Research Paper Editing and making required corrections Assignment | Cheap Essay Help

I will attach my research paper with annotations of corrections that need to be made. Please make all needed corrections to the paper. These will not be a lot of writing to be done only formatting and maybe a page or two to be added in addition to the required corrections.


Don't use plagiarized sources. Get Your Custom Essay on
Research Paper Editing and making required corrections Assignment | Cheap Essay Help
Just from $13/Page
Order Essay

Improving Medication Compliance in Geriatric Patients

Submitted by







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.





Improving Medication Compliance in Geriatric Patients



Eva M. Jacques



Has been approved


May 27, 2020



Tabitha Garbart, DNP, DPI ProjectChairperson

Hubert Cantave, MD, Committee Member




Lisa Smith, PhD, RN, CNE

Dean and Professor, College of Nursing and Health Care Professions





Many geriatric patients suffer from one or more chronic diseases andthe 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 wasn=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 distributionwhich means statically there was significant improvement in compliance. Itis 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; MedicationCompliance; Noncompliance.



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.



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 neededallowing 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.




Table of Contents

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

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

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



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




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






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 witha 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.

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 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 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, 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).



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 by data analysis from the MAS, the comparison of pre- and post-intervention, andalso 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 anHB-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 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 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 anHB-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 thedegree 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 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 wereof 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 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 theHB-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 weeklyHB-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 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 tolimitations 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. Thesepatients 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).

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.




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 anHB-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 anHB-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 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.


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.

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.

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

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);
  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-efficacythe confidence and conviction that an individual can successfully finish the behavior or action of interest regardless of the considered obstaclesshould 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 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 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 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 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.

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 care of the chronically ill patient, especially if they have a condition affecting their mental health (El-Saifi et al., 2019).


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 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 bein distressemotionally.

Utilization of mobile technology to enhance adherence.

According to Braun et al. (2013), the concept of mobile technology is the type of technology using cellular communication. Healthcare providers can use technology to provide care and monitor patients at a distance. Technology has many uses in medicine, evolving and changing the dimensions of the delivery of care. The accessibility factor makes a significant impact on health care. The use of technology in health care aid with patient monitoring, decrease unnecessary office visits, and decrease hospitalizations, as further described by Braun et al. (2013).


Providers can call patients using their mobile phones to check if patients have any questions or problems. In a study by Lyons et al. (2016), patients with chronic illness were enrolled in a program with access to two telephone conversations after 1 month or 6 weeks where they talked to a pharmacist. After 6 months, the findings showed that those who had the telephone conversation had an adherence rate of at least 90%, whereas those in the control group had an adherence rate of 19.6%. These findings indicated that having a telephone conversation with a chronically ill patient could significantly increase their chances of adherence. However, the content of the telephone conversations did not necessarily focus on adherence, but the conversations were tailored to the individual needs of the patient (Turner et al., 2016). Having patient-centered care should be the goal, even when the use of mobile phones is introduced. The provider should have the etiquette required when asking the patient about their personal information (Haase et al., 2017).

Calling patients is an expression of care. Central to the role of nursing is patient care, which should be pursued through all possible means (Delaney, 2018). Providers can partner with their patients and build a professional relationship to enhance care using mobile phone conversations. To ensure the objectivity of the conversation, the provider should have a guide. Delaney (2018) suggested the MAS be used so that the conversation would be focused on adherence. Objectivity enhances respect and ensures that the right boundary is set to enhance a professional relationship between the provider and the patient (Steele et al., 2016). Providers should support patients, especially those patients at risk of psychological issues, such as stress from their conditions. Such care ensures that the patients have follow-ups and reminders to remain committed to taking medications as prescribed, as opined by Watkins et al. (2018).

Cellular phones are already accessible in the United States. The rapid penetration of devices has transformed the population of the United States, such that even the seniors have smartphones (Watkins et al., 2018). Indeed, up to 59% of people aged 65 to 69 years have smartphones; the percentage of smartphone owners for those in the age bracket of 70 to 74 is 49% (Subramanyam et al., 2018). Besides, those who do not have wireless phones have landlines and other analog phones, which can still be used for conversations regarding their health (Boulos et al., 2011). The importance of smartphones and other wireless phones is that phones are portable; therefore, the owners can be found easily through a phone call. The availability of mobile phones, which provide accessibly to the patients, will aid in the successful implementation and completion of the DPI project, given that the patients will only have to use their preferred phones at the time of calling.

Smartphone use to increase medication adherence.

 In the United States, more than half of adults over 65 years old take at least three to four medications daily to treat chronic conditions and age-related changes in physical and emotional health (Sanders, 2013). Park, Howeie-Esqivel, and Dracup (2014) conducted a systematic quantitative review without meta-analysis for prevention purposes, as well as the management of acute and chronic illnesses. The researchers found that the use of text messaging would significantly improve medication adherence. Data collection consisted of a literature search of 29 quantitative research studies related to mobile phones and medication adherence. Although there was a significant improvement in medication adherence, it was suggested that long-term studies characterized by rigorous research methodologies, appropriate statistical and economic analyses, and the test of theory-based interventions are needed to determine the efficacy of mobile phones to influence medication adherence (Park et al., 2014).

