Improving Medication Compliance in Geriatric Patients

Submitted by

Eva M Jacques

Direct Practice Improvement Project Proposal

Doctor of Nursing Practice

Grand Canyon University

Phoenix, Arizona

May 27, 2020

© by Eva Mireille Jacques, 2020

All rights reserved.

GRAND CANYON UNIVERSITY

Improving Medication Compliance in Geriatric Patients

by

Eva M. Jacques

Has been approved

May 27, 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

iv

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 and unfortunately poor

compliance in the geriatric population is extensive. The purpose of this quantitative

quasi-experimental project was to determine if or to what degree the implementation of a

weekly phone call using the Hill Bone medication adherence scale (HB-MAS) would

impact medication compliance among geriatric patients in a private clinic in the

southeastern United States over six-weeks. 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. The population was of geriatric

patients’ 65 to 82 years of age. The total sample size was n= 60 of 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 the Shapiro-Wilk test. The

results of the Shapiro-Wilk test showed that the data of the MAS scores at pre-test for

both intervention group (SW(30) = 0.95, p = 0.14) and non-intervention group (SW(30) =

0.95, p = 0.12) followed normal distribution while only the data of the MAS scores at

post-test for the non-intervention (SW(30) = 0.94, p = 0.11) followed normal distribution

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 time period also a more diverse group of

participants.

Keywords: Geriatric; Medication Compliance; Noncompliance.

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Dedication

This 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

viii

List of Tables......................................................................................................................xi

List of Figures....................................................................................................................xii

Chapter 1: Introduction to the Project..................................................................................1

Background of the Project.............................................................................................3

Problem Statement.........................................................................................................4

Purpose of the Project....................................................................................................5

Clinical Questions..........................................................................................................7

Advancing Scientific Knowledge..................................................................................8

Significance of the Project.............................................................................................9

Rationale for Methodology..........................................................................................10

Nature of the Project Design........................................................................................11

Definition of Terms......................................................................................................12

Assumptions, Limitations, Delimitations....................................................................13

Summary and Organization of the Remainder of the Project......................................14

Chapter 2: Literature Review.............................................................................................16

Theoretical Foundations...............................................................................................18

Review of the Literature..............................................................................................22

Barriers to adherence.....................................................................................24

Utilization of mobile technology to enhance adherence................................27

Summary......................................................................................................................31

Chapter 3: Methodology....................................................................................................33

Statement of the Problem.............................................................................................34

Clinical Question.........................................................................................................34

ix

Project Methodology....................................................................................................36

Project Design..............................................................................................................37

Population and Sample Selection.................................................................................38

Instrumentation or Sources of Data.............................................................................40

Validity........................................................................................................................41

Reliability.....................................................................................................................42

Data Collection Procedures..........................................................................................43

Data Analysis Procedures............................................................................................44

Ethical Considerations.................................................................................................46

Limitations...................................................................................................................48

Summary......................................................................................................................49

Chapter 4: Data Analysis and Results................................................................................51

Descriptive Data...........................................................................................................52

Data Analysis Procedures............................................................................................54

Results..........................................................................................................................55

Summary......................................................................................................................67

Chapter 5: Summary, Conclusions, and Recommendations..............................................68

Summary of the Project...............................................................................................69

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Summary of Findings and Conclusion.........................................................................70

Implications..................................................................................................................72

Theoretical implications.................................................................................73

Practical implications.....................................................................................73

Future implications........................................................................................74

Recommendations........................................................................................................75

Recommendations for future projects............................................................75

Recommendations for practice......................................................................76

Summary......................................................................................................................77

References..........................................................................................................................79

Appendix A......................................................................................................................101

Appendix B......................................................................................................................102

Appendix C......................................................................................................................103

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List of Tables

Table 1. Descriptive Statistics Summaries of MAS Scores at Pre-test and Post-test……57

Table 2. Shapiro-Wilk Test of Normality of Data of Dependent Variables……………..61

Table 3. Levene’s Test of Homogeneity of Variances…………………………………. 63

Table 4. Repeated Measures ANOVA Results on MAS Scores………………………..65

xii

List of Figures

Figure 1. Participants' Gender …………………………………………………………...53

Figure 2. Number of Chronic Illnesses………………………………………………….53

Figure 3. Participants' Age………………………………………………………………54

Figure 4. Participants Education Level………………………………………………….54

Figure 5. MAS Score at Pre-Test………………………………………………………..59

Figure 6. MAS Score at Post-Test……………………………………………………....59

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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.

