Improving Medication Compliance In the Geriatric Patients
Submitted by
Eva M Jacques
Direct Practice Improvement Project Proposal
Doctor of Nursing Practice
Grand Canyon University
Phoenix, Arizona
April 29, 2020
© by Eva Mireille Jacques, 2020
All rights reserved.
GRAND CANYON UNIVERSITY
Improving Medication Noncompliance in Geriatric Patients
by
Eva M. Jacques
Has been approved
April 29, 2020
APPROVED:
Tabitha Garbart, DNP, DPI Project Chairperson
Hubert Cantave, MD, Committee Member
ACCEPTED AND SIGNED:
________________________________________Lisa Smith, PhD, RN, CNEDean and Professor, College of Nursing and Health Care Professions
_________________________________________Date
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Abstract
Many geriatric patients suffer from one or more chronic diseases and the management of
those chronic conditions may require one or more prescribed medications. Medication
compliance is essential in the treatment of chronic illness unfortunately; poor compliance
in the geriatric population is extensive. The purpose of this direct practice improvement
project was to identify ways to increase medication compliance in the geriatric
population. This quantitative quasi-experimental comparison project was set out to find
out to what degree a weekly phone call along with the administration of Hill Bone
medication adherence scale (HB-MAS) would increase compliance among noncompliant
geriatric patients over a six-week period. This project was conducted in a private clinic
in Southern United States. The health belief model was utilized to evaluate if a weekly
phone call along with the administration of a HB-MAS would motivate participants to
increase medication compliance over a period of six-weeks. The population was of
geriatric patients’ 65 to 82 years of age. The total sample size was n= 60 of self-admitted
noncompliant patients n=30 in the comparative group and n = 30 in the implementation
group. The data was collected from the HB-MAS The analysis of the data was done
utilizing a chi-square test the results showed that x2(1, N=60.) =0.003. A p-value of 0.03
indicates significant differences exist between the two groups. The results revealed t (98)
=-17.101, p=001 which means statically there was significant improvement in
compliance. It is recommended that future investigator who may want to duplicate this
project utilizes a much larger sample size for a longer period of time also a more diverse
group of participants;
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Keywords: Geriatric; Medication Compliance; Noncompliance.
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DedicationThis project is dedicated to my family who supported me throughout this
endeavor. To my husband, Daniel thank you I love you. I hope I have made you proud.
To my sons Evan and Jordan, I hope my achievement proved to you that you can do and
be anything you want in life if you work hard at it. Noting is impossible. I love you both
immensely.
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Acknowledgments
I would like to acknowledge my mentors, Dr. Hubert Cantave and Dr. Mayre
Urdaneta for their guidance and support towards the completion of this program. To my
dear friend and colleague Guerna Blot who supported, motivated, and pushed me when I
thought it was impossible to go on. Guerna, I thank you from the bottom of my heart. To
my friend Deborah Williams, who went above and beyond to assist me with obtaining
forms and signatures needed allowing continue moving forward in the program, I thank
you from the bottom of my heart and I am forever grateful. I want to thank the
administrators of my current place of employment for their support and allowing me to
accomplish this goal. Thank you very much.
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Table of Contents
List of Tables......................................................................................................................xi
List of Figures....................................................................................................................xii
Chapter 1: Introduction to the Project................................Error! Bookmark not defined.
Background of the Project.............................................................................................4
Problem Statement.........................................................................................................5
Purpose of the Project....................................................................................................7
Clinical Question(s) ....................................................................................................10
Advancing Scientific Knowledge................................................................................11
Significance of the Project...........................................................................................13
Rationale for Methodology..........................................................................................15
Nature of the Project Design........................................................................................17
Definition of Terms......................................................................................................19
Assumptions, Limitations, Delimitations....................................................................21
Summary and Organization of the Remainder of the Project......................................23
Chapter 2: Literature Review.............................................................................................25
Theoretical Foundations...............................................................................................28
Review of the Literature..............................................................................................33
Theme 1. You may want to organize this section by themes and subthemes.
To do so, use the pattern below..................................Error! Bookmark not defined.
Theme 2. Chapter 2 can be particularly challenging with regard to APA
format for citations and quotations. Refer to your APA manual frequently to make
sure your citations are formatted properly. It is critical that each in-text citation is
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appropriately listed in the References section............Error! Bookmark not defined.
Summary......................................................................................................................43
Chapter 3: Methodology....................................................................................................47
Statement of the Problem.............................................................................................48
Clinical Question.........................................................................................................49
Project Methodology....................................................................................................52
Project Design..............................................................................................................53
Population and Sample Selection.................................................................................55
Instrumentation or Sources of Data.............................................................................59
Validity........................................................................................................................60
Reliability.....................................................................................................................61
Data Collection Procedures..........................................................................................62
Data Analysis Procedures............................................................................................65
Ethical Considerations.................................................................................................68
Limitations...................................................................................................................70
Summary......................................................................................................................72
Chapter 4: Data Analysis and Results................................................................................74
Descriptive Data...........................................................................................................75
Data Analysis Procedures............................................................................................77
Results..........................................................................................................................78
Summary......................................................................................................................82
Chapter 5: Summary, Conclusions, and Recommendations..............................................84
Summary of the Project...............................................................................................85
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Summary of Findings and Conclusion.........................................................................86
Implications..................................................................................................................90
Theoretical implications.................................................................................90
Practical implications.....................................................................................91
Future implications........................................................................................91
Recommendations........................................................................................................93
Recommendations for future projects............................................................93
Recommendations for practice......................................................................95
Appendix A........................................................................Error! Bookmark not defined.
Appendix B..........................................................................................................................1
xi
List of Tables
Table 1. Correct Formatting...............................................Error! Bookmark not defined.
Table 2. t-Test....................................................................................................................77
Table 3. The Servant Leader..............................................Error! Bookmark not defined.
(Note: single-space table titles; double-space between entries)
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List of Figures
Figure 1. Scatterplot Example-Strong Negative Correlation ..........Error! Bookmark not
defined.
(Note: single-space figure titles; double-space between entries)
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Chapter 1: Introduction to the Project
Medication adherence is essential in the treatment of chronic diseases. Medication
adherence occurs when the patient takes the medication as prescribed (Smith et al., 2017).
Non-adherence in the management of chronic conditions is a major concern because
continuous treatment is essential for effective disease management. Raghupathi and
Raghupathi (2018) defined chronic condition as a physical or mental health condition
lasting more than one year and causing functional restrictions or requiring ongoing
monitoring or treatment. Lack of compliance with a medication regimen can lead to
worsening of symptoms and may lead to new complications. Treatment efficacy depends
on the patient’s compliance. Effective management of chronic comorbid conditions often
involves complex medication regimens, requiring different tablet combinations and
multiple daily dosing that can lead to a high rate of noncompliance to medication
regimens (Smith et al., 2017).
Aging is a strong risk factor for many chronic diseases (Pagès-Puigdemont et al.,
2016). According to the Global Health and Aging report presented by the World Health
Organization (WHO), “The number of people aged 65 or older is projected to grow from
an estimated 524 million in 2010 to nearly 1.5 billion in 2050, with most of the increase
in developing countries (Pagès-Puigdemont et al., 2016). America’s current
demographics indicate 10,000 Americans will turn 65 each day from now through the end
of 2029 (Raghupathi & Raghupathi, 2018). Hence, in the United States, the number of
people 65 years or older is expected to significantly increase. Therefore, the overall
number of patients with multiple diseases may significantly increase, and some patients
may be taking one or more medications to manage multiple chronic conditions.
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Multiple medications increase the likelihood of poor adherence among geriatric
patients. Mcmullen et al. (2014) reported that more than half of American adults take at
least one prescription drug, and one out of 10 take five or more. Qato et al. (2016) noted a
higher prevalence and used a representative sample of 2,206 adults aged 62 through 85
years of age. Their study showed that 87% of geriatric patients used at least one
prescription medication. About 36% of geriatric patients used at least five prescription
medications, while 38% used over-the-counter medication. Patients who take medications
inappropriately can face serious side effects, even including fatality. Medication
noncompliance is a major health problem; it accounts for 10% of all hospital stays and
causes approximately 125,000 deaths each year (Mayo & Mouton, 2017).
Several studies have shown that lack of adherence among the older adult
population represents a significant problem and has led to increased morbidity, mortality,
and healthcare cost (Jin, Kim, & Rhie, 2016; Marcucci et al., 2010; Yap, Thirumoorthy,
& Kwan, 2016). Researchers have identified improving adherence to medication as one
of the most cost-effective and achievable opportunities for improving health outcomes
(Nguyen, La Caze, & Cottrell, 2016). There is a need to recognize factors related to
nonadherence to medication, as providers and clinicians can then use findings to
strategize and formulate individual interventions that can increase compliance, thereby
improving patient outcomes( Karakurt, & Kaşikçi, 2012).
The Direct Practice Improvement (DPI) PICOT question is the following: With
geriatric (65 years to 82 years) patients with chronic illnesses in the Southeastern United
States who are noncompliant with their medication regimen to what degree does
implementation of a weekly phone call along with the administration of a Hill-Broon
Medication Adherence Scale (MAS) improve compliance compared to those who do not
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participate for 6 weeks?. The MAS was used to measure compliance. The information
obtained from this scale was used to counsel patients regarding the importance of
medication adherence. This tool was developed, in part, as a response to earlier
instruments, such as the Medication Adherence Questionnaire (MAQ) by Morisky,
Green, and Levine (1986). Researchers used the MAQ to measure medication adherence
for hypertension treatment and psychometric properties. The MAQ scale appeared
adequate in Morisky et al.’s (1986) study; as the researchers measured patients’ self-
reported compliance. Toll et al. (2007) posited that researchers could use the MAQ to
help health practitioners address the side effects of mediation proactively, thus addressing
medical challenges by the geriatric population.
The organization of this chapter is in various sections. First, the background of the
project shows both the history and the problem. The problem is discussed as well as to
the problem statement and the significance of the project. The selection of the
methodology is presented, along with the nature of the project design, definitions, and
limitations of the project.
Background of the Project
The geriatric population is prone to chronic illnesses, such as hypertension,
diabetes, arthritis, neurodegenerative, gastrointestinal, ocular, genitourinary, and
respiratory disorders, which may require chronic medication with multiple drugs. Poor
compliance in this age group is common (Patton, Hughes, Cadogan, & Ryan, 2017).
Failure to follow prescription medication can be costly to both the patient and the
healthcare system. Many geriatric patients have chronic conditions, such as the diseases
mentioned, which are poorly controlled due to noncompliance.
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Noncompliance with the medication regimen is a major health problem,
especially in the geriatric population (Mayo & Mouton, 2017). Non-adherence to
prescribed medication does not only threaten patient health but also contributes to the
increasing costs of health care in the United States. Noncompliance is a major cause of
disease exacerbation and treatment failure. According to Cutler et al. (2018), annual
costing of medication non-adherence ranges from $100 to $290 billion in the United
States; hence, researchers should consider medication compliance as crucial in the
geriatric population.
Currently, increases have occurred in geriatric patients arriving at their primary
care providers with extremely elevated blood pressures and blood glucose levels.
Consequently, these issues have led to an increase in patients using healthcare services,
such as urgent care centers and emergency rooms. Thus, finding effective ways to
increase medication compliance among the geriatric population is essential in improving
their quality of life.
Problem Statement
It is not known if or to what extent supplying a weekly phone call, and the
administration of MAS can increase medication compliance among the geriatric patients.
Those who find a way to increase compliance through communication, education, and
encouragement may reduce hospitalization rate and thereby improve their quality of life
(Jin et al. 2016). Compared to young adults, the healthcare needs of the geriatric patients
are diverse and complex due to comorbidities and the need for multiple medications, as
described by Lam and Fresco (2015). Clinicians may use the findings of this project to
assist geriatric patients in the clinic with increasing compliance with their prescribed
medications and increase awareness of their chronic disease processes.
