explanations for this high rate of adherence including

the use of self-report measures, which are prone to the effects

Table 2 Medication adherence frequency

Score category Number (%)

0–24 3 (2Æ9)

25–49 1 (1Æ4)

55–74 8 (7Æ2)

75–99 17 (14Æ3)

100 89 (75Æ2)

Table 3 Model statistics and significant predictors of medication

knowledge and medication adherence

Predictors B� 95% CI§ p-value

Medication knowledge*

Number of medicines �2Æ08 �3Æ39 to �0Æ77 0Æ002

Number of comorbid

conditions

2Æ38 0Æ40 to 4Æ36 0Æ019

Age �0Æ67 �1Æ01 to �0Æ34 <0Æ001

Self-management capacity� �0Æ50 �0Æ70 to �0Æ30 <0Æ001

Model statistics F test = 13Æ54, df = 111, p < 0Æ001, adj R2 = 0Æ15

Medication adherence Odds ratio

Condition-related characteristics

Primary diagnosis, HF 3Æ99 1Æ32 to 12Æ05 0Æ014

Patient-related characteristics

Self-Management capacity� 0Æ94 0Æ90 to 0Æ98 0Æ002

Gender, male 0Æ36 0Æ13 to 0Æ92 0Æ045

Model statistics v2 test = 18Æ67, df = 111, p < 0Æ002, Cox & Snell

R2 = 0Æ16

Potential range *[0 (lack of knowledge)–100 (high level of knowl-

edge)].�[0 (very good)–88 (very poor)].�Unstandardised beta coefficient.§95% Confidence interval for unstandardised beta coefficient.

R Gallagher et al.


of recall and social desirability bias (Jerant et al. 2008), and

the high proportion of participation in disease-specific support

programmes. While chronic illness support programmes are

designed to promote self-management, including understand-

ing and managing related medicines (Williams et al. 2008),

programme participation was not a predictor of adherence in

the analyses. This was an unexpected finding and it may reflect

the processes for invitation to specialty programmes, that is,

patients needing more support may have received more

encouragement to participate. However, these influences

could not be determined in the current study and warrant

further investigation, as do the presence of perceived barriers

shown to be important in Wu et al. (2008b).

There was, however, some support for the relationships

and factors outlined in the MAM (Sabate 2003). Two of the

four factors tested, condition-related and patient-related,

were found to be independently predictive of medication

adherence, whereas, treatment-related and socioeconomic

factors were not. This is in contrast to a previous study which

tested the model among patients with HF, finding that all

except health care system-related factors were predictive of

adherence (Wu et al. 2008a). Consistent with previous

reports (DiMatteo 2004, Lam et al. 2007), one condition-

related factor was an independent predictor, the patients’

primary diagnosis, as patients with HF were much more

likely to be adherent than patients with COPD. The inclusion

of COPD patients may therefore help explain the difference

between these results and those of Wu et al. (2008a);

however, it is not clear why patients with COPD were less

likely to be adherent, as no published evidence was found

that explored these differences.

Evidence suggests that the nature of the respiratory

medication regimen may be one of the potential issues

(Lam et al. 2007). In particular, the need to change the

medication regimen in response to fluctuating symptoms and

acute exacerbations is reported to be problematic (DiMatteo

2004, Lam et al. 2007). Nonetheless, patients with HF face

many of the same barriers (Wu et al. 2008a). In fact, any

medication regimen that requires more patient participation,

rather than following a fixed schedule, increases the difficulty

of achieving full adherence. As this study did not explore

these issues, further investigation is required.

The patient-related factors found to predict medication

adherence included female gender and self-management

capacity. This is interesting as many studies, using multivar-

iate analyses, have not found gender to have an effect on

medication adherence (DiMatteo 2004, George et al. 2005,

Chen et al. 2007, Kim et al. 2007, Wu et al. 2008a) or have

found that women report less adherence (Laforest et al.

2010). There is some evidence, however, to suggest that

women with other chronic conditions such as asthma and

hypertension are more likely to be adherent than their male

counterparts (Lorig et al. 1996, 2001a, Chan et al. 2010).

When this difference has been explored in more depth,

women described more positive attitudes towards taking

medicines than men, which promoted adherence (Lorig et al.

1996).

The effect of self-management capacity for chronic illness

is also interesting as knowledge was not identified as a

predictor of medication adherence in the current study. This

is in contrast with the MAM which identifies both knowledge

and skills as important (Sabate 2003). The effect of medica-

tion knowledge on medication adherence, however, has been

reported to be inconsistent (DiMatteo 2004, Osterberg &

Blaschke 2005, Vestbo et al. 2009), whereas the capacity to

self-manage has consistently been identified as important to

medication adherence (Hope et al. 2004, Osterberg &

Blaschke 2005).