2

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 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 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 MAS was used to measure compliance. The information obtained

3

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.

Noncompliance with the medication regimen is a major health problem,

especially in the geriatric population (Mayo & Mouton, 2017). Non-adherence to

4

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 was not known if or to what extent a weekly phone call and the administration

of MAS can increase medication compliance among the geriatric patients. Finding a way

to increase compliance through communication, education, and encouragement may

reduce hospitalization rate and thereby improve 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.

A quantitative study of more than 75,000 commercially insured patients showed

that 30% failed to fill a new prescription, also new prescriptions for chronic conditions,

5

such as high blood pressure, diabetes, and high cholesterol, were not filled 20% to 30%

of the time (Miller, 2016). In a study African Americans, aged 65 years and older taking

an average of 5.7 medications, it was discovered that patients 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 a medication regimen. 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. 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).

6

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 received a weekly

phone call and answer the questions of the MAS versus participants that did not receive a

phone call. The goal of this project was to increase medication compliance in the geriatric

patient population 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 an HB- MAS. The weekly

phone call not only served as a reminder for patients to take their medications but also

encouraged them to ask questions they may have at that time about their disease process

and their medications. The dependent variable was the degree of compliance, as indicated

7

by data analysis from the MAS, the comparison of pre- and post-intervention, and also

the normalization of clinical values.

Clinical Questions

Looking for strategies to improve process aiming at improving compliance among

the geriatric population is crucial. Healthcare providers must strategize to find means of

improving processes to increase compliance among the geriatric population. Thus,

identifying methods 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 quasi-experimental 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 questions determined if there is a relationship between the weekly

phone call and increase in medication compliance in geriatric patients between the age of

65 to 82 years old who suffers from at least one chronic condition. The weekly phone

8

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; thus, 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 geriatric patients (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

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

9

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

confirming 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, ensure 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 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

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

10

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 the

geriatric patients served. The findings translated into decreased emergency room visits,

office visits, and hospitalizations.

Rationale for Methodology

A quantitative quasi-experimental comparison 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 admitted 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 project (see Lamiani, Borghi, & Argentero,

2017). Quantitative researchers emphasize objective measurements, statistical,

mathematical, or numerical analysis of data collected through polls, questionnaires, and

surveys, or by manipulating pre-existing statistical data using computational techniques

(Bryman, 2017). For this project, the degrees of noncompliant patients were identified

11

from the responses to the questions from MAS. The MAS 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 investigator assumes that

the phenomena 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 comparative 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 comparative design to analyze

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

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

12

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 project’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).

13

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 project (Kaur, 2013). Two types of variables are used for this project: the

dependent variable and the independent variable.

14

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’ relationship status, socioeconomic status, and health

literacy can affect a patient’s compliance.

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

was that there were more women than men who participated participate in the project.

Delimitations. This project only focused on two clinical 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

15

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 and or complications. 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 or the expected sid effects of their medications.

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.

16

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.

17

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 healthcare providers, the

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 project. 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).

Investigators 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 project 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 the formulation of interventional strategies to combat this issue.

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 as 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.

In summary, 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.

In summary, providers need to assess and evaluate the causes of noncompliance. One

must realize that adherence to a medication regimen is a multidimensional occurrence.

Healthcare providers need to communicate with their patients and assess their

understanding of their medication regimen. The stress of taking multiple medications can

become overwhelming and causes patients to be in distress emotionally.

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

28

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).

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

29

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).

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

30

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).

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.

To summarize, 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

31

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 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

32

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

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

findings relevant to resolving the problem.

35

Clinical Questions

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.