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A quantitative study of more than 75,000 commercially insured patients showed
that 30% failed to fill a new prescription, and new prescriptions for chronic conditions,
such as high blood pressure, diabetes, and high cholesterol, were not filled 20% to 30%
of the time (Miller, 2016). According to the study African Americans, aged 65 years and
older taking an average of 5.7 medications, could not identify the purpose of at least one
of their medications over 56% of the time. The results of this multivariate analysis
showed that copayment for drugs, memory deficits, Medication Regimen Complexity
Index (MRCI), and medication-related knowledge were all associated with adherence to
the dosage regimen of medications. Miller (2016) found that participants with a higher
level of knowledge about therapeutic purpose and knowledge about the dosage regimen
of their medications were seven times (Confidence Interval: 4.2–10.8) more likely to
adhere to frequency and dose of medications. Conversely, participants with a low
complexity index were two times (Confidence Interval: 1.1–3.9) more likely to adhere to
the dosage regimen of their medications, compared with participants with a high drug
regimen complexity index.
Non-adherence to a medication regimen is complex and it will take the
collaboration of providers and patients to formulate individualized plans to arrive at
compliance. The road to compliance starts with a multidimensional and multidisciplinary
approach, and providers play a pivotal role in encouraging their patients to be compliant
by utilizing evidence-based practice strategies tailored to improving compliance. From
the literature reviewed, noncompliance in the geriatric population is a major health
problem. Lack of adherence causes nearly 125,000 deaths and 10% of hospitalizations
while costing the already strained healthcare system between 100 to 289 billion dollars a
year (Mayo & Mouton 2017).
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Purpose of the Project
The purpose of this quantitative direct practice improvement project was to
evaluate if an intervention, such as a weekly phone call with the administration of a
MAS, can increase medication compliance in the geriatric patient population. The project
compared compliance between two groups of participants. Those that are receiving a
weekly phone call and answer the questions of the MAS versus participants that do not
receive a phone call. The goal of this project was to increase medication compliance in
the geriatric population who are seen at a private clinic located in the Southeastern United
States. This project intended to provide a way to aid providers with assisting the geriatric
patient with improving compliance with their medication regimen. Such compliance can
be clinically beneficial, given the complexity of managing geriatric patients with chronic
conditions.
The focus of the project was to provide healthcare providers with an opportunity
to help geriatric patients with medication compliance. According to Huang et al. (2013),
mobile phone technology using text messages has been shown to be useful to improve
adherence rates. However, previous studies reported that participants prefer interventions
that not only act as a reminder but also allows them to enquire about their illness or
simply to communicate with their providers. Thus, practitioners must see every patient
interaction as an opportunity to educate patients about their disease process and
encourage compliance with the medication regimen. The independent variable was the
implementation of a weekly phone call and the completion of a MAS. The weekly phone
call not only serves as a reminder for patients to take their medications but also
encourages them and answers any questions they may have at that time about their
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disease process and their medications. The dependent variable was the degree of
compliance, as indicated by data analysis from the MAS and the comparison of pre- and
post-intervention and clinical values.
Clinical Question(s)
Looking for strategies to improve process aiming at improving compliance among
the geriatric population is crucial. Healthcare providers must strategize to find ways of
improving processes to increase compliance among the geriatric population. Thus,
identifying strategies that can influence geriatric patients at being compliant will
significantly decrease the rate of negative outcomes related to poor compliance in the
geriatric population.
The PICOT question to be answered: With geriatric patients with chronic
illnesses who are noncompliant with their medication regimen, how does the
implementation of a weekly phone call and the administration of an HB-MAS improve
compliance comparing to those who do not participate over a period of 6 weeks? The
specific clinical questions are as follows:
For this project, the quantitative correlational method was used to answer the
following questions:
Q1: To what degree does the implementation of an HB-MAS via weekly phone
call increase medication compliance among geriatric patients with chronic
diseases?
Q2: What is the relationship between the patients who are participating in the
weekly MAS and the patients who are not participating?
The clinical question determined if there is a relationship between the weekly
phone call and increase in medication compliance in geriatric patients between the age of
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65 to 82 years old who suffers from at least one chronic condition. The weekly phone
call was the independent variable, while the increased compliance rate with medication
regimen among geriatric patients was the dependent variable.
Advancing Scientific Knowledge
Patients who increase compliance with a medication regimen can prevent
undesirable health outcomes. Geriatric patients must learn the importance of taking their
prescribed medications as ordered. Therefore, healthcare providers must take every
opportunity to explain the potential untoward effects of noncompliance to geriatric
patients. Many factors may be associated with patients’ noncompliance; therefore,
providers should investigate the reason for noncompliance to be able to successfully
intervene.
Originally, Becker (1974) used the health belief model (HBM) to demonstrate the
relationship between health beliefs and health behaviors, assuming that preventive
behaviors depend on the individual’s beliefs. Modern researchers have used the model to
investigate various health issues (Luquis & Kensinger, 2019; Mirhoseni, Mazloomy, &
Moqaddasi Amiri, 2019). Mirhoseni et al. (2019) used the HBM to study blood pressure
in Yazd, while Luquis and Kensinger (2019) used the HBM to study prevention services
that leadership used to help young adults. Others have used the HBM in different studies.
Researchers have used the HBM to investigate behavioral changes and disease
prevention in the geriatric (Baktash & Naji, 2019; Yazdanpanah, Saleh Moghadam,
Mazlom, Haji Ali Beigloo, & Mohajer, 2019). Baktash and Naji (2019) used the HBM to
encourage more exercise behavior among geriatric home residents to prevent stroke.
Yazdanpanah et al. (2019) used the HBM to study elderly patients’ medication adherence
to develop strategies to encourage more use of medications among this population. For
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this project, this model’s constructs were used to identify barriers to compliance and
provide an understanding of the lack of compliance with geriatric patients. The acquired
knowledge will be beneficial and necessary to formulate plans for interventions, thereby
improving outcomes.
Significance of the Project
Multiple studies on medication adherence since the ’60s have focused on quality
improvement initiatives that placed more emphasis on practice routine, care
recommendations, and guidelines. For instance, clinicians can ensure that patients with
chronic conditions receive their prescribed medications to demonstrate improvement in
health outcomes (BrarPrayaga et al., 2018). However, clinicians should focus on ensuring
that the patients take their prescribed medications as ordered for positive treatment
outcomes. Previous research has shown that adherence to medications is related to reduce
the risk of a poor outcome by 26% (Toll et al., 2007). Thus, clinicians should ensure their
patients with chronic conditions consistently take their prescribed medications to prevent
the progression of the disease.
The independent variable was the weekly phone call with an HB-MAS. The
weekly call not only reminded patients to take their medications as prescribed but l also
encouraged them to ask questions about their medications. The dependent variable was
the degree of weekly compliance, which was measured using the an HB-MAS.
The goal of the project was to increase medication compliance with the geriatric
population and provide healthcare providers with an evidence-based opportunity to help
geriatric patients with medication compliance. The result of this project can make a
significant impact on the individual patient as clinicians can use findings to improve
patient outcomes. According to Jin et al. (2016), being compliant with the medication
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regimen can contribute to the alleviation of symptoms, reduction of morbidity and
mortality rates, reduction of risk of side effects, and reduction of the burden on health
care costs. This project is significant to the healthcare facility because of the population
served, are older adults. The investigator emphasized the importance of adherence to
medication and revealed ways through which adherence can be improved among geriatric
patients. The findings translated into decreased emergency room visits, office visits, and
hospitalizations.
Rationale for Methodology
A quantitative correlational design was selected for this project. This method was
used to determine if the implementation of weekly phone calls and the administration of a
MAS would increase medication compliance among geriatric patients. Several
participants were selected through convenience sampling. They were patients in private
clinics who self-admit noncompliance. The inclusion criteria were of patients who are 65
to 82 years of age, have at least one chronic disease, do not exhibit any cognitive
impairment, and have access to a phone. The exclusion criteria were of patients who had
no chronic disease, cognitively impaired, or have no access to a phone. The selected
participating patients were divided into two groups of 30. One group received an HB-
MAS weekly when called to determine compliance and the other group did not. The
investigator analyzed the results of the MAS to have a better understanding of the factors
involved in noncompliance.
A quantitative method was used for the process of collecting, analyzing,
interpreting, and writing the results of this study (see Lamiani, Borghi, & Argentero,
2017). Quantitative researchers emphasize objective measurements, statistical,
mathematical, or numerical analysis of data collected through polls, questionnaires, and
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surveys, or by manipulating pre-existing statistical data using computational techniques
(Bryman, 2017). For this project, the degrees of noncompliant patients were identified
from the responses to the questions from MAS. The MAS test included five questions.
For each question, there is a scale of 1 to 4. The highest point a patient can earn is 4. The
quantitative method involves measurement, and a quantitative researcher assumes that the
phenomena under study can be measured. Quantitative investigations further set out to
analyze data for trends and relationships to verify the measurements made (Watson,
2015) through comparisons of the results between those adhering and non-adhering
individuals. The primary objective of this project was to determine if an intervention,
such as a weekly phone call and the administration of an HB-MAS, could increase
compliance in the geriatric patient population by comparing the HB-MAS for each group.
Nature of the Project Design
A correlational design was utilized with a focus on finding if interventions, such
as a weekly phone call, aids in increasing medication compliance and, thereby, improves
outcomes for the geriatric patient. This design was used to determine if there was a
relationship between the administration of weekly HB-MAS and medication compliance.
This design was also selected because it is widely used for testing the relationship among
variables. With the correlational design, the investigator can determine if there is a
relationship (see Mitchell, 1985) between noncompliance and intervention, such as a
weekly phone call.
The investigator used the quantitative correlational relationship between the data
and the variables to predict the existence of a relationship. With a project such as
medication noncompliance in the geriatric population, the probability value (p-value) was
used. A p-value of less than 0.05 indicated that significant differences exist between the
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two groups: the participants of the weekly program and the nonparticipants. The analysis
of variance (ANOVA) was used, as described by Gorder and Foreman (2014), to
determine if the implementation of a weekly phone call will improve compliance among
the geriatric population.
Definition of Terms
For this DPI project, terms, variables, concepts, and phenomena were used. The
following terms and phrases were operationally used in this project:
Assumptions. Assumptions refer to something that is taken as certain or true to
happen without any proof (Simon & Goes, 2013).
Delimitations. Delimitations refer to limitations consciously set by the authors
themselves. They are concerned with the definitions that the researchers decided to set as
the boundaries or limits of their work so that the study’s aims and objectives do not
become impossible to achieve. (Theofanidis & Fountouki, 2018).
Dependent variables. Dependent variables refer to the variable of interest to the
researcher (Kaur, 2013).
Geriatric patients. These patients are 65 years of age or older (Rocque et al.,
2017).
Health literacy. Health literacy refers to the degree to which an individual can
obtain, communicate, process, and understand basic health information and services to
make proper health decisions (Rasu, Bawa, uminski, Snella, & Warady, 2015).
Independent variables. This variable is believed to affect the dependent variable
(Kaur, 2013).
Limitations. Limitations refer to any particular concern or potential weaknesses
of the project (Theofanidis & Fountouki, 2018).
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Medication adherence. Medication adherence is defined as the extent to which a
person's behavior agrees with the agreed medication regimen from a health care provider
(Yap et al., 2016).
Medication Adherence Scale (MAS). MAS is an instrument that provides a
simple method for clinicians in various settings to assess patients' self-reported
compliance levels and to plan appropriate interventions (Kim, Hill, Bone, & Levine,
2000).
Medication compliance. This compliance refers to the extent to which patients
take medication as prescribed by their healthcare professionals (Verloo, Chiolero, Kiszio,
Kampel, & Santschi, 2017). Compliance was evaluated through the utilization of a MAS.
Medication noncompliance. Medication noncompliance is when medications are
not taken as prescribed (Jimmy & Jose, 2011).
Polypharmacy. Polypharmacy is characterized as the use of multiple medications
for the treatment of a single or several coexisting diseases (Bazargan et al., 2017).
Relationship Status. Relationship status refers to an individual's connection with
a significant other (Alsabbagh et al., 2014).