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 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).


Medication adherence, especially among seniors (people aged 65 years and above), is poor. Yet, this population has an increased risk of getting multiple chronic diseases compared to younger people. Nonadherence has negative impacts ranging from increased hospitalization and deaths, proneness to opportunistic diseases and worsening of symptoms, and elevated costs of treatment. Given that the population throughout the globe is aging, the issue of non-adherence is a major concern that should be addressed.

Many theories have been applied in handling the issue of non-adherence, the investigator has found the HBM as the most effective.Theorists have postulated that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, and self-efficacy (C. L. Jones et al., 2016), thus finding the barriers to noncompliance can help increase compliance through discussion, clarification, and patient education.

Several factors can negatively affect compliance with medication. Such factors can be economical, making acquiring the medication unaffordable. Another factor is emotional fatigue resulting from taking many drugs almost daily, dealing with the side effects of those drugs, and lack of supportive relationships. A weekly call and completing a MAS exude caring, concern, empathy, and support.

It is expected that the group with the intervention will show a significant change in that their rates of adherence which will lead to positive health outcomes. The next 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.








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.

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.



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 anHB-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 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 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 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

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 populationan 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 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 werefor 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:

In the equation,  is the sample size required, and were 30, ,  are constants with regards to accepted . On the other hand, the Z1-, β, Z are also representative of constants reliant on the power of the study. The  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:

= 30

The investigator predicted the following two potential outcomes of the study:

the administration of a medication adherence scale and those who do not.

InstrumentationorSources 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 of20 and the lowest of 5. The interventional group n=30 received a phone call and completed theHB-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 theHB-MASfor both groups will show an increase in compliance rates of all participants enrolled in the weekly phone calls and the administration of anHB-MAS program.  Given the sample for the current study, the administration of theHB-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 theHB-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 consistency and validity, which can ensure that the actual variables of the study are, measured (Van den Broucke et al., 2011).


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 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 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 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 anHB-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.

AnHB-MASwas collected from all 60 participants at the beginning of the project.  The completedHB-MASs were stored in a locked drawer to prevent unauthorized events. Over the consecutive 6 weeks, a phone call was placed and anHB-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, anHB-MASwas 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 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 includedthe 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 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 anHB-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 anHB-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 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 .

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 theInstitutional 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 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 theparticipants, 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.


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. 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.


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; 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-MASwas given to collect data at the beginning of the program. At the end of the six weeks, anHB-MASwas 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.




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 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 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.

Figure1. Participants’ Gender


Figure 2. Number of Chronic Illnesses of Participants






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 ofHB-MASincrease compliance in geriatric patients?  The second clinical question was: What was the relationship between a weekly phone call and the administration of anHB-MAS in medication compliance in noncompliant geriatric patients?

The questionnaire was developed to establish valid scientific outcomes, hence when developing the questionnaires, consideration to achieve accuracy and trustworthiness was made. The development considered high inter-item correlations and a Cronbach’s reliability value of at least 0.70. The quantitative method was used along with the SPSS to analyze the data.

Utilizing the correlational project design, the degree of compliance pre-and-post-intervention was compared and barriers to compliance were identified.     The data was collected over a period of six weeks. Some of the descriptive data that were used are age, gender, number of chronic diseases, and name of chronic diseases.  Inferential statistical analysis was used, utilizing the statistical software of SPSS, and a t-test was performed to determine statistical significance. The use of a t-test was appropriate because the focus of the project was to compare pre and post-test compliance data in geriatric patients.


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.

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)

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


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 statesthat 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 outlineassumption was satisfied.


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.

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-valuegreater 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

Period Group Shapiro-Wilk
Statistic df p
Pre-test (week 1) Non-intervention 0.95 30 0.14
Intervention 0.95 30 0.12
Post-test (week 6) Non-intervention 0.94 30 0.11
Intervention 0.85 30 0.00


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.

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-valuegreater 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 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  

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 illnesseswere 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.

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 wasa 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 illnessesthat 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

Source Type III Sum of Squares df Mean Square F 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
Error 746.68 58 12.87      

*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, < 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.

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 wasa 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

Source time Type III Sum of Squares df Mean Square F p Partial Eta Squared
time Linear 102.68 1 102.68 11.51 0.001* 0.17
time * Group Linear 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



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 over the six weeks period.  Also, results showed that the compliance rate with medication regimen between geriatric patients with chronic illnessesthat 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 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 anHB-MASto increase medication compliance.  These findingshave 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. TheHBM theoretical framework, was used to guide the 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 anHB-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 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 anHB-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 (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.



The findings of this study have implications for theory, practice, and the future. For theory, the findings supported the HBM, which served as the theoretical framework of the study. In terms of practice, findings show support for the use of weekly phone calls, and the administration of anHB-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 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.


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, 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 theirhealthcare 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 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.