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 of a weekly phone call and the administration of an HB-

MAS (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 an HB-MAS 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

call will be the independent variable, while an increase in compliance rate with

36

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

investigator 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

wider population. The use of quantitative methodology is most appropriate given the

37

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 comparative 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), project investigators 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 increased

38

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 and those who do

not.

Instrumentation or Sources of Data

The tool used for data collection will be the HB-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

geriatric patients who may tire quickly. Questionnaires often show high levels of internal

42

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

set were analyzed to ensure that the research instrument is reliable. This process showed

43

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

reconciliation and confirmation before the start of the project. It was confirmed that all

44

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:

The independent variable included the implementation of weekly phone calls

45

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

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

46

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

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

47

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.

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

48

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

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.

49

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

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;

50

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 concerning 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).

The project involved a total of 60 pre-test participants. A total of 30 participants

was assigned to the non-intervention group while the other 30 participants were assigned

to the interventional group. The 30 intervention participants were the participants who

received a weekly phone call and complete the MAS. Among the 30 patients in the

53

intervention group that received a call (N = 30), 10 (33%) participants 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 in the intervention group 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 to 75 years old and 8 were between the

ages of 76 to 82 years old.

Female Male05

1015202530354045

MaleFemale

Figure1. 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. Number of Chronic Illnesses of Participants

54

Education level

High SchoolCollege Graduate

Figure 4. Participants' Education Level

This section included a presentation of participants' demographic characteristics. The

succeeding sections provided the data analysis procedures employed to address the

clinical questions posed in the project. 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

55

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 a t-test was appropriate because the focus of

the project was to compare pre and post-test compliance data in geriatric patients.

Results

As stated, the investigator used repeated-measures ANOVA to determine the

relationship between a weekly phone call and the administration of a MAS to increase

medication compliance among geriatric patients with chronic diseases. This was

conducted to aid the investigator at answering the two clinical questions: (1) To what

degree does a weekly phone call and the administration of MAS increase compliance in

geriatric patients? (2) What was the relationship between a weekly phone call and the

administration of a MAS in medication compliance in noncompliant 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 an

indication of medication adherence patterns.

56

Descriptive statistics for the variables of interest. Univariate analysis was

conducted using the dataset to generate descriptive statistics. Univariate analysis is a

standard procedure that typically involves the computation of means, medians, standard

deviations, and other descriptive data, usually to gain a comprehensive overview of the

dataset and to screen for outliers. Additionally, univariate analysis can be helpful for

readers to assess the generalizability of study results. Table 1 shows descriptive statistics

summaries for the MAS scores of medication compliance at pre-test and post-test

between the non-interventional and interventional group.

For the non-intervention group, the mean MAS score at post-test (M = 9.97, SD =

3.69) was significantly lower than the mean MAS score at pre-test (M = 10.03, SD =

3.68). Also, for the intervention group, the mean MAS score at post-test (M = 6.90, SD =

1.58) was significantly lower than the mean MAS score at pre-test (M = 10.53, SD =

3.73). Comparison of the mean MAS scores showed that the MAS scores among the 30

geriatric patients with chronic illnesses in the non-intervention group and 30 geriatric

patients with chronic illnesses in the intervention group have a decreasing trend in the

MAS scores from the pre-test to the post-test. It should be noted that high scores in the

survey indicate that patients have fewer adherences to the medication prescription, while

lower scores indicate more adherences. A comparison of the MAS scores at the post-test

between the two sample groups showed that the mean MAS scores for the intervention

group (M = 6.90, SD = 1.58) were also significantly lower than for the non-intervention

group (M = 9.97, SD = 3.69). However, the significance of the difference of the MAS

scores will be investigated in the repeated measures ANOVA.

Table 1

Descriptive Statistics Summaries of MAS Scores at Pre-test and Post-test (N = 60)

57

Time Group M SD N

Pre-test (week 1) Non-intervention 10.03 3.68 30

Intervention 10.53 3.73 30

Total 10.28 3.68 60

Post-test (week 6) Non-intervention 9.97 3.69 30

Intervention 6.90 1.58 30

Total 8.43 3.21 60

58

Test of required assumption of the parametric test. The repeated-measures

ANOVA was conducted to address the research objectives. This statistical analysis is a

parametric test that requires certain assumptions before conducting the test. The different

required assumptions of this test include no presence of outliers in the data set, normality

of the data of the dependent variable, and homogeneity of variance. Each of these

assumptions was tested and the results are presented below.