Socioeconomic status. Socioeconomic status (SES) is a multidimensional
construct representing an individual’s position relative to other people in the community
(Alsabbagh et al., 2014).
Variables. Variables are comprised of anything that has quality or quantity that
varies in the research study (Kaur, 2013). Two types of variables are used for this project:
the dependent variable and the independent variable.
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Assumptions, Limitations, Delimitations
Assumptions. The patients participating in the project were contacted weekly,
and a MAS was administered with every patient contact. It was assumed that the
participants will answer the MAS truthfully. It was assumed that patients with one
chronic disease will be more compliant than patients with multiple chronic conditions. It
is also assumed that patients who live alone will be less compliant than patients who live
with spouses, family members, or others.
Limitations. The limitation of the project was that it was conducted at a center
that caring for patients of a specific culture and ethnicity. Most patients are African
American and are most are of Caribbean descent. The patients’ cultures may influence
their compliance with the treatment regimen. As stated by Bazargan et al. (2017), racial
differences in adherence to prescribed medication regimens among minority older adults
have been previously reported in several studies. It is suggested that factors that change
minority patients’ medication-taking practices must be re-examined. Another limitation,
due to the population served at the clinic, is that there were more women than men who
participated participate in the project.
Delimitations. This project only focused on two research questions, which
include the following: (a) To what degree does the implementation of an HB-MAS via
weekly phone call increase medication compliance among the geriatric patient, and (b)
what is the relationship between the patients that are participating in the weekly an HB-
MAS and the patients that are not participating? This project was delimited to measuring
the level of compliance variable among geriatric patients. The data collected with the
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implementation of the MAS and the weekly phone call was based on the guidelines set
for this project.
Summary and Organization of the Remainder of the Project
Nonadherence to prescribed medication is of major concern for the geriatric
population (Yap et al., 2016). Noncompliance with medication regimen may lead to
negative outcomes. Medication compliance can promote health, decrease cost, and, in
turn, increase life expectancy. Knowing the causes of noncompliance with the geriatric
population is a crucial step to understanding the issue of noncompliance. Many patients
are noncompliant with their medication regimen due to ignorance of the effects of non-
adherence. The HBM is an ideal explanatory framework to address the issue of
noncompliance, as based on past researchers’ successful use of the model (see Baktash &
Naji, 2019; Becker, 1974; Luquis & Kensinger, 2019; Mirhoseni et al., 2019;
Yazdanpanah et al., 2019).
Numerous tools are being used to measure medication noncompliance, one of
which is the MAS. Although the MAS has been shown as helpful when dealing with the
issue of noncompliance, health care providers should produce guidelines to define
adherence procedures (Bercier & Maynard, 2015). Chapter two discusses an extensive
literature review on the previous body of works regarding the issue of noncompliance in
the geriatric population. The theoretical foundation of the project is presented in this
chapter, with emphasis on different learning behavioral and cognitive theories aiming at
increasing medication compliance. The focus in chapter two will also include evidence-
based practices and synthesis of the literature review.
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Chapter 2: Literature Review
The number of people in the United States aged 65 years and over in 2010 was 40
million and; is projected to rise to 88 million by 2050 (O’quin et al., 2015). Persistence in
medication adherence, especially among chronically ill seniors, is recognized globally as
a public health problem (Costa et al., 2015). Failure of chronically ill patients to adhere to
medication routines can worsen the symptoms and result in new complications.
According to Smith et al. (2017), medication adherence is abiding by the prescription
given for taking medication. However, Bazergan et al. (2017) stated that medication non-
adherence could occur in different ways, such as not filling the prescription, not taking
medication, missing doses, taking the wrong amount, taking medication at the wrong
time of day, not taking it as prescribed (e.g., with or without food), purposefully
discontinuing it for a period, or stopping it altogether.
Mcmullen et al. (2014) stated more than 50% of American adults use at least one
prescription drug; 10% take five or more of the prescribed drugs. Having multiple
prescription drugs that a patient is expected to take regularly can be burdensome, hence
making adherence difficult (Anglada-Martinez et al., 2015). Indeed, Anglada‐Martinez et
al. (2015), established that 50% to 60% of patients with chronic illness have a problem
with medication adherence. In as much as noncompliance is high among the chronically
ill, the effects are detrimental, resulting in 10% of all hospital stay and 125, 000 deaths
annually (Mayo & Mouton, 2017). The cost of hospitalization due to non-adherence is
$100 billion annually (Prayaga et al., 2018). Cutler et al. (2018) provided similar
findings, stating that the yearly cost of medication non-adherence in the United States
ranges from $100 to $290 billion.
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To curb the negative effects of non-adherence among the geriatric population
more prone to multiple chronic illnesses, researchers should take advantage of mobile
technology. According to Prayaga et al. (2018), 70% of chronically ill senior citizens
believe that electronically requesting prescription refills is important. Due to the overall
increase of senior citizens with multiple chronic illnesses managed using different
medications (Verloo et al., 2017), there is a need to assess this alarming problem of
nonadherence among the geriatric population.
A systematic literature review was conducted through multiple online literature
sites using ProQuest, MEDLINE, PubMed, Cumulative Index of Nursing and Allied
Health Literature, Excerpta Medica Database, and PsycINFO to identify credible sources
for review. The search terms used included non-adherence, noncompliance, telehealth,
geriatric population, chronically ill, and phone call, and medication adherence scale. The
Boolean strategy was used, and some of the search terms were interchanged with their
synonyms to get more refined results. Additionally, limiters (including limiting articles to
those published within the last 5 years and only to peer-reviewed articles) were used to
ensure that the selected sources were up-to-date credible and meet criteria.
Medication adherence is highly important for any patient population; however,
the geriatric patient population requires a lot more attention regarding this subject matter.
According to Rubin (2019), nonadherence to medication can account for up to 50% of
failures in treatment in the United States. Additionally, it accounts for up to 25% of
hospitalizations in the same country. For this reason, 80% or more adherence patients are
required for optimal therapeutic efficacy. The older adult patient population is very
sensitive, considering that most have chronic conditions and are taking three or more
medications.
18
Medication adherence begins when the patients follow the recommendations
made by the healthcare provider (Frances, Thirumoorthy, & Kwan, 2016). Medication
adherence increases the chances of being treated appropriately, thus improving the state
of health of patients. Disease management takes precedence because the mortality rate is
decreased when medication adherence is achieved.
Healthcare providers must make it a priority to address compliance with their
geriatric patients to aid in decreasing complications (Frances et al., 2016). Various factors
have been identified to affect medication compliance in the geriatric, ranging from health
illiteracy, socioeconomic factors, cognitive illness, healthcare providers, and healthcare
systems. With the background identified, the goal of this DPI project was to improve
medication compliance and adherence in the geriatric population.
This chapter includes a discussion of the relevant literature based on themes
including the theoretical foundation. Next, statistics on non-adherence among chronically
ill geriatric patients are provided about the negative impacts. The theme of reasons for
non-adherence is then reviewed, followed using mobile phones in enhancing adherence.
Finally, the investigator presents the gaps in the literature that made it necessary to
conduct the current project.
Theoretical Foundations
19
Researchers and clinicians should think of health beyond it being only an issue of
the patient to face this challenging problem of non-adherence among geriatric patients
with chronic illnesses. One should also consider how it affects health practitioners,
government, family, and friends of the patient and the entire community (Siddiqui et al.,
2017). Non-adherence influences the entire healthcare system and the community in
general; hence, it should be handled holistically. Several nursing theories were
considered for this project, including self-care theory, the chronic care model (CCM), the
e-health enhanced chronic care model (eCCM), and the HBM; those models have been
applied in enhancing self-care and the overall health of chronically ill patients (Kwan,
2012; Sultan, 2016). The self-care model was unsuitable because researchers of the
model tended to presume that the patient was solely responsible for his or her self-care
and, therefore, unsuitable to the study. Given that the primary population for this project
is the geriatric patients who may have reduced functions due to their advanced ages, this
theory is rejected. Although the CCM and eCCM have some significant concepts that
would be relevant to the project, researchers of those models did not address the behavior
prediction. Thus, the HBM, which has been shown as a valid model for predicting health
behavior (C. L. Jones et al., 2016; Willis, 2018), will be the selected theory to be applied
for the project.
Researchers of the HBM have postulated that people take action to avert illness
(C. L. Jones et al., 2016; Willis, 2018)
1. If they believe that they are individually susceptible to a given condition
(apparent predisposition);
2. If they consider it to portend profound consequences (apparent severity);
20
3. If they believe that a certain course of action is at their disposal to help them
reduce the vulnerability, the severity, or result in other positive results
(apparent benefits); and
4. In case they believe that certain negative attributes are associated with the
course of action taken (apparent barriers).
Scholars have suggested that self-efficacy—the confidence and conviction that an
individual can successfully finish the behavior or action of interest regardless of the
considered obstacles—should be included as part of the model (Jones et al., 2016).
However, few studies utilizing HBM as a theoretical foundation have included self-
efficacy. Although it has been less examined, those who use the framework may posit
that certain cues (e.g., certain factors in an individual’s environment) can influence the
eventual course of action that a person takes. These cues to action can be either external
or internal and include factors, such as experiencing the symptoms of an illness and being
exposed to information related to medication and drugs for the said illness. Similar to
self-efficacy, the proposed cues to action have also been rarely investigated, especially
given the transitory nature (Jones et al., 2015).
Researchers have examined the viability of the HBM and its concepts regarding
behavior prediction; however, the findings of these studies have not been consistent
(Jones et al., 2016). The initial study analyzing the viability of HBM was carried out in
1974, and it focused primarily on assessing significant statistical associations instead of
looking at the impact of sizes (as cited in Jones et al., 2016). Jones et al. (2016)
established significant empirical support for HBM, with results from prospective studies
being almost as important as those from retrospective studies. Jones et al. found that
supposed barriers were the most significant single predictor, and supposed severity was
21
the least significant predictor of preemptive health behavior across all studies and
behaviors. Similarly, both supposed benefits and vulnerability were powerful predictors
of preventive health behavior; nevertheless, the perceived vulnerability was a stronger
predictor of protective health behavior.
On the contrary, other meta-analysis showed that the effect of each HBM
constructs on behavior was somewhat small (Carpenter, 2016). Nevertheless, these
studies were critiqued for not correcting the estimates of impact sizes of the unequal split
in behavioral result metrics, as well as the HBM construct measures. Regarding the
framework’s general effect, studies focusing on the predictive significance of the model
in its totality showed that HBM could indicate predictions of future behavior, although
somewhat weakly when compared to other health behavior theories (as cited in
Carpenter, 2016). However, most recent studies had shown that barriers and benefits are
consistently the most significant predictors. Overall, within Carpenter’s (2016) analysis,
the estimates were somewhat low for the associations between the estimates of how stark
a certain negative health finding would be for a subject and the possibility of the subject
adopting a given behavior. Furthermore, the association between vulnerability behavior
and beliefs was close to 0.
Carpenter (2016), Griffin (2017), and Patton et al. (2017) showed a conflict
occurred within the health belief literature. For example, health benefit constructs seemed
differentially linked to behavior, an outcome suggestive of a fundamental hierarchy for
the variables in the framework. Not only does this inhibit the progress of research, but it
also may explain the inconsistencies in the various reviews (Carpenter, 2016; Griffin,
2017; Patton et al., 2017). Regrettably, in many individual types of research, variable
ordering is not assessed, since HBM constructs tend to be examined considering their
22
additive effect on a result variable. For instance, various studies have shown that the
HBM constructs of perceived barrier, severity, and benefits were mutually powerful
predictors of medication acceptance. Other studies have also shown that perceived
benefits, vulnerability, barriers, severity, and self-efficiency were predictors of drug
adherence (Chao et al., 2016; Holmes et al., 2016).
Review of the Literature
The prevalence of non-adherence is shockingly high. The foundation of the
evidence for this project begins with a series of quantitative research literature reviews
and meta-analyses aiming at supporting the PICOT question relating to non-adherence
with the geriatric population. For instance, a meta-analysis conducted by Lemestra et al.