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 anHB-MAS and it positively correlates with educational level, age, number of chronic diseases, and gender.  The results of this project showed an increase in compliance along with normalization vital signs and biomarkers. These findings implied that personalization was also important in providing intervention to geriatric patients, as each patient may have different needs. Weekly phone calls would allow healthcare professionals to provide personalized care at a low cost for patients who may not be able to be physically present in clinical or therapeutic interventions. The present 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.






Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211–217.

Anglada‐Martinez, H., Riu‐Viladoms, G., Martin‐Conde, M., Rovira‐Illamola, M. Sotoca‐Momblona, J. M., & Codina‐Jane, C. (2015). Does mHealth increase adherence to medication? Results of a systematic review. International Journal of Clinical Practice, 69(1), 9–32.

Baktash, M. Q., & Naji, A. B. (2019). Efficacy of health belief model in enhancing exercise behavior to preventing stroke among geriatrics homes residents in Baghdad City. Indian Journal of Public Health Research & Development10(2), 928–933.

Balkrishnan, R. (2005). The importance of medication adherence in improving chronic-disease related outcomes: What we know and what we need to further know. Medical Care, 43(6), 517–520.

Batsis, J. A., Germain, C. M., Vásquez, E., & Bartels, S. J. (2016). Prevalence of weakness and its relationship with limitations based on the Foundations for the National Institutes for Health project: Data from the Health and Retirement Study. European Journal of Clinical Nutrition, 70(10), 1168–1173.

Bazargan, M., Smith, J., Yazdanshenas, H., Movassaghi, M., Martins, D., &, Orum, G. (2017). Non-adherence to medication regimens among older African-American adults. BioMed Central Geriatrics, 17(1), Article 163.

Becker, M. H. (1974). The health belief model and personal health behavior. Health Education Monographs 2(12), 324–508. Retrieved from

Bercier, M. L., & Maynard, B. R. (2015). Interventions for medication noncompliance: A systematic review. Research on Social Work Practice, 25(1), 81–89. Retrieved from

Blanca, M., Alarcon, R., Arnau, J., Bono, R., & Bendayan, R. (2017). Non-normal data: Is ANOVA still a valid option?Psicothema, 29(4), 552-557. doi: 10.7334/psicothema2016.383

Boulos, M. N. K., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. Biomedical Engineering Online, 10(1), Article 24.

Brannen, J. (2017). Mixing methods: Qualitative and quantitative research. New York, NY: Routledge.

BrarPrayaga, R., Jeong, E., Feger, E., Noble, H., Kmiec, M., & Prayaga, R. (2018). Improving refill adherence in Medicare patients with tailored and interactive mobile text messaging: Pilot study. JMIR Mhealth and Uhealth, 6(1), e30.

Braun, R., Catalani, C., Wimbush, J., & Israelski, D. (2013). Community health workers and mobile technology: A systematic review of the literature. PloS One, 8(6), e65772.

Brown, M. T., & Bussell, J. K. (2011, April). Medication adherence: WHO cares? In Mayo clinic proceedings (Vol. 86, No. 4, pp. 304–314). New York, NY: Elsevier.

Bryman, A. (2017). Quantitative and qualitative research: Further reflections on their integration. In Mixing methods: Qualitative and quantitative research (pp. 57–78). New York, NY: Routledge.

Chang, K. C., Wang, J. D., Tang, H. P., Cheng, C. M., & Lin, C. Y. (2014). Psychometric evaluation, using Rasch analysis, of the WHOQOL-BREF in heroin-dependent people undergoing methadone maintenance treatment: Further item validation. Health and Quality of Life Outcomes, 12(1), Article 148.

Chia, L. R., Schlenk, E. A., & Dunbar-Jacob, J. (2006). Effect of personal and cultural beliefs on medication adherence in the elderly. Drugs & Aging, 23(3), 191–202.

Chisholm-Burns, M. A., & Spivey, C. A. (2012). The cost of medication nonadherence: Consequences we cannot afford to accept. Journal of the American Pharmacists Association, 52(6), 823–826.

Choi, A., Lovett, A. W., Kang, J., Lee, K., & Choi, L. (2015). Mobile applications to improve medication adherence: Existing apps, quality of life and future directions. Advances in Pharmacology and Pharmacy, 3(3), 64–74.

Chung, G. C., Marottoli, R. A., Cooney, L. M., Jr., & Rhee, T. G. (2019). Cost‐Related Medication nonadherence among older adults: Findings from a national representative sample. Journal of the American Geriatrics Society, 67(12), 2463–2473.

Costa, E., Giardini, A., Savin, M., Menditto, E., Lehane, E., Laosa, O., . . . Marengoni, A. (2015).Interventional tools to improve medication adherence: Review of literature. Patient Preference and Adherence, 9, 1303–1314.

Cutler, R., Fernandez-Llimos, F., Frommer, M., Benrimoj, C., & Garcia-Cardenas, V. (2018). Economic impact of medication non-adherence by disease groups: A systematic review. BMJ Open, 8(1), e016982.