Outlier investigation. The first required assumption states that there should be no

presence of outliers in the data set. Again, investigation of the presence of outliers of the

final dataset including the 30 geriatric patients with chronic illnesses in the non-

intervention group and 30 geriatric patients with chronic illnesses in the intervention

group was conducted through visual inspection of the boxplot for each of the data of

MAS scores at pre-test and post-test. The boxplots are summarized in Figures 5 to 6.

Investigation of the boxplot of the data MAS scores at the pre-test for both intervention

and non-intervention groups (Figure 5) showed no presence of outliers. Investigation of

the boxplot of the data MAS scores at post-test for both intervention and non-intervention

groups (Figure 6) also showed no presence of outliers. Thus, the no presence of outline

assumption was satisfied.

59

Figure 5. MAS Score at Pre-test

Figure 6. MAS Score at Post-test

Normality. The second assumption tested the assumption of normality, meaning

that the data of the dependent variable should exhibit a normal distribution. Normality

was tested using the Shapiro-Wilk test. The results of the Shapiro-Wilk test are shown in

Table 2.

60

Results of the Shapiro-Wilk test showed that the data of the MAS scores at pre-

test for both intervention group (SW(30) = 0.95, p = 0.14) and non-intervention group

(SW(30) = 0.95, p = 0.12) followed normal distribution while only the data of the MAS

scores at post-test for the non-intervention (SW(30) = 0.94, p = 0.11) followed normal

distribution. Normal distribution was based on the Shapiro-Wilk statistics having a p-

value greater than the level of significance, set at 0.05, which was the case of the results.

However, investigation of the normal test result for the data of the MAS score at the post-

test for the intervention (SW(30) = 0.85, p < 0.001) did not follow a normal distribution.

Although the data did not follow a normal distribution, the statistical analysis of ANOVA

was used and is robust to the violation of normality (Blanca, Alarcon, Arnau, Bono, &

Bendayan, 2017). This allowed for the analysis to go on as planned. With these results,

the assumption of normality was satisfied by data of three out of the four dependent

variables.

Table 2

Shapiro-Wilk Test of Normality of Data of Dependent Variables

61

Period Group Shapiro-Wilk

Statisti

c

df p

Pre-test (week 1) Non-intervention 0.95 3

0

0.1

4

Intervention 0.95 3

0

0.1

2

Post-test (week

6)

Non-intervention 0.94 3

0

0.1

1

Intervention 0.85 3

0

0.0

0

62

Homogeneity of covariance. The fifth assumption tested is homogeneity or

equality of covariance. The assumption of equal covariance was tested using Box’s tests

of equality of covariance matrices. The p-value of the Box’s test of equality of

covariance matrix should be greater than the level of significance value of 0.05 to prove

that the covariance of the dependent variables is equal or homogenous across the

different categorical groups of the independent variables. The results of the Box’s test of

equality of covariance matrices showed that the covariance of the dependent variable of

MAS scores at pre-test and post-test was homogenous across the two samples groups of

non-intervention and intervention of the geriatric patients with chronic illnesses (Box's M

= 18.75, F(3, 605520) = 6.02, p < 0.001). Thus, the homogeneity of covariance

assumption was violated.

Homogeneity of variance. The sixth and final assumption tested was the

homogeneity of or equality of variances. Levene’s test was conducted to determine

whether the variances of the different dependent variables of MAS scores are

homogeneous across the different categories/groupings of the independent variable. The

results of the Levene’s test are shown in Table 3.

63

Results of the Levene’s test showed that only the variance of MAS scores at pre-

test (F(1, 58) = 0.20, p = 0.65) was homogenous or equal across the two sample groups of

non-intervention and intervention groups. Homogeneity of variances was achieved based

on Levene’s statistics with the p-value greater than the level of significance set at 0.05.