(2018) determined that only 29% of patients who have been hospitalized following a
heart attack fill their statin medication within 90 days as required. A quantitative study by
Miller (2016) that used a large cross-sectional sample of 75, 000 found that 30% of
patients did not refill their new prescription. This can be contrasted with findings from
Lee et al. (2018) that revealed 6% of senior patients had not adhered to their medication
for the last year.
Among non-institutionalized seniors, drug non-adherence ranges between 10 to
40%, resulting in a 10 percent increase in hospital admissions and 125,000 deaths. Non-
adherence complicates treatment and management of chronic disease (Nguyen, La Caze,
& Cottrell, 2016). Some patients may also be exposed to other health challenges if they
23
do not follow the prescription instructions. For instance, patients with HIV/Aids may be
at risk of contracting opportunistic infections.
Non-adherence to medication is no doubt a worldwide issue that should be
addressed immediately due to its detrimental effects such as increased cost of care,
increased comorbid diseases, worsening conditions, and even death (Chisholm-Burns &
Spivey 2012). According to Lemstra, Nwankwo, Bird, and Moraros (2018), in the United
States, non-adherence causes the country ~$290 billion (USD) yearly. Cutler et al. (2018)
recorded similar findings have been recorded, whereas Prayaga et al. (2018) recorded a
slightly lower cost of not less than $100 billion. In Canada, the cost of no-adherence is as
high as 1.6 billion Canadian dollars. Patients face the high cost of healthcare increasingly
each day. Chung, Marottoli, Cooney, and Rhee (2019) established that in 2017, 6.8% of
old adults (above 65 years) reported that they experienced the impact of non-adherence to
medication through the increasing cost of medications.
To combat non-adherence, collaboration from all stakeholders is necessary.
Families play an important role in providing support to clients when it comes to
medication adherence. Also, clients and family members expressed concern with
medication burden, which appeared to affect their support and adherence to medication.
Nevertheless, families considered medication to be an important component of treatment,
particularly because of the knowledge they gained from the intervention regarding the
illness (Balkrishnan 2005; Brown & Bussell 2011). It was found that families provide
ample support to patients when it comes to medication and believe it is important to
achieve health.
For instance, the probability of senior citizens who have no marital partner to
succumb to medication non-adherence is 56.7%, whereas the likelihood for patients who
24
have spouses is 47.8%. This implies that when there is a person who is close and shows
concern, then a patient is better able to adhere to their medication as opposed to when
they are all alone. El-Mallakh and Findlay (2015) stated that the support team should
offer all the necessary assistance for patients with neurological diseases such as
schizophrenia. Bolkan et al. (2013) conducted a longitudinal survey of 716 veterans and
found that due to family support and involvement, 71% of the veterans adhere to their
medication regiment—supporting the notion that those patients surrounded by the family
have better medication compliance.
Multiple reasons have been reported as barriers to noncompliance. The analysis
and synthesis of the literature review will continue based on two major themes: reasons
for noncompliance and emotional and physical fatigue as it relates to noncompliance.
Three subthemes will be associated with each of the themes. The three subthemes
identified as barriers to adherence are the high cost of medication, emotional and physical
fatigue, and communication. For Theme 2, utilization of mobile technology to enhance
adherence to the subthemes are accessibility and smartphone use to increase adherence to
medical prescriptions.
Barriers to adherence.
Poor adherence to medication is multifaceted. Understanding the reason for non-
adherence can aid in interventional strategies. Many aspects have been identified as a
cause for non-adherence to medication regimen such as emotional and physical fatigue,
high cost of medication, communication, demography, sociocultural, and behavioral,
among others.
The high cost of medication.
25
Piette et al. (2011) described one of the major reasons for noncompliance is the
high cost of medication. Piette et al. stated that 2.7 million senior citizens accounted for
their non-adherence to medication as being cost-related. Lee et al. (2018) found that
medication un-affordability relates to non-adherence by (β = 0.55; standard error, 0.01; p
< .001). Most people did not refill their medications because of elevated costs. Senior
citizens are likely to be retired and have reduced functional skills; hence, they cannot
work as much as the younger generations (Piette et al., 2011).
Emotional and physical fatigue.
Emotional and physical fatigue brought about by taking medication can cause
non-adherence. Given that senior citizens are more prone to having more than one
chronic disease, each is managed by a plethora of different drugs. Often, patients must
take these medications regularly (Marcum et al., 2017). The side effects of the drugs can
be exhausting both mentally and psychologically, hence resulting in non-adherence.
Therefore, patients can simply refuse or forget to take the drugs, thus resulting in
medication wastage and increased cost of healthcare due to the wastage (Shruthi et al.,
2016). Physical exhaustion can occur if the patient must travel for the drug to be refilled.
The importance of eHealth and the use of mobile devices is that it can help patients
obtain drugs when needed.
The relationship that the patient has with their providers and caregivers can
influence commitment to medication prescription. Living on medication is not easy,
hence requiring much support in the form of education and encouragement by the
healthcare providers regarding medications. Caregivers should have an empathetic
relationship with their patients so that they can offer to encourage them to communicate
about their adherence (Midão et al., 2017). The caregivers should be well trained to take
26
care of the chronically ill patient, especially if they have a condition affecting their
mental health (El-Saifi et al., 2019).
Communication.
Communication is another barrier experienced in medication adherence.
Schoenthaler, Knafl, Fiscell, and Ogedegbe (2017) explored if healthcare providers and
patient communication played a role in medication adherence/compliance for
hypertensive patients. Schoenthaler et al. (2017) gathered information from a population
of 92 hypertensive patients. The data were collected through a patient-provider; all
encounters were audiotaped at baseline was used and coded using the Medical Interaction
Process System. The data were collected for 3 months regarding patients’ adherence.
This study was limited to patients with hypertension in primary care settings in
New York City, as more than 90% came from the New York region. The findings were
that the odds of poor medication adherence are greater when patient-provider interactions
are low in patient-centeredness and do not address patients’ socio-demographic
circumstances or their medication regimen (Schoenthaler et al., 2017). Lack of adherence
has resulted in high rates of morbidity and, in some cases, has resulted in deaths.
Researchers have reported that most patients who do not adhere to their prescribed
medication regimens lack knowledge on the importance of medication (Schoenthaler et
al., 2017). Therefore, communication and education of the public on the importance of
prescribed medication in treatment plans is of the utmost importance to ensure good
health.
There are various causes of noncompliance with medication. According to
Hugtenburg et al. (2013) and Fischer et al. (2010), some of the causes include fear of
potential side effects, misunderstandings of the prescriptions, depression, and mistrust of
27
the medication and lack of symptoms, among others. Before any intervention to the
issues of noncompliance is considered, there is a need to understand the underlying
causes of non-adherence. Based on the literature reviewed, the causes of non-adherence
can be put in several categories, such as social and economic aspects; factors related to
medication, patient-related aspects, and health care system issues; and finally, the issues
related to the condition in which a patient is suffering. By understanding the cause of
noncompliance to drug administration, a good policy to ensure compliance can be well
designed, such as the National Public Health Policy in Sweden (Wamala et al., 2007).
There is a need to measure noncompliance to medication as researchers and
clinicians can then use results to design tailor-made intervention mechanisms. Ineffective
methods of countering the problem of non-adherence can result in undesirable outcomes
(Wamala et al., 2007). Undesirable outcomes are costly and dangerous to the patient’s
health and should be avoided at all costs by ensuring that each cause of noncompliance is
well articulated by the patient and dealt with accordingly. Inaccurate methods of
intervening in the issue of noncompliance due to lack of communication may result in the
rejection of a highly effective method of intervention by the patient. ( )
Utilization of mobile technology to enhance adherence.
According to Braun et al. (2013), the concept of mobile technology is the type of
technology using cellular communication. Healthcare providers can use technology to
provide care and monitor patients at a distance. Technology has many uses in medicine,
evolving and changing the dimensions of the delivery of care. The accessibility factor
makes a significant impact on health care. The use of technology in health care aid with
patient monitoring, decrease unnecessary office visits, and decrease hospitalizations, as
further described by Braun et al. (2013).
28
Accessibility.
Providers can call patients using their mobile phones to check if patients have
any questions or problems. In a study by Lyons et al. (2016), patients with chronic illness
were enrolled in a program with access to two telephone conversations after 1 month or 6
weeks where they talked to a pharmacist. After 6 months, the findings showed that those
who had the telephone conversation had an adherence rate of at least 90%, whereas those
in the control group had an adherence rate of 19.6%. These findings indicated that having
a telephone conversation with a chronically ill patient could significantly increase their
chances of adherence. However, the content of the telephone conversations did not
necessarily focus on adherence, but the conversations were tailored to the individual
needs of the patient (Turner et al., 2016). Having patient-centered care should be the goal,
even when the use of mobile phones is introduced. The provider should have the etiquette
required when asking the patient about their personal information (Haase et al., 2017).
Calling patients is an expression of care. Central to the role of nursing is patient
care, which should be pursued through all possible means (Delaney, 2018). Providers can
partner with their patients and build a professional relationship to enhance care using
mobile phone conversations. To ensure the objectivity of the conversation, the provider
should have a guide. Delaney (2018) suggested the MAS be used so that the conversation
would be focused on adherence. Objectivity enhances respect and ensures that the right
boundary is set to enhance a professional relationship between the provider and the
patient (Steele et al., 2016). Providers should support patients, especially those patients at
risk of psychological issues, such as stress from their conditions. Such care ensures that
the patients have follow-ups and reminders to remain committed to taking medications as
prescribed, as opined by Watkins et al. (2018).
29
Cellular phones are already accessible in the United States. The rapid penetration
of devices has transformed the population of the United States, such that even the seniors
have smartphones (Watkins et al., 2018). Indeed, up to 59% of people aged 65 to 69 years
have smartphones; the percentage of smartphone owners for those in the age bracket of
70 to 74 is 49% (Subramanyam et al., 2018). Besides, those who do not have wireless
phones have landlines and other analog phones, which can still be used for conversations
regarding their health (Boulos et al., 2011). The importance of smartphones and other
wireless phones is that phones are portable; therefore, the owners can be found easily
through a phone call. The availability of mobile phones, which provide accessibly to the
patients, will aid in the successful implementation and completion of the DPI project,
given that the patients will only have to use their preferred phones at the time of calling.
Smartphone use to increase medication adherence.
In the United States, more than half of adults over 65 years old take at least three
to four medications daily to treat chronic conditions and age-related changes in physical
and emotional health (Sanders, 2013). Park, Howeie-Esqivel, and Dracup (2014)
conducted a systematic quantitative review without meta-analysis for prevention
purposes, as well as the management of acute and chronic illnesses. The researchers
found that the use of text messaging would significantly improve medication adherence.
Data collection consisted of a literature search of 29 quantitative research studies related
to mobile phones and medication adherence. Although there was a significant
improvement in medication adherence, it was suggested that long-term studies
characterized by rigorous research methodologies, appropriate statistical and economic
analyses, and the test of theory-based interventions are needed to determine the efficacy
of mobile phones to influence medication adherence (Park et al., 2014).
30
Individual patients can use invented applications (apps) to improve their practices
of adhering to medical prescriptions—the other reason that using smartphones may
increase adherence to medication (Morrissey et al., 2018). Inventors create apps to
enhance self-management for patients facing chronic conditions. Morrissey et al. (2018)
showed that increasing patients, including older adults, have embraced the use of
smartphones to improve their health statuses. Choi et al. (2015) also pointed out the
benefits of using smartphones in increasing adherence to medication. The researchers
identified 160 adherence applications, which were integrated into smartphones. Their
findings showed that irrespective of the untested nature of the majority of the apps, they
represented a possible strategy recommendable by healthcare providers to patients who
are non-adherent to improve their ability to observe medication (Choi et al., 2015). For
this research study, the use of the phones is an added advantage to the targeted patients
given their high potential of increasing adherence to medication.