Daniel, H., Christian, W., Robin, H., Lars, S., & Thomas, M. (2019). Statin treatment after acute coronary syndrome: Adherence and reasons for non-adherence in a randomized controlled intervention trial. Scientific Reports, 9(1). doi:10.1038/s41598-019-48540-3

Davy, C., Bleasel, J., Liu, H., Tchan, M., Ponniah, S., & Brown, A. (2015). Effectiveness of chronic care models: Opportunities for improving healthcare practice and health outcomes: A systematic review. BioMed Central Health Services Research, 15(1), Article 194.

Delaney, L. J. (2018). Patient-centered care as an approach to improving health care Australia. Collegian, 25(1), 119–123.

Dhillon, A. K., Hattingh, H. L., Stafford, A., & Hoti, K. (2015). General practitioners’ perceptions on home medicines reviews: A qualitative analysis. BioMed Central Family Practice16(1), Article 16.

Ellis-Barton, C. (2016). Ethical considerations in research participation virality. Journal of Empirical Research on Human Research Ethics11(3), 281–285.

El-Mallakh, P., & Findlay, J. (2015). Strategies to improve medication adherence in patients with schizophrenia: The role of support services. Neuropsychiatric Disease and Treatment, 11, 1077–1090.

El-Saifi, N., Moyle, W., Jones, C., & Alston-Knox, C. (2019). Determinants of medication adherence in older people with dementia from the caregivers’ perspective. International Psychogeriatrics31(3), 331–339.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

Fischer, M. A., Stedman, M. R., Lii, J., Vogeli, C., Shrank, W. H., Brookhart, M. A., & Weissman, J. S. (2010). Primary medication non-adherence: Analysis of 195,930 electronic prescriptions. Journal of General Internal Medicine, 25(4), 284–290.

Foot, H., La Caze, A., Gujral, G., & Cottrell, N. (2016). The necessity-concerns framework predicts adherence to medication in multiple illness conditions: A meta-analysis. Patient Education and Counseling, 99(5), 706–717.

Frances, A., Thirumoorthy, T., & Kwan, Y. H. (2016). Medication adherence in the elderly. Journal of Clinical Gerontology and Geriatrics, 7(2), 64–67. 2015.05.001

Jimmy, B., & Jose, J. (2011). Patient medication adherence: Measures in daily practice. Oman Medical Journal, 3, 155. Retrieved from

Jin, H., Kim, Y., &Rhie, S. J. (2016). Factors affecting medication adherence in elderly people. Patient Preference and Adherence,10, 2117–2125.

Haase, J., Farris, K. B., & Dorsch, M. P. (2017). Mobile applications to improve medication adherence. Telemedicine and e-Health, 23(2), 75–79.

Halcomb, E., & Hickman, L. (2015). Mixed methods research. Nursing Standard, 29(32), 41–47.

Hamza, S. A., El Akkad, R. M., Abdelrahman, E. E., & Abd Elghany, S. A. (2019). Non adherence to Antihypertensive medications among hypertensive elderly patients in outpatient geriatric clinic. The Egyptian Journal of Geriatrics and Gerontology, 6(1), 1-7. doi:10.21608/EJGG.2019.30888

Hicks, C. M. (2009). Research methods for clinical therapists e-book: Applied project design and analysis. New York, NY: Elsevier Health Sciences.

Holt, E. W., Rung, A. L., Leon, K. A., Firestein, C., & Krousel-Wood, M. (2014). Medication adherence in older adults: A qualitative study. Educational Gerontology, 40(3), 198–211.

Horváth, K. A., Ill, G., & Milánkovich, Á. (2017, July). Passive extended double-sided two-way ranging algorithm for UWB positioning. In 2017 Ninth International Conference on Ubiquitous and Future Networks (pp. 482–487).

Huang, D., Sangthong, R., McNeil, E., Chongsuvivatwong, V., Zheng, W., & Yang, X. (2013). Effects of a phone call intervention to promote adherence to antiretroviral therapy and quality of life of HIV/AIDS patients in Baoshan, China: A randomized controlled trial. AIDS Research and Treatment, 2013, 580974.

Hugtenburg, J. G., Timmers, L., Elders, P. J., Vervloet, M., & van Dijk, L. (2013). Definitions, variants, and causes of nonadherence with medication: A challenge for tailored interventions. Patient Preference and Adherence, 7, 675–682.

Iuga, A. O., & McGuire, M. J. (2014). Adherence and health care costs. Risk Management and Healthcare Policy, 7, 35–44.

Gabbay, U., Yosef, N., Feder-Krengel, N., & Meyerovitch, J. (2012). Therapeutic equivalent substitute that is new or unfamiliar to the chronic patient may result in medication error. International Journal of Health care Quality Assurance, 25(6), 509–518.

Gee, P. M., Greenwood, D. A., Paterniti, D. A., Ward, D., & Miller, L. M. S. (2015). The eHealth enhanced chronic care model: A theory derivation approach. Journal of Medical Internet Research, 17(4), e86.

Kadam, P., & Bhalerao, S. (2010). Sample size calculation. International Journal of Ayurveda Research, 1(1), 55–57. Retrieved from

Karakurt, P., & Kaşikçi, M. (2012). Factors affecting medication adherence in patients with hypertension. Journal of Vascular Nursing, 30(4), 118–126.