On the other hand, the variance of the MAS scores at post-test (F(6, 25) = 21.18, p <

0.001) was not homogenous or unequal across the two sample groups of non-intervention

and intervention groups. Thus, the homogeneity of variances assumption was violated.

However, it should be noted that the ANOVA utilize F statistics, which are generally

robust to violations of the assumption as long as group sizes are equal, which is the case

of the study (non-intervention group: n = 30, intervention group: n = 30). Equal group

sizes are defined by the ratio of the largest to the smallest group being less than 1.5

(Tabachnick & Fidell, 2013).  Thus, the homogeneity of variance assumption was still

satisfied by all dependent variables in the study.

Table 3

Levene’s Test of Homogeneity of Variances

Period F df1 df2 p

Pre-test (week 1) 0.20 1 58 0.65

Post-test (week 6) 21.18 1 58 0.00

Tests the null hypothesis that the error variance of the dependent variable

is equal across groups.

a. Design: Intercept + Group

Within Subjects Design: time

64

Repeated Measures ANOVA Results. A repeated-measures ANOVA was

conducted to determine whether the MAS scores to measure medication compliance of

geriatric patients with chronic illnesses were significantly different at pre-test and post-

test between the two sample groups of non-intervention and intervention group. This

analysis determined whether the MAS scores to measure medication compliance between

geriatric patients with chronic illnesses that participated in the intervention of weekly

phone call and the administration of a MAS (intervention group) versus those that

geriatric patients with chronic illnesses that did not participate in the intervention (non-

intervention group) were significantly different at different time periods of measurement

(pre-test versus post-test). As stated, a level of significance of 0.05 was used in the

repeated measures ANOVA. The significance of the effect of the intervention of weekly

phone call and the administration of a MAS on the MAS scores as a measure of

medication compliance is determined by investigating the differences of scores at the

different year periods between samples at the non-intervention and intervention group.

There are significant differences if the p-value of the F statistic is less than the level of

significance value set at 0.05.

65

Table 4 summarizes the results of the between-participants effects of the

invention on the MAS scores or the differences in the MAS scores between non-

intervention and intervention group. Results of the between-subjects effects showed that

the MAS scores between the non-intervention group and intervention group were

significantly different (F (1, 58) = 3.84, p = 0.05) at the level of significance of 0.05.

There was a significant difference since the p-value was less than the level of significance

value of 0.05. This means that the compliance rate with medication regimen between

geriatric patients with chronic illnesses that participated in the intervention of weekly

phone call and the administration of a MAS (intervention group) versus those that

geriatric patients with chronic illnesses that did not participate in the intervention (non-

intervention group) were significantly different. Comparison of the total mean MAS

scores in Table 1 showed that the mean MAS score at post-test for geriatric patients with

chronic illnesses in the intervention group (M = 6.90; SD = 1.58) was significantly lower

than geriatric patients with chronic illnesses in the non-intervention group (M = 9.97; SD

= 3.69). This indicated that the geriatric patients with chronic illnesses that participated

in the intervention of weekly phone call and the administration of a MAS have higher

compliance rate with medication regimen as compared to the geriatric patients with

chronic illnesses that did not participate in the intervention of weekly phone call and the

administration of a MAS.

Table 4

Repeated Measures ANOVA Results of Between-Subjects Effects of Intervention

on MAS Scores

66

Source

Type III

Sum of

Squares

dfMean

SquareF p

Partial Eta

Squared

Intercept 10509.41 1 10509.41 816.34 0.00 0.93

Group 49.41 1 49.41 3.84 0.05* 0.06

67

Error 746.68 58 12.87      

68

*Significant difference at level of significance of 0.05

Table 5 presents the results of the test of within-subjects effects. This determined

the main effect of whether the repeated measures of the MAS score were significantly

different at pre-test and post-test. The analysis also determined the interaction effect of

whether the repeated measures and intervention had a two-way influence on the MAS

scores of the geriatric patients with chronic illnesses. Results test of within-subjects

effects showed that the MAS scores at pre-test and post-test of the geriatric patients with

chronic illnesses were significantly different (F(1, 58) = 10.71, p < 0.002). Looking at

the descriptive statistics in Table 1, it can be seen that the mean MAS score at post-test