Phone intervention for medical care is a rapidly evolving practice that has been
utilized to improve the delivery of health services in many jurisdictions across the world
(Free et al., 2017). The use of the phone can be a low-cost solution to offering health
education and improving medication compliance for people with chronic diseases. For
instance, Kim and Jeong (2017) studied mobile phone SMS use by nurses in South Korea
and found that for 6 straight months, the use of SMS reduced HbA1C in patients with
diabetes to about 1.15% at 3 months and about 1.05% at 6 months, which was somewhat
better when compared to the baseline in the control group. Similarly, Horvath, Ill, and
Milánkovich (2017) also demonstrated that phones were effective tools for offering
health education, medication and clinic appointment reminders for chronic diseases, such
as HIV and diabetes, as well as for building awareness regarding diseases. Recent
31
research in the Netherlands showed that mobile phones improved compliance to
medication by Type II diabetes patients, particularly regarding the precision with which
the patients adhered to the regimen prescribed; additionally, they accepted it as an
essential intervention tool for medication compliance (Vervloet et al., 2018).
Summary
Medication adherence, especially among seniors (people aged 65 years and
above), is poor. Yet, this population has an increased risk of getting multiple chronic
diseases compared to younger people. Nonadherence has negative impacts ranging from
increased hospitalization and deaths, proneness to opportunistic diseases and worsening
of symptoms, and elevated costs of treatment. Given that the population throughout the
globe is aging, the issue of non-adherence is a major concern that should be addressed.
Many theories have been applied in handling the issue of non-adherence, the
investigator has found the HBM as the most effective. Theorists have postulated that
messages will achieve optimal behavior change if they successfully target perceived
barriers, benefits, and self-efficacy (C. L. Jones et al., 2016), thus finding the barriers to
noncompliance can help increase compliance through discussion, clarification, and
patient education.
Several factors can negatively affect compliance with medication. Such factors
can be economical, making acquiring the medication unaffordable. Another factor is
emotional fatigue resulting from taking many drugs almost daily, dealing with the side
effects of those drugs, and lack of supportive relationships. A weekly call and completing
a MAS exude caring, concern, empathy, and support.
It is expected that the group with the intervention will show a significant change
in that their rates of adherence which will lead to positive health outcomes. The next
32
chapter will be the methodology section. Chapter three contains detailed information
about the methods and designs used in identifying and selecting the sample, collecting
data, and analyzing the content provided.
33
Chapter 3: Methodology
Older adults have chronic diseases and multiple comorbidities. Adherence to
medication is essential in achieving therapeutic levels, which is beneficial in disease
management (Frances et al., 2016). Nevertheless, medication compliance has been an
issue, particularly amongst the geriatric population. Medication adherence (i.e.,
medication compliance) is a complex and important component of caring for older adults.
Many research studies about noncompliance with prescription medication have occurred
among geriatric patients. Although qualitative, quantitative, and mixed-method
approaches have been utilized to discuss this global problem, most researchers utilized
quantitative approaches. Quantitative researchers present the findings numerically and
ensure generalization of findings to a wider population.
For this project, the quantitative correlational method was to answer the following
questions:
Q1: To what degree does the implementation of a medication adherence scale via
weekly phone call increase medication compliance among geriatric patients with
chronic diseases?
Q2: What is the relationship between the patients who are participating in the
weekly MAS and the patients that are not participating?
This chapter includes the details about the methodology that was used to get the
relevant data for the project. It discusses the project’s methodology, project design,
population and sample, instrumentation, validity and reliability, data collection
procedures, data analysis procedures, ethical considerations, and limitations of the
project. Emphasis was placed on documenting the processes involved in conducting this
project in detail to facilitate replication by others.
34
Statement of the Problem
The problem with non-adherence is that it increases the chances of prolonged
hospitalization, worsening of symptoms, and possibly even causing death. Specifically,
lack of adherence causes nearly 125,000 deaths, causes 10% of hospitalizations, and costs
the already strained healthcare system between 100 to 289 billion dollars a year (Mayo &
Mouton, 2017). Miller (2016) attempted to find answers and provide recommendations to
assist with the problem of non-adherence among the chronically ill seniors. His cross-
sectional study of a large sample of 75,000, establish that 30% of people with chronic
illness did not refill their prescriptions; diabetes and high cholesterol were not filled 20%
to 22% of the time, respectively (Miller, 2016). According to Bazargan (2017), cultural
factors are among the causes for non-adherence; the study showed that an average of
5.7% of African-Americans aged 65 years or more did not know the purpose of at least
one of their medications over 56% of the time. Additionally, non-adherence results in a
high cost of treatment for both the individual patient and the healthcare system (Mayo &
Mouton, 2017).
Healthcare providers and patients should work together to formulate
individualized plans to arrive at compliance. Uses of information technology in
healthcare, such as mobile applications, have been shown as useful in enhancing
adherence. However, it was not known if the implementation of a MAS through a weekly
phone call from the interdisciplinary team to noncompliant patients can increase
compliance with the medication regimen at the clinic. This DPI project showed new
35
findings relevant to resolving the problem.
Clinical Question
Restating the clinical questions provides a basis for understanding the design
adopted in strategizing processes to increase compliance among the geriatric population,
which is crucial for healthcare providers to aid geriatric patients at being compliant.
Identifying strategies that can influence medication compliance in geriatric patients can
significantly decrease the rate of negative outcomes related to poor compliance in the
geriatric population (reference)
It is not known how to increase medication compliance by providers amongst the
geriatric patients. The PICOT question to be answered is the following: (P) With geriatric
patients with chronic illnesses who are noncompliant with their medication regimen, (I),
how does the implementation of the MAS via a weekly phone call (O) improve
compliance (C) comparing to those who do not participate (T) over a period of six
weeks?
The specific clinical questions are as follows:
Q1: To what degree does the implementation of a MA via weekly phone call
increase medication compliance among geriatric patients with chronic diseases? The
MAS is a scale used to evaluate the degree of adherence to medications. The MAS was
originally developed in 2000 by Myong Kim, Martha Hill Lee Bone, and David Levine.
This scale was used to measure medication adherence for hypertensive patients. Since
then, it has been used for several chronic diseases. The MAS for this DPI project was
used as a tool for screening geriatric patients for medication adherence.
The first clinical question was to determine if there is a relationship between the
MAS and the increase in medication compliance. The weekly MAS performed via phone
36
call will be the independent variable, while an increase in compliance rate with
medication regimen among geriatric patients is the dependent variable. The second
question is the following:
Q2: What is the relationship between the patients who are participating in the
weekly MAS and the patients who are not participating? The second clinical
question focused on examining if there is a relationship between the patients
participating in the weekly MAS and the patients who are not participating. The
independent variable was the relationship between patients who are participating
in the weekly MAS. The dependent variable was the outcome of participation.
Project Methodology
Three basic methods of conducting projects include quantitative, qualitative, and
mixed-method designs. Qualitative projects are often used for exploring phenomena that
deal with the question of “why” and “how” of the problem statement. The investigator’s
focus when conducting qualitative projects is to explore the similarities and patterns in
the dataset (Brannen, 2017). Conversely, the investigator can use quantitative projects to
show the relationship between the dependent and the independent variables. The method
is ideal in problems requiring future predictions to develop an understanding of the
degree to which one variable impacts the others.
With most projects, the investigator starts with identifying variables and then
forming the project questions to be tested (Rivera et al., 2017). The mixed-method
researcher combines both the qualitative and quantitative methodology in the same
project (Halcomb & Hickman 2015). For this project, a quantitative methodology was
utilized. Quantitative methodology is often objective as it employs randomization in the
sampling procedure and uses a big sample. The results obtained can be generalized to a
37
wider population. The use of quantitative methodology is most appropriate given the
nature of the PICOT question, which involves predicting future outcomes for using
weekly MASs to enhance compliance.
Project Design
The project’s design was specific to a strategic method of collection and analysis
of data. The focus of the project is on the objectives to be achieved, as well as how the
presented project’s problems are tackled to evaluate pre-and post-intervention outcomes.
In essence, the design was concerned with the operation patterns in the project, such as
the kind of information to be collected, the sources of obtaining such information, and the
specific procedures needed (Hicks, 2009). The project design is extremely important.
Adopting the correct design ensures that the information will show all the concerns raised
in the research questions (Lamiani, Borghi, & Argentero, 2017).
A correlational design was deemed as most appropriate for this project because
the investigator has an interest in the absence or presence of a predictor relationship
between use of the weekly MAS and level of compliance among chronically ill geriatric
patients. According to Foot et al. (2016), the correlational design is most proper in project
questions seeking to analyze the predictive relationship. With this design, it is more
effective to study the scores in a group as opposed to individual scores; the investigator
keeps the group variable discrete to retain the highest power in the statistical result.
Investigators can use correlational designs to discover relational trends (assessing the
positive and the negative variables) within a single group (Lamiani et al., 2017).
According to Rivera et al. (2017), correlational project researchers should have a
large range of variables scores to determine the existence of the relationship. With this
project, the main variables include weekly MAS through phone calls (independent) and
38
increased compliance rate with medication regime among geriatric patients (dependent).
Other variables that were not the focus of this project but might have influenced the
outcome would include the sample’s ethnic background, level of education, marital
status, socioeconomic status, and health literacy.
One of the techniques commonly used to collect data in correlational design is
survey questionnaires. For the project, the MAS served as a survey questionnaire and was
utilized in the data collection process. Participants were asked to respond to the MAS
truthfully about their compliance with their medication regimes.
Population and Sample Selection
39
The population of interest for the project included geriatric patients with
chronic illnesses, specifically those who self-report noncompliant with medication
regimens in a clinic located in the southeastern part of the United States. For this reason,
the investigator applied the following inclusion and exclusion criteria: (a) patients 65 to
82 years of ages and above; (b) patients with at least one chronic illness for which a
prescribed medication has been provided, (c) patients are not hospitalized, (d) patients
self-admit noncompliance with their prescription medications, (e) patients who are not
cognitively impaired, and (f) patients with an operational phone. The exclusion criteria
involved exempting patients below the age of 65 who are compliant with a medication
regimen, have no chronic illness, are hospitalized at the time of the study, or are
cognitively impaired, and do not have access to a phone. For the sample, the investigator
identified a total of 60 patients who are between the ages of 65 to 82 and self-admit
noncompliance. They were divided into two groups; one group received a phone call
weekly and an administration of a MAS while the other did not.
A convenience sample was used in getting the sample as it has the advantage of
allowing the investigator to obtain relevant basic data as well as trends regarding studies
as opposed to the use of a randomized approach (Li & Haupt 2016). The procedure for
undertaking this sampling method involved first taking multiple samples from the
population—an approach meant to produce reliable results. Secondly, the process of
surveying the population was repeated to understand whether the results are truly
representative of the population identified chronically ill geriatric patients. The third and
final stage involved cross-validation of the data, followed by comparison with the other
section of the general population. Selecting the desired sample to reduce bias and
facilitate the repetition element process (Etikan, Musa, & Alkassim, 2016). The patients
40
were divided into three strata based on their number of chronic diseases. The first strata
comprised of patients diagnosed with one chronic illness. The second strata were for
patients with two chronic illnesses, while the third strata were for patients with more than
two chronic illnesses. The investigator then performed a convenience sampling for each
of the strata.
This sampling strategy was selected because of its objectiveness. Other
advantages of using convenience sampling include enabling the investigator to get a
separate effect size from each of the strata and ensuring that even the minority samples
are included in the study to get representatives from all populations, especially when the
process is repeated (Ponto, 2015).
The total sample obtained for the study will include 30 participants. This sample
was convenient for the investigator as it helped in writing a program for administering the
MAS.