Kaur, S. P. (2013). Variables in research. Indian Journal of Research and Reports in Medical Sciences, 3(4), 36–38. Retrieved from

Kelly, P., Fitzsimons, C., & Baker, G. (2016). Should we reframe how we think about physical activity and sedentary behaviour measurement? Validity and reliability reconsidered. International Journal Of Behavioral Nutrition And Physical Activity, 13(1). doi: 10.1186/s12966-016-0351-4

Khorsandi, M., Fekrizadeh, Z., & Roozbahani, N. (2017). Investigation of the effect of education based on the health belief model on the adoption of hypertension-controlling behaviors in the elderly. Clinical Interventions in Aging12, 233–240.

Kim, J., Combs, K., Downs, J., & Tillman, F. (2018). Medication adherence: The elephant in the room. US Pharm, 43(1), 30-34. Retrieved from

Kokturk, N., Polatli, M., Oguzulgen, I. K., Saleemi, S., Al Ghobain, M., Khan, J., … & El Hasnaoui, A. (2018). Adherence to COPD treatment in Turkey and Saudi Arabia: results of the ADCARE study. International journal of chronic obstructive pulmonary disease, 13, 1377. doi:10.2147/COPD.S150411

Kim, M., Hill, M., Bone, L., & Levine, D. (2000). Development and testing of the hill-bone compliance to High Blood Pressure Therapy Scale. Progress in Cardiovascular Nursing, 15(3), 90–96.

Kucukdagli, P., Bahat, G., Bay, I., Kilic, C., Oren, M. M., Turkmen, B. O., & Karan, M. A. (2019). The relationship between common geriatric syndromes and potentially inappropriate medication use among older adults. Aging Clinical and Experimental Research, 2019, 1–7.

Kwan, A. (2012). Using mobile technologies for healthier aging. New York, NY: mHealth Alliance and Pfizer.

Lam, W. Y., & Fresco, P. (2015). Medication adherence measures: An overview. BioMed Research International, 2015, Article 217047.

Lamiani, G., Borghi, L., & Argentero, P. (2017). When healthcare professionals cannot do the right thing: A systematic review of moral distress and its correlates. Journal of Health Psychology, 22(1), 51–67.

Lau, K. P., & Adewumi, A. D. (2019). Awareness and use of medication management services in relation to medication adherence prior to hospitalization among older adults in Regional Australia. International Journal of Clinical Pharmacy41(1), 189–197.

Lee, S., Jiang, L., Dowdy, D., Hong, Y. A., & Ory, M. G. (2018). Attitudes, beliefs, and cost-related medication nonadherence among adults aged 65 or older with chronic diseases. Preventing Chronic Disease, 15, E148.

Lemanek, K. L., & Yardley, H. (2019). Noncompliance and Nonadherence. In Handbook of Cognitive Behavioral Therapy for Pediatric Medical Conditions (pp. 407-416). Springer, Cham.

Lemstra, M., Nwankwo, C., Bird, Y., & Moraros, J. (2018). Primary nonadherence to chronic disease medications: A meta-analysis. Patient Preference and Adherence, 12, 721–731.

Leung, L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of Family Medicine and Primary Care, 4(3), 324–327.

Li, T., Vedula, S. S., Hadar, N., Parkin, C., Lau, J., & Dickersin, K. (2015). Innovations in data collection, management, and archiving for systematic reviews. Annals of Internal Medicine, 162(4), 287–294. Retrieved from

Li, X., & Haupt, J. (2016). Robust low-complexity randomized methods for locating outliers in large matrices. Retrieved from

Lum, Z. K., Suministrado, M. S. P., Venketasubramanian, N., Ikram, M. K., & Chen, C. (2019). Medication compliance in Singaporean patients with Alzheimer’s disease. Singapore Medical Journal60(3), 154–169.

Luquis, R. R., & Kensinger, W. S. (2019). Applying the health belief model to assess prevention services among young adults. International Journal of Health Promotion and Education57(1), 37–47.

Lyons, I., Barber, N., Raynor, D. K., & Wei, L. (2016). The Medicines Advice Service Evaluation (MASE): A randomized controlled trial of a pharmacist-led telephone-based intervention designed to improve medication adherence. BMJ QualSaf, 25(10), 759–769.

Marcucci, M., Iorio, A., Nobili, A., Tettamanti, M., Pasina, L., Marengoni, A., . . . REPOSI Investigators. (2010). Factors affecting adherence to guidelines for antithrombotic therapy in elderly patients with atrial fibrillation admitted to internal medicine wards. European Journal of Internal Medicine, 21(6), 516–523.

Marcum, Z. A., Hanlon, J. T., & Murray, M. D. (2017). Improving medication adherence and health outcomes in older adults: An evidence-based review of randomized controlled trials. Drugs & Aging, 34(3), 191–201.