(M = 9.97, SD = 3.69) was significantly lower than the mean MAS score at pre-test (M =

10.03, SD = 3.68) for the non-intervention group; while the mean MAS score at post-test

(M = 6.90, SD = 1.58) was significantly lower than the mean MAS score at pre-test (M =

10.53, SD = 3.73) for the intervention group. Comparison of the mean MAS scores

showed that the MAS scores among the 30 geriatric patients with chronic illnesses in the

non-intervention group and 30 geriatric patients with chronic illnesses in the intervention

group have a decreasing trend in the MAS scores from the pre-test to the post-test. This

means that both geriatric patients with chronic illnesses in the non-intervention group and

intervention group have greater adherence in medication prescription at the post-test than

at the pre-test.

69

On the other hand, the interaction between the repeated measures (pre-test versus

post-test) and intervention also had a significant effect on the MAS scores (F(1, 58) =

10.70, p = 0.002) on the geriatric patients with chronic illnesses. This means that there

was a significant difference in the MAS scores of geriatric patients with chronic illnesses

at pre-test and post-test because of the intervention. A comparison of the MAS scores at

the post-test between the two sample groups showed that the mean MAS scores for the

intervention group (M = 6.90, SD = 1.58) were also significantly lower than for the non-

intervention group (M = 9.97, SD = 3.69). This means that geriatric patients with chronic

illnesses that participated in the intervention of weekly phone call and the administration

of a MAS have higher compliance rates with medication regimen as compared to the

geriatric patients with chronic illnesses that did not participate in the intervention. The

result of the project showed a significant increase in compliance among the group of 30

participants that received a weekly phone call and complete a MAS over the six weeks

period. Approximately 95% of all participants showed an increase in compliance with a

decrease in the MAS scores. Also, results showed that a weekly phone call can positively

impact the level of compliance in the geriatric population. The decreases in the MAS

scores from pre-test to post-test 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.

Table 5

Repeated Measures ANOVA Results on MAS Scores

70

Source time

Type III

Sum of

Squares

dfMean

SquareF p

Partial Eta

Squared

time Linear 102.68 1 102.68 11.51 0.001* 0.17

time *

GroupLinear 95.41 1 95.41 10.70 0.002* 0.16

Error(time) Linear 517.42 58 8.92      

*Significant difference at level of significance of 0.05

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). Also, 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. The result of the repeated measures

ANOVA showed there was a significant increase in compliance among the geriatric

patients with chronic diseases that received a weekly phone call and complete a MAS

71

over the six weeks period. Also, results showed that the compliance rate with medication

regimen between geriatric patients with chronic illnesses that participated in the

intervention of weekly phone call and the administration of a MAS (intervention group)

versus those that geriatric patients with chronic illnesses that did not participate in the

intervention (non-intervention group) were significantly different. Specifically, geriatric

patients with chronic illnesses that participated in the intervention of weekly phone call

and the administration of a MAS have higher compliance rates with medication regimen

as compared to geriatric patients with chronic illnesses that did not participate in the

intervention.

In the following chapter, Chapter five concludes this study. 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.

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

72

medical conditions, and greater healthcare costs. Up to 10% of hospital readmissions

have been attributed to non-adherence. Previous investigators 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 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

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 HBM theoretical framework, was used to guide the

73

interpretation and implications of the findings. Findings from previous projects will be

juxtaposed with the present project 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

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

74

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 project 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

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 projects 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

75

(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 project 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.

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,

76

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 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

77

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. This project 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 knowledge in the field of nursing.

78

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 projects should also examine and other adherence

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,

79

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.

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

80

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 studies 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 project, 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

results.

The results of the project 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

81

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 project’s

findings thus show support for cost-effective interventions to provide informational,

educational, and psychological support for geriatric patients in terms of medication

adherence.

82

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Appendix A

GCU IRB Letter of Approval

105

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

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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 SON-HillBone@jhu.edu  if you have any follow-up questions.