In calculating the right sample size, the general formula is the following:
n=2(Zα+Z1− β)
2 σ 2
∆2
In the equation, n is the sample size required, and were 30, Zα, Z are constants with
regards to accepted α . On the other hand, the Z1-, β, Z are also representative of
constants reliant on the power of the study. The σ is the standard deviation, while ∆
refers to the difference in the effect of two interventions (Kadam & Bhalerao 2010). The
calculation of the sample size is as follows:
n=2(0.123+0.0 .369)2(0.492)2
0.252 = 30
The investigator predicted the following two potential outcomes of the study:
41
H1: There is a strong relationship between the implementation of weekly phone
calls and the administration of a Medication Adherence Scale and medication
compliance of the geriatric patients.
H2: There is a statistically significant difference in medication compliance levels
between the chronically ill geriatric patients who participate in the weekly phone
calls and the administration of a Medication Adherence Scale program and those
who do not.
Instrumentation or Sources of Data
The tool used for data collection will be the MAS with five questions; it was
approximated to take, at most, four minutes. The questions’ responses will be 1, 2, 3, or
4, assigned respectively, which translated to the highest possible of 20 and the lowest of
5. The interventional group n=30 received a phone call and completed the HB-MAS
weekly. A high scoring scale is indicative of less adherence while a low score indicates
more adherence to the prescribed medication regimen. The pre and post-HB-MAS scores
for the comparison and the interventional groups were compared and analyzed. However,
it was predicted that the comparison of the HB-MAS for both groups will show an
increase in compliance rates of all participants enrolled in the weekly phone calls and the
administration of an HB-MAS program. Given the sample for the current study, the
administration of the HB-MAS via phone interviews was cost-effective compared to face
to face, which will incur transportation costs and be time-consuming (see Ponto, 2015).
The questions of the HB-MAS were brief and concise, uses a variety of questions
that were easy to administer. This was ideal, given that the target population comprised of
42
geriatric patients who may tire quickly. Questionnaires often show high levels of internal
consistency and validity, which can ensure that the actual variables of the study are,
measured (Van den Broucke et al., 2011).
Validity
. Validity refers to the degree to which evidence in a project measures what they
claim to measure (Althubaiti et al., 2016). In the current project, criterion-related,
content, and construct validity of the questionnaire will be established. The content
validity indicates the extent to which the items included in the questionnaire and the
scores of each question represented all possible questions about the improvement of the
compliance rate among chronically ill geriatric patients. The key concept of
administering a MAS through phone calls and chronic diseases noncompliance was
compared with other related studies to identify similarities in the findings. The reliability
and validity of the MAS tool are because it is easy to implement and can be adjusted as
necessary (Ueno et al. 2018).
According to Provost et al. (2015), validity is discriminant and convergent.
Discriminant validity shows how items operate in the same way converge with like items
and diverging while discriminating against opposites. Conversely, convergent validity
refers to how several variables associate positively in a similar direction; higher
convergences have more similarities in operations. The goal was to establish valid
scientific outcomes; hence, the questionnaire was adjusted to achieve accuracy and
credibility.
Reliability
Reliability is the extent of the consistency and reproducibility of the study
(Leung, 2015). The participants were randomly split into two halves. The results for each
43
set were analyzed to ensure that the research instrument is reliable. This process showed
high similarities between the split-halves, which is indicative of the instrument's high
level of reliability. Reliability is significant during the assessment, as it is often presented
as contributing to the overall validity of the study. Additionally, reliability is the extent to
which a tool gives measurements that are consistent, stable, and repeatable (Kelly,
Fitzsimons & Baker, 2016). For the project, all the questions were clear and free of error.
The questions from this tool ensured that measured specific variables and were easily
assigned a numerical variable, which eased the analysis process. Utilizing the Rasch
Analysis Index will enable examination of whether replication of items in the same order
is possible given a different sample with similar characteristics (Chang et al., 2014). The
Rasch analysis model was used to point out negatively worded items, leading items,
redundant items, and those out of the concept, thus not having any valid contribution to
the research questions. The items of the final instrument contained questions relevant to
the project and they are free of errors contributing to a high level of reliability.
Data Collection Procedures
The data collection procedure was a significant step in the project process as it
involves the practical steps taken to gather information from the participants (Li et al.,
2015). According to Li et al. (2015), data collection procedures should outline the
systematic steps used to arrive at the evidence for the project question. Several
procedures and methods that can be used in data collection include case studies, historical
methods, descriptive methods, and experimental methods. For this project, a combined
aspect of survey and experimental procedures used in obtaining the data.
At the beginning of the project, the delivery of participant’s medications was
confirmed with the pharmacy. Participants were required to bring their medications for
44
reconciliation and confirmation before the start of the project. It was confirmed that all
participants had the right medication and the right amount of medication to cover the
whole 6 weeks of the project. Furthermore, for this project, the participants were
approaches on days that were not too busy, and when the target populations had group
therapies to get as many people as possible. The investigator, with the assistance of the
clinic’s employees, introduced herself as a doctorate student conducting a project on how
weekly phone calls and the administration of an HB-MAS can enhance medication
compliance among the chronically ill geriatric patients. The relevance of the project was
explained to the patients, healthcare providers at the clinic, as well as the assistant. They
were also made aware of the duration of the project, and the need for a phone number for
contact purposes.
An HB-MAS was collected from all 60 participants at the beginning of the
project. The completed HB-MASs were stored in a locked drawer to prevent
unauthorized events. Over the consecutive 6 weeks, a phone call was placed and an HB-
MAS was completed for one group each week. Participants must have been contacted for
all six weeks to be eligible. At the end of the 6 weeks, an HB-MAS was again
administered to all 60 participants. The data for both groups were then compared to
analyze the degree of compliance. Clinical values were also compared and examined.
The MAS was securely stored in a locked drawer in an office with a locked door to avoid
interference from unauthorized individuals in preparation for the data analysis process.
The entire data collection process took a period of 7 weeks; 6 weeks mainly used for the
MAS administration. The data was then transferred to Statistical Package for the Social
Sciences software (SPSS) for calculation.
The variables being assessed include the following:
45
The independent variable included the implementation of weekly phone calls
and the administration of a MAS through a phone call to establish patients’
adherence to medical prescriptions.
The main dependent variable included the level of adherence to medical
prescriptions.
The other dependent variable included the number of chronic diseases,
age, gender, educational level, socioeconomic level, and relationship
status, as they are also related to medical prescription adherence.
Data Analysis Procedures
Given the nature of the project as a quantitative study, statistical and
mathematical procedures were used to analyze the data. The characteristics and
demographic features of the participants involved using descriptive analyses. As
suggested by the name, descriptive statistics often yield findings in terms of the standard
deviation (absolute dispersion), means (arithmetic mean), correlational coefficient,
percentages, and frequencies, which are relevant in understanding the scope of
participants. The descriptive statistics also enhance the process of discussing the results.
The descriptive statistic process was used for measures of variation and dispersion. The
collected data was then transferred to a Statistical package for the social sciences (SPSS)
the process of calculation, and open-refining.
Given the nature of the project as correlative, the Pearson correlation coefficient
“r” (product-moment correlation coefficient) was utilized in the analyses. The clinical
questions included the following: (a) What is the relationship between the patients that
46
are participating in the weekly phone calls and the administration of a medication
adherence scale program and the patients who are not participating, and (b) to what
degree does the implementation of weekly phone calls and the administration of a MAS
via a phone call increase medication compliance among the geriatric patient?
Responding to the first question involved comparing general assumptions
concerning the findings from the participants. The response to the second question
involved analyzing the effect of the use of a phone call and MAS in improving
compliance with the medication regimen. The first assumption to be considered is the
following: There is a strong relationship between the implementation of MAS via weekly
phone calls and medication compliance of the geriatric patients. The second assumption
is the following: There is a statistically significant difference in medication compliance
level between the chronically ill geriatric patients who participate in an HB-MAS
program and those who did not.
The Pearson correlation model is proper when the variables are normally
distributed because the coefficient is often affected by values that are extreme, leading to
an exaggerated of dampened result (Yang et al., 2016). According to Pandis (2016), the
Pearson correlation coefficient is effective in expressing the strength of how two
variables correlate within a linear relationship; the values often range from -1 to 1. A
positive correlation is determined if findings show that high values in one variable rate
with high values of the others. In this case, a positive correlation can be evident because
the progressive use of weekly phone calls and an HB-MAS can be associated with higher
rates of medication compliance for the group.
A hierarchical multiple regression analysis was used to test the other variables
that may have influenced the outcome of the findings for both assumptions. Other
47
variables were measured using the hierarchical regression analysis which includes age,
gender, educational level, and numbers of chronic disease characteristics. The reason for
including hierarchical regression is due to findings of other investigators suggesting that
such factors can affect adherence to medical prescriptions (Yap et al., 2016). The SPSS
was utilized in both of the statistical calculations to determine correlation, and the
recommended p-value was ≤ 0.05.
Ethical Considerations
Ethics is concerned with the conduct of peoples, hence provides guidelines for
standards and norms that are acceptable when interacting with others (Ellis-Barton,
2016). Therefore, investigators should abide by the Institutional Review Board (IRB)
guidelines. The IRB guidelines are aimed to protect participants from physical,
emotional, psychological, monetary, and legal issues that may arise when research studies
are conducted in ways that are inconsistent with the required guidelines (Yip et al., 2016).
In some cases, ethical issues may be related to the research process. Before the data
collection process, IRB approval, permission to utilize the evidence-based tool, and
permission to conduct the project were given by the administration of the clinic to
conduct the project at the site.
Privacy, anonymity, and confidentiality were maintained throughout the whole
process. Participants were recruited privately in a comfortable environment and all of
their questions were answered. Privacy was also maintained and ensured during the
administration of the MASs. Additionally, all the data collected were stored in a secure
place to prevent any unauthorized access. They were securely placed in a locked drawer
behind a locked office door accessible only by the investigator.
48
Confidentiality and anonymity were ensured by assigning numeric code to each
participant. The utilization of the codes helps ensure specific information is not traceable
to specific respondents. Participants were not required to provide any personal
information in the instruments which ensure a high degree of anonymity. Furthermore,
data analysis did not involve the names of participants nor the project site.
To reduce any bias guidelines and the role of each participant were clearly stated
at the start of the project. Additionally, it was made clear to those participating at the
beginning of the project that there will not be any forms of material or monetary rewards
for their participation. The participants were also made aware that they were at liberty to
ask any question for further clarification before agreeing to participate. Only those who
consented took part in the study. Furthermore, it was communicated to the participants
that as much as their participation during the entire time of the project was highly
desired; they were at liberty to stop their participation at any time if they choose to and
for whatever reason without fear of any consequences.
The results of the project will be shared with the colleagues at the clinic to
encourage the use of evidence-based information for dealing with chronically ill geriatric
patients who are not adhering to their medication regimen. The project was beneficial to
all parties involved, which includes the participants, the facility, and the investigator.
Throughout the process of conducting this project, there was minimal if any harm
incurred by either the investigator or the participants.
Limitations
According to Theofanidis and Fountouki (2018), the limitations of a project refer
to any particular weaknesses usually out of the researcher’s control and are closely
associated with the chosen research design, statistical model constraints, funding
49
constraints, or other factors of the characteristics attached to the design or the
methodology that affects the findings and their interpretation. Limitations often provide
constraints on generalizability, practical applications, and other utilization of the findings.
There is no absolute perfect project because there are various loopholes that can
compromise the integrity of the study. However, these loopholes can be addressed with
keen consideration. One of the limitations of this project was that for participation to be
possible, the respondents needed to have a phone. Although a large number of geriatric
patients had access to a phone, a few participants who are qualified and willing to take
part in the project were not able to participate due to a lack of access to a phone. Given
that the population of interest is of geriatric who, unlike the young generation, have lower
chances of having a phone, a few were excluded. Additionally, patients who become
hospitalized during the project would have had to drop out of the study. Fortunately, none
were hospitalized during the project.
The other limitation of the study was that the sample used was not representative
of the entire population as one of the characteristics of MAS (Lam & Fresco, 2015). All
the participants will be sampled from within the same clinic, which caters to a specific
culture, thereby implying that generalization of an older adult is not entirely appropriate.
This process will also be a limiting factor as it will gradually reduce the sample size.
Based on the sample, the study may not be representative of patients from all
socioeconomic backgrounds and ethnicities.