Mayeye, B. F., Ter Goon, D., & Yako, E. M. (2019). An Exploratory Study of the Determinants of Adherence to Antiretroviral Therapy by Adolescents and Youths in the Eastern Cape, South Africa. Global Journal of Health Science, 11(11). doi:10.5539/gjhs.v11n11p141

Mayo-G, T. L., & Mouton, C. (2017). Examining the association between health literacy and medication adherence among older adults. Journal of Health Communication, 33(9), 1124–1130.

Mazhar, F., Akram, S., Malhi, S. M., & Haider, N. (2018). A prevalence study of potentially inappropriate medications use in hospitalized Pakistani elderly. Aging Clinical and Experimental Research30(1), 53–60.

McMullen, C. K., Safford, M. M., Bosworth, H. B., Phansalkar, S., Leong, A., Fagan, M. B., . . . Brinkman, W. B. (2015). Patient-centered priorities for improving medication management and adherence. Patient Education and Counseling, 98(1), 102–110.

Melchiorre, M. G., Papa, R., Rijken, M., van Ginneken, E., Hujala, A., & Barbabella, F. (2018). eHealth in integrated care programs for people with multimorbidity in Europe: Insights from the ICARE4EU project. Health Policy, 122(1), 53–63.

Midão, L., Giardini, A., Menditto, E., Kardas, P., & Costa, E. (2017). Adherence to medication in older adults as a way to improve health outcomes and reduce healthcare system spending. In G. D’Onofrio, D. Sancarlo, & A. Greco (Eds.), Gerontology (pp. 25–40).

Miller, T. A. (2016). Health literacy and adherence to medical treatment in chronic and acute illness: A meta-analysis. Patient Education and Counseling, 99(7), 1079-1086.

Mirhoseni, S. J., Mazloomy, S., & Moqaddasi Amiri, M. (2019). The study of blood pressure related factors based on health belief model in Yazd in 2017. Tolooebehdasht18(4), 107–119. Retrieved from

Mitchell, T. R. (1985). An evaluation of the validity of correlational research conducted in organizations. Academy of Management Review, 10(2),192–205.

Morisky, D. E., Ang, A., Krousel-Wood, M., & Ward, H. (2018). Predictive validity of a medication adherence measure in an outpatient setting. Journal of Clinical Hypertension, 10(5), 348–354.

Morrissey, E. C., Casey, M., Glynn, L. G., Walsh, J. C., & Molloy, G. J. (2018). Smartphone apps for improving medication adherence in hypertension: Patients’ perspectives. Patient Preference and Adherence, 12, 813–822.

Nguyen, T., La Caze, A., & Cottrell, N. (2016). Validated adherence scales used in a measurement-guided medication management approach to target and tailor a medication adherence intervention: A randomized controlled trial. BMJ Open, 6(11), e013375.

O’Quinn, K. E., Semalulu, T., & Orom, H. (2015). Elder and caregiver solutions to improve medication adherence. Health Education Research, 30(2), 323–335.

Pagès-Puigdemont, N., Mangues, M. A., Masip, M., Gabriele, G., Fernández-Maldonado, L., Blancafort, S., & Tuneu, L. (2016). Patients’ perspective of medication adherence in chronic conditions: A qualitative study. Advances in Therapy, 33(10), 1740–1754.

Pandis, N. (2016). Linear regression. American Journal of Orthodontics and Dentofacial Orthopedics, 149(3), 431–434.

Park, L., Howie-Esquivel, J., & Dracup, K. (2014). A quantitative systematic review of the efficacy of mobile phone interventions to improve medication adherence. Journal of Advanced Nursing, 70(9), 1932–1953.

Patton, D. E., Hughes, C. M., Cadogan, C. A., & Ryan, C. A. (2017). Theory-based interventions to improve medication adherence in older adults prescribed polypharmacy: A systematic review. Drugs & Aging, 34(2), 97–113.

Pednekar, P. (2019). The impact of medication adherence on healthcare utilization and costs among elderly patients with diabetes who were enrolled in a state pharmaceutical assistance program (Doctoral dissertation, University of the Sciences in Philadelphia). Retrieved from ProQuest Dissertations. (UMI No. 13856162)

Piette, J. D., Beard, A., Rosland, A. M., & McHorney, C. A. (2011). Beliefs that influence cost-related medication non-adherence among the “haves” and “have nots” with chronic diseases. Patient Preference and Adherence, 5, 389–396.

Ponto, J. (2015). Understanding and evaluating survey research. Journal of the Advanced Practitioner in Oncology6(2), 168–171. Retrieved from

Prayaga, R. B., Jeong, E. W., Feger, E., Noble, H. K., Kmiec, M., & Prayaga, R. S. (2018).Improving refill adherence in Medicare patients with tailored and interactive mobile text messaging: Pilot study. JMIR mHealth and uHealth, 6(1), e30.

Provost, S. M., Lanham, H. J., Leykum, L. K., McDaniel, R. R., Jr., & Pugh, J. (2015). Health care huddles: Managing complexity to achieve high reliability. Health Care Management Review, 40(1), 2–12.