Self-management capacity includes multiple aspects, such

as the ability to monitor and make decisions regarding

symptoms, take prescribed medicines and consult with

health professionals in a timely manner. It is possible that

in this study, self-management capacity subsumed several

other factors previously identified as influencing medication

adherence, including age (Barr et al. 2002, Wu et al.

2008a), illness severity (Williams et al. 2008), social

support (Reid et al. 2006, Chen et al. 2007, Lam et al.

2007, Kuzuya et al. 2008, Wu et al. 2008b), barriers (Wu

et al. 2008a) and assistance with medicines (DiMatteo

2004). This may also potentially explain the lack of

influence of socioeconomic factors, particularly social sup-

port, and condition-related factors such as illness severity,

that were found to be predictors when the MAM has been

tested previously (Wu et al. 2008a). This explanation is

likely as self-management capacity was relatively high in the

study sample, possibly as a result of participation in the

specialty disease management programmes, although such

participation by itself was not predictive of medication

adherence.

Relevance to clinical practice

It is clear that in chronic illnesses, such as HF and COPD,

there is a need to measure the patient’s capacity to self-

manage to ensure that appropriate support can be provided

for medication management. There are a number of instru-

ments available for this purpose (Elliott & Marriott 2009).

Such a measure would ideally incorporate not only a self-

report measure but also an assessment of physical and

cognitive barriers for older patients with chronic illnesses



who are recovering from acute illness exacerbations, as was

the case in the current study.

The study findings also reveal the need to develop and

direct interventions to improve adherence among men,

individuals with COPD and persons with poor capacity for

self-management. Most interventions aimed at improving

medication adherence do so through patient education,

assuming that improving medication knowledge will improve

adherence. It is likely that medication knowledge, whilst

necessary, is not sufficient to influence adherence. The most

effective interventions to improve medication adherence are,

therefore, less likely to be those focussed on patient educa-

tion, and more likely to be those tailored to increasing

patients’ self-management capacity. Such programmes will

need to involve the support of formal and informal carers.

Furthermore, simple strategies designed to improve medica-

tion adherence, such as medication reminders, daily routines

and multi-dose packs have been reported to be effective in

systematic reviews (Lorig et al. 1996) and by patients

themselves (Reid et al. 2006). All of these strategies are

recommended to improve medication adherence in persons

with chronic illnesses.

Limitations

While this study adds to the current literature on medication

knowledge and adherence among patients with HF and

COPD, the findings should be interpreted with caution.

Firstly, these results report medication knowledge and

adherence rates for the primary diagnosis-related medicines

of either COPD or HF and not for medicines being taken by

study participants for other comorbid conditions. In addi-

tion, data on medication knowledge and adherence were

collected using an instrument that does not have established

reliability and validity. Furthermore, the instrument depends

on self-report, which is subject to recall and social

desirability bias, which may have influenced the unusually

high level of reported medication adherence. It can be

reasonably assumed, however, that non-adherence levels

were accurate as self-report of medication adherence has

high specificity (George et al. 2007). Also, adherence scores

did not account for the level of the medicine delivered, or

intentional versus non-intentional adherence in this study.

Furthermore, the participants were a convenience sample

from one hospital site and therefore the results may not be

generalisable. Finally, there are likely to be other factors

that contribute to medication adherence relevant to the

MAM which were not measured by the current study and

hence the explanatory power of the models described was

lower than expected.

Conclusion

Taking medicines as prescribed is a vital component of

managing a chronic illness and avoiding adverse events.

Medication adherence was relatively high in this study, but

not as high in men and patients with COPD, whereas

medication knowledge was poor, particularly among older

adults who were prescribed multiple medicines. Both medi-

cation knowledge and adherence were lower among patient’s

with poorer capacity for self-management, identifying the

importance of assessing self-management capacity following

acute illness exacerbations and providing interventions and

assistance that correspond with patient needs. Interventions

are required to improve medication adherence, including

those which aim to enhance overall self-management capacity

rather than relying solely on increasing medication knowl-

edge. The MAM offers an organising framework for under-

standing the complex and multidimensional factors

associated with medication adherence; however, rigorous

systematic testing is required to determine if the model is

useful for chronic illness in general.

Acknowledgements

We acknowledge the support of Professor Judith Donoghue

for her assistance with the original conceptualisation of the

study.

Contributions

Study design, funding, literature review, data coding and

analysis and management of the study and manuscript

writing process: RG; method, data management and ana-

lyses, and concept development for the literature review and

critical revision of the manuscript: MW; study design,

funding, and concept development for the literature review

and critical revision of the manuscript: LC and JSP; concept

development for the literature review and critical revision of

the manuscript: KMW.

Funding

The study was supported by a New South Wales Nurses and

Midwives Board (Category 5 Grant) and University of

Technology, Sydney Research Strength Grant 2006.

Conflict of interest

No conflict of interest has been declared by the authors.

R Gallagher et al.


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