Summary
Issues of noncompliance to prescribed medication among geriatric patients with
chronic illnesses are common (Mayo & Mouton, 2017). Yet, the consequences of not
following the medication have detrimental effects on both the individual patient and the
50
healthcare system. Resultantly, there is a need for providers to adopt new strategies to
collaborate with geriatric patients so that they can improve on how they comply with the
drugs. The weekly phone calls and MAS provide the potential for such collaboration;
however, there is a paucity of studies that have explored this possibility. The project was
an attempt to fill this research gap by investigating the correlation between weekly phone
calls and the level of adherence. a quantitative correlational design was to examine how
to increase medication compliance by providers among geriatric patients. The data was
collected in a local clinic with a total of 60 respondents selected following stratified
random sampling. The project took place within six weeks, where the MAS was
administered to one group of the respondents. HB-MAS was given to collect data at the
beginning of the program. At the end of the six weeks, an HB-MAS was administered for
comparison and clinical values were examined. The collected data were statistically
analyzed. This project had a few limitations. Ethical considerations were considered, for
the protection of both the participants and the investigator. A discussion of the data
collected and the analysis using simple descriptive is statistics are discussed.
51
Chapter 4: Data Analysis and Results
Medication compliance is an essential part of the treatment plan. However, 50%
or more of patients with chronic diseases do not take their medications as prescribed.
(Sanders & Van Oss, 2018). In the geriatric population, nonadherence increases with
multimorbidity, polypharmacy, regimen complexity, previous adverse drug events
(ADEs), and impaired cognition (Siu et al.2019). According to Costa et al., (2015),
medication adherence is recognized as a worldwide public health problem, particularly
important in the management of chronic diseases. Aging puts the geriatric patient at risk
for chronic diseases. Proper usage of medications and compliance to medications has
been associated with improved health, increased functional status, decreased risk of falls,
improved cognition (Sanders & Van Oss, 2018).
The purpose of the project is to evaluate if an intervention such as a weekly phone call
and the administration of a medication adherence scale (MAS) can help providers to
evaluate and improve medication compliance amongst the geriatric population. Although
there have been many studies regarding noncompliance with medication regimen, it
remains a worldwide problem especially with regard to the geriatric population. To
tackle this issue, this DPI aimed at investigating contributing factors to medication
noncompliance, and is geared towards finding possible ways to assist healthcare
providers in increasing compliance through planning and applying effective tailored care.
This project intended to answer the following clinical questions: Q1: To what degree
does a weekly phone call and the administration of MAS increase compliance in geriatric
patients over a period of six weeks? And Q2: What was the relationship between a
weekly phone call and the increase in medication compliance in geriatric patients? A
quantitative methodology and MAS were used to answer those questions. This chapter
52
discusses data collection and analysis including procedures and results.
Descriptive Data
Descriptive data analysis was performed to evaluate the general and clinical
characteristics of the participants. There were numerous statistical techniques used to
analyze the data. The population is geriatric patients between the ages of 65 to 82 years
old who were patients in a private clinic in the Southern part of the United States. The
participants must have had at least one chronic condition, admitted noncompliance with
their medication regimen, had no impaired cognition and had access to a phone. For the
project, there were a total of 60 patients who admitted noncompliance with their
medication regimens. The participants were divided into two even groups of 30. One half
received a weekly call and complete MAS and the other half had no intervention of any
kind. General and clinical characteristics were used to evaluate the data. All of the
participants were be above 65 years old. Forty-eight participants suffered from
hypertension, 12 with diabetes, and 33 suffered both diabetes and hypertension. There
were 19 males and 41 females (see Figure 1). There were 18 participants with one
chronic disease, 20 with two chronic diseases, and 22 with more than two chronic
diseases (see Figure 2). Forty-six participants were between 65 to 75 years of age and
fourteen were between the ages of 76 to 82 years old (see Figure 3). Forty-seven
participants were high school graduates, while 13 participants went to college (see Figure
4).
53
Female Male05
1015202530354045
MaleFemale
Figure1. Bar Graph of Participants' Gender
One chronic disease Two chronic diseases more than one chronic diseases
0
5
10
15
20
25
Number of Chronic Illnesses
Figure 2. Bar Graph of the Number of Chronic Illnesses of Participants
Educational level
High SchoolCollege Graduate
Figure 4. Pie Chart of Participants' Educational Level
54
This section included a presentation of participants' demographic characteristics. The
succeeding sections provided the data analysis procedures employed to address the
research questions posed in the study. After which, the results of statistical analyses were
presented.
Data Analysis Procedures
The data collected were used to answer both clinical questions. The first clinical
question that was answered is was: To what degree does a weekly phone call and the
administration of HB-MAS increase compliance in geriatric patients? The second
clinical question was: What was the relationship between a weekly phone call and the
administration of an HB-MAS in medication compliance in noncompliant geriatric
patients?
The questionnaire was developed to establish valid scientific outcomes, hence when
developing the questionnaires, consideration to achieve accuracy and trustworthiness was
made. The development considered high inter-item correlations and a Cronbach's
reliability value of at least 0.70. The quantitative method was used along with the SPSS
to analyze the data.
Utilizing the correlational project design, the degree of compliance pre-and-post-
intervention was compared and barriers to compliance were identified. The data was
collected over a period of six weeks. Some of the descriptive data that were used are age,
gender, number of chronic diseases, and name of chronic diseases. Inferential statistical
analysis was used, utilizing the statistical software of SPSS, and a t-test was performed to
determine statistical significance. The use of t-test was appropriate because the focus of
the study was to compare pre and post-test compliance data in geriatric patients.
55
Results
The investigator used binary logistics to determine the relationship between a weekly
phone call and the administration of a MAS to increase compliance. This was conducted
to aid the investigator at answering the clinical question: To what degree does a weekly
phone call and the administration of MAS increase compliance in geriatric patients? First,
the responses on the MAS were evaluated to look for improvement in medication
adherence behavior. Then, the scores were evaluated along with the biomarker for the
indication of medication adherence patterns. Scores between gender, age, education, and
the number of chronic diseases were also evaluated and examined.
The investigator then examined the individual predictors. The demographics
proved to be extremely significant. The analysis of the data shows that the level of
education was proven to be significant in relation to adherence. Participants with higher
education reflected complete adherence in the fourth week of administering the HB-
MAS through the end of the project.
The second clinical question was: What was the relationship between a
weekly phone call and the administration of a MAS in medication compliance in
noncompliant geriatric patients? A regression analysis was conducted to determine
whether the weekly phone call is related to the administration of a MAS in medication
compliance in noncompliant geriatric patients. The independent variable was the use of
weekly phone calls while the dependent variable was the medication compliance of
patient-participants.
The result of the project showed a significant increase in compliance among the
interventional group. Among the 30 patients that received a call, 10 (33%) participants
56
had Hypertension (HTN), 8 (27%) were with type 2 diabetes (DM), 3 (1%) with DM,
HTN and hyperlipidemia, and 1(3. %) with DM, HTN, and 1(3%) with DM, HTN,
hyperlipidemia and congestive heart failure (CHF). Of the 30 participants who received a
phone call and completed the MAS, 28 were high school graduates and 2 were college
graduates, 26 were between the age of 65-75, and 8 were between the ages of 76-82.
Approximately 95% of all participants showed an increase in compliance with a decrease
in the MAS scores.
Table 1
MAS scores over the six week study period (N = 30)
Phone call Week1 Week2 Week3
Week4 Week5 Week6
Age 65-75 90 82 71 65 42 26Age 76-82 18 12 12 10 8 6High school graduate 96 89 75 64 52 33College Graduate 6 2 1 0 0 0Male 17 11 8 5 4 5
Female 18 15 12 10 6 6One chronic disease(HTN)
64 52 41 36 24 10
Two chronic diseases(HTN, DM)
20 16 13 9 7 5
Three chronic diseases(HTN, DM, hyperlipidemia)
9 7 6 6 3 5
Four chronic diseases (HTN, DM, hyperlipidemia, Congestive heart failure)
10 8 9 8 6 6
57
The results of the study supported that age, gender, number of chronic diseases, and
education greatly affect the level of compliance in the geriatric population. It further
supports that a weekly phone call can positively impact the level of compliance in the
geriatric population. The decreases in the weekly MAS scores positively reflected what
the investigator set out to evaluate, which was the relationship between a weekly phone
call, and the administration of a MAS, and improved medication compliance.
Summary
Improving medication compliance among the geriatric population is of the utmost
importance. In general, only 50% of the general population has been estimated to adhere
to their medications, and this may range from 47 to 100% in the elderly (Shrutthi et al.,
2016). Despite numerous studies, poor compliance among older persons remains a public
health concern, as it accounts for adverse outcomes, medication wastage with an
increased cost of healthcare, and substantial worsening of the disease with increased
disability or death. (Sshurutti, et. al, 2016). In addition, noncompliance grossly
contributes to avoidable hospitalization and re-hospitalization after discharge.
The purpose of this project was to identify whether any relationship exists between a
weekly phone call and MAS to increase medication compliance. The education provided
weekly would hopefully help provide the necessary information to the participants aiding
at increasing compliance. Chapter five includes a summary of the project, a discussion of
the findings, conclusions, the implications of the findings, and recommendations based
on the results of the present project.
58
Chapter 5: Summary, Conclusions, and Recommendations
The goal of this project was to determine if a weekly phone call and the completion of
MAS would increase compliance among geriatric patients. Several studies have
demonstrated that insufficient medication adherence among older adults can result in
worsening clinical outcomes, including re-hospitalization, exacerbation of chronic
medical conditions, and greater healthcare costs. In fact, up to 10% of hospital
readmissions have been attributed to non-adherence. Previous researchers have indicated
that poor medication adherence is associated with higher risks of morbidity,
hospitalization, mortality and was also associated with many adverse health outcomes
(Verloo, Chiolero, Kiszio, Kampel & Santschi, 2017).
This project aimed to identify if there is a relationship between a weekly phone
call and the administration of an HB-MAS in order to increase medication compliance.
These findings have indicated that such a relationship exists. These findings were mostly
supported by the literature, as will be discussed further on. Findings also have
implications on the use of phone call interventions by nurse practitioners. This study
extends the knowledge of alternative ways to promote medication adherence in geriatric
patients.
Summary of the Project
This project involved a quantitative investigation of the relationship between a
weekly phone call and the administration of MAS to increase medication compliance.
The study involved two groups with intervention in one group. The intervention was six
weeks of weekly phone calls and the administration of MAS. The main clinical questions
for this study were: Q1. To what degree does the implementation of a MAS via weekly
59
phone call increase medication compliance among geriatric patients with chronic
diseases? and Q2. What is the relationship between the patients who are participating in
the weekly MAS and the patients who are not participating? The remainder of this
chapter will include a summary of the project, a summary of the findings and conclusion,
discussion and implication of the findings, and the conclusion and recommendations
based on the results. The health belief model (HBM) theoretical framework, was used to
guide the interpretation and implications of the findings. Findings from previous studies
will be juxtaposed with the present study to determine how the results fit in with existing
knowledge.
Summary of Findings and Conclusion
The findings of this study answered the two research questions presented above. The
first key finding of this study revealed the individual predictors of medical adherence in
relation to the intervention involving a phone call and the administration of an HB-MAS.
The level of education was significantly and positively related to medication adherence,
particularly from the fourth to the sixth week of intervention. This is in line with the
HBM, which considers demographic factors such as educational levels as possible
influencers of medical adherence (Mayeye, Ter Goon, & Yako, 2019). The present
study’s first key finding indeed revealed that demographic factors may be influential in
medication adherence for the geriatric population.