Qato, D. M., Wilder, J., Schumm, L. P., Gillet, V., & Alexander, G. C. (2016). Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the United States, 2005 vs 2011. JAMA Internal Medicine, 176(4), 473–482.

Raghupathi, W., & Raghupathi, V. (2018). An empirical study of chronic diseases in the United States: A visual analytics approach. International Journal of Environmental Research and Public Health, 15(3), 431.

Rasu, R. S., Bawa, W. A., Suminski, R., Snella, K., & Warady, B. (2015). Health literacy impact on national healthcare utilization and expenditure. International Journal of Health Policy and Management, 4(11), 747–755.

Redston, M. R., Hilmer, S. N., McLachlan, A. J., Clough, A. J., & Gnjidic, D. (2018). Prevalence of potentially inappropriate medication use in older inpatients with and without cognitive impairment: A systematic review. Journal of Alzheimer’s Disease, 61(4), 1639–1652.

Rivera, S. C., Kyte, D. G., Aiyegbusi, O. L., Keeley, T. J., & Calvert, M. J. (2017). Assessing the impact of healthcare research: A systematic review of methodological frameworks. PLoS Medicine, 14(8), e1002370.

Rochon, P. A. (2019). Drug prescribing for older adults. UpToDate. Retrieved from

Rocque, G. B., Pisu, M., Jackson, B. E., Kvale, E. A., Demark-Wahnefried, W., Martin, M. Y., . . . Williams, C. P. (2017). Resource use and Medicare costs during lay navigation for geriatric patients with cancer. JAMA Oncology, 3(6), 817–825.

Rosenstock, I. M. (1990). The health belief model: Explaining health behavior through expectancies. In K. Glanz, F. M. Lewis, & B. K. Rimer (Eds.), The Jossey-Bass health series. Health behavior and health education: Theory, research, and practice (p. 39–62). Jossey-Bass.

Rubin, R. (2019). Synchronizing medication refills could improve adherence. JAMA, 322(8), 716–716.

Saragosa M., Carbone, S., King, M., Poole, M., Peckham, A., Allin, S., & Marchildon, G. (2020). Using Technology to Support Home and Community Care in Rural Areas. Toronto: North American Observatory on Health Systems and Policies. Rapid Review, 20. Retrieved from

ShafieeHanjani, L., Apostolidis, A., Winckel, K., & Burrows, J. A. (2017). An evaluation of the utilization of the Medication Action Plan form in a tertiary hospital: What is the impact of the pharmacist? Journal of Pharmacy Practice and Research, 47(4), 257–262.

Shruthi, R., Jyothi, R., Pundarikaksha, H. P., Nagesh, G. N., & Tushar, T. J. (2016). A study of medication compliance in geriatric patients with chronic illnesses at a tertiary care hospital. Journal of Clinical and Diagnostic Research: JCDR, 10(12), FC40–FC43.

Siddiqui, A., Siddiqui, A. S., Jawaid, M., & Zaman, K. A. (2017). Physician’s perception versus patient’s actual incidence of drug non-adherence in chronic illnesses. Cureus, 9(11), e1893.

Simon, M. K., & Goes, J. (2013). Assumptions, limitations, delimitations, and scope of the study. Retrieved from http://www.dissertation

Sinan, O., & Akyuz, A. (2019). Effects of home visits on medication adherence of elderly individuals with diabetes and hypertension. Eastern Journal of Medicine, 24(1), 8–14.

Siu, H. Y. H., Delleman, B., Langevin, J., Mangin, D., Howard, M., Fang, Q., . . . Chan, D. (2019). Demonstrating a technology-mediated intervention to support medication adherence in community-dwelling older adults in primary care: A feasibility study. Gerontology and Geriatric Medicine5, 2333721419845179.

Smith, D., Lovell, J., Weller, C., Kennedy, B., Winbolt, M., Young, C., & Ibrahim, J. (2017). A systematic review of medication nonadherence in persons with dementia or cognitive impairment. PLoS ONE, 12(2), 1–19.

Soones, T. N., Lin, J. L., Wolf, M. S., O’Conor, R., Martynenko, M., Wisnivesky, J. P., & Federman, A. D. (2017). Pathways linking health literacy, health beliefs, and cognition to medication adherence in older adults with asthma. Journal of Allergy and Clinical Immunology, 139(3), 804–809. doi:10.1016/j.jaci.2016.05.043

Steele, K. M., Ruisinger, J. F., Bates, J., Prohaska, E. S., Melton, B. L., & Hipp, S. (2016). Home-based comprehensive medication reviews: Pharmacist’s impact on drug therapy problems in geriatric patients. The Consultant Pharmacist®, 31(10), 598–605.

Subramanyam, A. A., Singh, S., & Raut, N. B. (2018). Mobile phone use in the elderly: Boon or bane? Journal of Geriatric Mental Health, 5(2), 81.

Sultan, M. (2016). Informing the design of a smartphone-based telemonitoring system for multiple chronic conditions (Doctoral dissertation). Retrieved from

Tabachnick, B. G. & Fidell, L. S. (2013). Using multivariate statistics (6th ed.) Boston, MA: Pearson.