Previous studies have highlighted the influence of level of education on
medication adherence in various countries and concerning various illnesses. In Kokturk et
al.’s (2018) study of chronic obstructive pulmonary disease (COPD) patients in Turkey
and Saudi Arabia, they noted that high school and college graduates were more adherents
to medication compared to non-graduates. The level of education could somehow reflect
60
the level of understanding in a patient, which could thus influence their adherence to
medical instructions, especially if these instructions are complicated (Kokturk et al.,
2018). Similarly, a study on elderly hypertensive patients in Cairo, Egypt likewise
showed that higher educational attainment was positively related to better medical
adherence (Hamza, El Akkas, Abdelrahman, & Abd Elghany, 2019). Educational
attainment was found to be related to health literacy, which in turn influenced medical
adherence (Scoones et al., 2017). These previous findings showed support for the present
study’s finding that level of education could be a factor in the relationship between
intervention and medication adherence in geriatric patients.
The second key finding of this study was that there was a significant increase in
compliance for the group who received a weekly phone call and completed an HB-MAS
within six weeks. The result of this project is aligned with Daniel, Christian, Robin, Lars,
and Thomas’s (2019) findings that intervention for geriatric patients through telephone
significantly improved medication adherence for acute coronary syndrome. They noted
that commonly cited reasons for their control group, including non-compelling side
effects and misunderstandings, were not present in their intervention group. This showed
the educational value of phone call interventions in reaching patients and improving their
adherence (Daniel et al., 2019). Another study supporting the present study’s finding was
by Huang et al. (, 2013), who found through their study of effects of a phone call as an
intervention to promote antiviral adherence, that there was a significant increase in
physical wellbeing amongst patients who received interventional phone calls. Results
showed that a phone call intervention could maintain high self-reported adherence to
patients. In a randomized trial conducted by Huang et, al. (2013) it was found that
patients who received short mobile message support had significantly improved
61
antiretroviral therapy (ART) adherence and rates of viral suppression compared with the
control individuals. Mobile phones might be effective tools to improve patient outcomes
in resource-limited settings.
Previous studies have also explored the advantages and possible disadvantages of
monitoring patients via telephone. Telemonitoring, the term for monitoring patients
through phone calls, was found to reduce both short- and long-term hospitalization rates
(Tse et al., 2018). Healthcare practitioners can keep up to date with a patient’s status,
including heart rate, blood pressure, body weight, and other vital information through the
telephone provided the patient had the proper equipment at home. At the same time,
healthcare practitioners can also give advice and increase patients’ self-efficacy through
the telephone, thereby improving their medical adherence (Tse et al., 2018). Even the
simple act of reminding patients to refill and take their medication was purported to help
patients, especially those with chronic illnesses that needed continuous medication (Costa
et al., 2015). On a negative note,
Saragosa et al. (2020) warned against the issue of patient privacy in using phone calls
and other electronic methods. Practitioner-patient confidentiality may not be as secure in
phone calls, which can easily be recorded, as it is in personal meetings (Saragosa et al.,
2020). Nonetheless, phone call interventions provide a convenient and cost-effective
method for patients who are unable to be physically present.
The findings of this study have implications for theory, practice, and the future. For
theory, the findings supported the HBM, which served as the theoretical framework of
the study. In terms of practice, findings show support for the use of weekly phone calls
and MAS to improve medication adherence among geriatric patients. Findings also have
implications for the future of nursing research.
62
Implications
The findings of this study have implications for theory, practice, and the future. For
theory, the findings supported the HBM, which served as the theoretical framework of
the study. In terms of practice, findings show support for the use of weekly phone calls,
and the administration of an HB-MAS to improve medication adherence among geriatric
patients. Findings also have implications for the future of nursing research.
Theoretical implications.
For this project, the HBM was used. It explains that a person’s health-seeking
behaviors depend on whether they feel that condition or disease presents a danger to them
or their loved ones (Becker, 1974; Rosenstock, 1990). The model considers (a) the
severity of the illness in question, (b) one’s susceptibility to this disease, (c) the
advantages of attempting to prevent the disease, and (d) the barriers that prevent one from
acting to prevent the condition (Nursing Theories, 2012; Willis, 2018). The theory also
emphasizes cues to prompt action, which in this case is medical adherence, and self-
efficacy (Lemanek & Yardley, 2019; Willis, 2018). The weekly phone calls served as
both cues to action and a means for improving self-efficacy.
The project supported the HBM theory with its findings. The theoretical implication
of the findings is that the HBM may influence the decision-making processes of the
geriatric population by accepting and understanding that complying with their medication
regimen will improve their outcomes (Rosenstock, 1990). The HBM’s weighing in of
benefits and barriers to medical adherence allows geriatric patients to realize that the
positive outcomes outweigh the negative ones (Willis, 2018). The healthcare provider’s
63
role, therefore, is to make every effort to first and foremost educate, encourage, and assist
the geriatric patients at becoming compliant and thereby positively improve health
outcomes. The findings of the present study thus extend the knowledge related to HBM,
revealing how it can be applied, even though phone calls, to geriatric patients’ medical
adherence.
Practical implications.
This project’s goal was geared toward practice improvement. Finding ways to
increase compliance will help in improving patient outcomes. Strict medical adherence
can improve health outcomes more so than treatment itself (Kim, Combs, Downs, &
Tillman, 2018). Technological strategies such as phone calls can be utilized by healthcare
providers to provide services for their geriatric patients (Kim et al., 2018). Personalized
interventions, which can be done with such calls and with the guidance of MAS, are
particularly important for geriatric patients as well, as this population may be more at risk
for errors (Van Boven et al., 2018). Consequently, this project has proven that a weekly
phone call can make a significant impact in improving medication compliance in the
geriatric population.
Enhanced medical adherence not only improves patient outcomes but also has
implications for costs. Patients who do not strictly adhere to their medication are at a
higher risk for mortality and morbidity (Midão, Giardini, Menditto, Kardas, & Costa,
2017). The enhanced health outcomes brought by low-cost phone call interventions could
mean less expenditure involving other healthcare costs. The findings thus imply that
healthcare providers could utilize cost-effective interventions, such as the weekly phone
calls, for better health outcomes and decreased costs.
Future implications.
64
This study offered an insight into medication compliance. It outlined the success that
an intervention can increase compliance. This can have huge benefits within the health
care industry as simple, cost-effective, corrective actions can help strengthen adherence,
especially with limited resources (Midão et al., 2017). However, this study did not
compare and contrast other programs. A comparison could indicate which types of
programs are more cost and time effective. Additionally, it would be interesting to know
how patients feel about the program and if it is easier on them. The findings thus imply
further inquiry into such medical adherence interventions to advance the knowledge in
the field of nursing.
Recommendations
Recommendations for future projects.
Although this project has a strong design, a good theoretical foundation, and a
positive finding, the sample size was too small and the timeframe was too short. This
project was limited to a small private clinic with low-income families who are mostly
insured by the government. The participants were mostly of one culture and ethnicity.
Recommendation for future investigation would suggest a larger sample, a longer
timeframe, a more diverse population. Also, including privately insured participants to
ensure validity and reliability.
The inclusion of more diverse samples may also allow for more comparisons in
terms of demographics. Factors other than educational level should be considered as
possible predictors for medication adherence, such as race, geographic location, and
occupation. These predictors would help nurse practitioners to model their interventions
accordingly.
As aforementioned, future studies should also examine and other adherence
65
programs in comparison to the one in the present study. An intervention utilizing other
electronic media would be interesting, especially for the geriatric population who may
not be as technologically savvy as other populations. Other comparable interventions
could include clinical or therapeutic interventions. A comparison between these different
types of interventions would help determine which type would be best utilized for
geriatric patients.
Finally, future investigators could utilize qualitative designs to explore the
perspectives of both patients and healthcare practitioners regarding the intervention.
Patients could be interviewed regarding their preference and ease of use of the
intervention. Healthcare practitioners such as physicians, nurses, and even pharmacists,
could be interviewed to gather their opinions on such interventions. A Delphi study could
even be conducted to gather expert opinion on the utility of the intervention.
Recommendations for practice.
It is also recommended that healthcare providers take time to have a conversation
about medication compliance with their patients. Personalized interventions are most
effective for patients prone to error (Van Boven et al., 2018). Geriatric patients may have
more needs than regular patients and may need more education and reminders to adhere
to their medications. Medication instructions for geriatric patients should also be clearer
and easier to follow (Midão et al., 20187). In line with the present study’s findings
regarding the level of education, the instructions provided through the intervention should
be modified to suit the needs of the patient, as some may not fully understand the
instructions. The use of phone calls could be a cost-effective way to provide such
personalized care, as it does not require the patient to be physically present but still
allows them to communicate clearly and openly with their healthcare practitioners.
66
Healthcare practitioners should not only provide informational and educational
support for patients but also psychological support. In accordance with the HBM, patients
must have adequate self-efficacy to properly comply with their medication instructions
(Willis, 2018). Healthcare practitioners should be encouraging and responsive to the
needs of geriatric patients. They should consider the patients’ perspectives regarding
possible barriers in medication adherence, and provide possible alternatives to such
barriers. They should also increase patient involvement in the process. One way to
include the patient is to utilize MAS so that patients could observe their adherence
practices for themselves. Through the personalized weekly phone calls, healthcare
practitioners could check up on the well-being of their patients and provide psychological
support to them.
Aside from phone call interventions, other interventions have been presented in the
literature, such as face-to-face types of interventions (Kim et al., 2018). Practitioners
could benefit greatly from examining these researches for an intervention that they would
deem appropriate and suitable for their patients. They should seek evidence-based
practices that have been proven to aid in increasing compliance.
Summary
With this study, the investigator was able to confirm poor medication adherence
among the geriatric population. This study was to investigate the effects of interventions
such as a weekly phone call and an administration of a MAS as it relates to medication
compliance among the geriatric population. The HBM was used as a theoretical
framework to guide the study, considering patients’ severity of illness, susceptibility,
advantages of adherence, barriers to adherence, and cues to action. These principles
guided the overall process of the study including the intervention and interpretation of the
67
results.
The results of the study revealed a significant increase in compliance for geriatric
patients who received a weekly phone call and completed MAS. Such findings revealed
that the simple and cost-effective act of calling and conversing with patients could
positively influence their medical adherence. The level of compliance was assessed by
the use of an HB-MAS and it positively correlates with educational level, age, number of
chronic diseases, and gender. The results of this project showed an increase in
compliance along with normalization vital signs and biomarkers. These findings implied
that personalization was also important in providing intervention to geriatric patients, as
each patient may have different needs. Weekly phone calls would allow healthcare
professionals to provide personalized care at a low cost for patients who may not be able
to be physically present in clinical or therapeutic interventions. The present study’s
findings thus show support for cost-effective interventions to provide informational,
educational, and psychological support for geriatric patients in terms of medication
adherence.
68
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I, Eva Jacques verify that I have completed 10 clock hours in association with the
goals and objectives for this assignment. I have also tracked said practice hours in the
Typhon Student Tracking System for verification purposes and will be sure that all
approvals are in place from my faculty and practice mentor.
90
Appendix A
GCU IRB Letter of Approval
91
1
Appendix B
HILL-BONE MEDICATION ADHERENCE SCALE(HB-MAS)
None of the time= 1, Some of the time=2, Most of the time =3 All the time=4,
1-How often do you forget to take your medicine 1 2 3 4
2-How often do you decide not to take your medicine 1 2 3 4
3-How often do you miss taking your meds when you feel better 1 2 3 4
4-How often do you miss taking your meds when you feel sick 1 2 3 4
5-How often do you miss taking your meds when you care less 1 2 3 4
2
Appendix C
Permission Letter
THANK YOU FOR YOUR INTEREST IN USING THE HILL-BONE SCALE.
SCORING GUIDE the validation and use of the scale. We would like to request that you cite the scale using the references provided. We would appreciate you sharing the findings of your research with us.
Please don't hesitate to reach out to us at [email protected] if you have any follow-up questions.
Kim, M.T., Hill, M.N., Bone, LR., Levine. D.M. Development and testing of the lIil!Bone compliance to high blood pressure therapy scale. Progress in Cardiovascular Nursing Summer 2000, 90-96. ==:.--'..:....:.:....:"..::..:=====...:..:.J:'-="=:..:::.:-:..:..:..c:...:c...:...:-"---"