Thakkar, J., Kurup, R., Laba, T. L., Santo, K., Thiagalingam, A., Rodgers, A., & Chow, C. K. (2016). Mobile telephone text messaging for medication adherence in chronic disease: A meta-analysis. JAMA Internal Medicine, 176(3), 340–349.

Theofanidis, D., &Fountouki, A. (2018). Limitations and delimitations in the research process. Perioperative Nursing, 7(3), 155–163.

Toll, B. A., McKee, S. A., Martin, D. J., Jatlow, P., & O’Malley, S. S. (2007). Factor structure and validity of the Medication Adherence Questionnaire (MAQ) with cigarette smokers trying to quit. nicotine & tobacco research. Official Journal of the Society for Research on Nicotine and Tobacco, 9(5),597–605.

Tse, G., Chan, C., Gong, M., Meng, L., Zhang, J., Su, X. L., … & Zhao, J. C. (2018). Telemonitoring and hemodynamic monitoring to reduce hospitalization rates in heart failure: a systematic review and meta-analysis of randomized controlled trials and real-world studies. Journal of geriatric cardiology: JGC, 15(4), 298. doi:10.11909/j.issn.1671-5411.2018.04.008

Turner, A. P., Roubinov, D. S., Atkins, D. C., & Haselkorn, J. K. (2016). Predicting medication adherence in multiple sclerosis using telephone-based home monitoring. Disability and Health Journal, 9(1), 83–89.

Ueno, H., Yamazaki, Y., Yonekura, Y., Park, M. J., Ishikawa, H., & Kiuchi, T. (2018). Reliability and validity of a 12-item medication adherence scale for patients with chronic disease in Japan. BioMed Central Health Services Research, 18(1), Article 592.

Unutmaz, G. D., Soysal, P., Tuven, B., & Isik, A. T. (2018). Costs of medication in older patients: Before and after comprehensive geriatric assessment. Clinical Interventions in Aging13(2), 607–622.

Van Boven, J. F. M., Cushen, B., Sulaiman, I., Greene, G., MacHale, E., Mokoka, M. C., … Costello, R. W. (2018). Personalising adherence-enhancing interventions using a smart inhaler in patients with COPD: an exploratory cost-effectiveness analysis. Npj Primary Care Respiratory Medicine, 28(1). doi:10.1038/s41533-018-0092-8

Van den Broucke, S., Dargent, G., & Pletschette, M. (2011). Development and assessment of criteria to select projects for funding in the EU health programme. The European Journal of Public Health22(4), 598–601.

Verloo, H., Chiolero, A., Kiszio, B., Kampel, T., & Santschi, V. (2017). Nurse interventions to improve medication adherence among discharged older adults: A systematic review. Age & Ageing, 46(5), 747–754.

Wamala, S., Merlo, J., Bostrom, G., Hogstedt, C., & Agren, G. (2007). Socioeconomic disadvantage and primary non-adherence with medication in Sweden. International Journal for Quality in Health Care, 19(3), 134–140.

Watson, R. (2015). Quantitative research. Nursing Standard, 29(31).

Yang, P., Stankevicius, D., Marozas, V., Deng, Z., Liu, E., Lukosevicius, A., . . . Min, G. (2016). Lifelogging data validation model for internet of things enabled personalized healthcare. Institute of Electrical and Electronics EngineersTransactions on Systems, Man, and Cybernetics: Systems, 48(1), 50–64.

Yap, A. F., Thirumoorthy, T., & Kwan, Y. H. (2016). Medication adherence in the elderly. Journal of Clinical Gerontology and Geriatrics, 7(2), 64–67.

Yap, A. F., Thirumoorthy, T., & Kwan, Y. H. (2016). Systematic review of the barriers affecting medication adherence in older adults. Geriatrics & Gerontology International, 16(10), 1093–1101.

Yazdanpanah, Y., Saleh Moghadam, A. R., Mazlom, S. R., Haji Ali Beigloo, R., & Mohajer, S. (2019). Effect of an educational program based on health belief model on medication adherence in elderly patients with hypertension. Evidence Based Care, 9(1), 52–62. Retrieved from

Yeon Park, H., Ae Seo, S., Yoo, H., & Lee, K. (2018). Medication adherence and beliefs about medication in elderly patients living alone with chronic diseases. Patient Preference and Adherence, 12, 175–181.

Yip, C., Han, N. R., & Sng, B. L. (2016). Legal and ethical issues in research. Indian Journal of Anesthesia, 60(9), 684–688. doi:10.4103/0019-5049.190627









Appendix A

GCU IRB Letter of Approval



Appendix B

















Appendix C

Permission Letter

Place Order
Grab A 14% Discount on This Paper
Pages (550 words)
Approximate price: -
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Try it now!

Grab A 14% Discount on This Paper

Total price:

How it works?

Follow these simple steps to get your paper done

Place your order

Fill in the order form and provide all details of your assignment.

Proceed with the payment

Choose the payment system that suits you most.

Receive the final file

Once your paper is ready, we will email it to you.