Please use the attached DNP-DPI Project and Sample PowerPoint to complete this assignment

In this topic, you will participate with your full DPI Committee in the DPI Completed Project – Defense PowerPoint and Call. This meeting requires that you present your finalized DPI Project in PowerPoint form.

General Requirements:

Use the following information to ensure successful completion of the assignment:

· Remember to use the appropriate forms and templates (if required) for completing this assignment. These are available in the DNP PI Workspace in the DC Network.

· Locate the “Preparing for Your Final Direct Practice Improvement Project Defense” resource in the DNP-965 folder of the DNP PI Workspace of the DC Network.

· Locate the “DNP-965 Final Defense PowerPoint Template,” located in the DNP-965 folder in the DNP PI Workspace of the DC Network. 

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· Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.

· This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

· You are not required to submit this assignment to LopesWrite.

Directions:

Completing the Benchmark – DPI Completed Project – Defense PowerPoint and Call is the required deliverable to progress through the DPI Project implementation and completion phases.

1. Use the “DNP-965 Final Defense PowerPoint Template,” located in the DNP-965 folder of the DNP PI Workspace in the DC Network, to create a PowerPoint presentation of your Final DPI Completed Project to be used during your DPI Completed Project – Defense PowerPoint and Call.

2. Attach a copy of the potential poster presentation concept for your DPI Project as per the format you and your DPI chairperson predetermined; the Benchmark DPI Completed Project – Defense PowerPoint and Call submission is incomplete without this element.

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Improving Medication Adherence among Type II Home Healthcare Diabetic Patients

Submitted by

Bola Odusola-Stephen

A Direct Practice Improvement Project Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Nursing Practice

Grand Canyon University

Phoenix, Arizona

April 6, 2021

© Bola Odusola-Stephen, 2020

All rights reserved.

GRAND CANYON UNIVERSITY

Improving Medication Adherence among Type II Home Healthcare Diabetic Patients

by

Bola Odusola-Stephen

has been approved.

April 6, 2021

APPROVED:

            Maria Thomas., DNP., DPI Project Chairperson

Bamidele Jokodola., DNP., DPI Project Mentor

ACCEPTED AND SIGNED:

________________________________________

Lisa Smith, PhD, RN, CNE

Dean and Professor, College of Nursing and Health Care Professions

_________________________________________

Date

Abstract

Medication adherence is essential in controlling chronic health conditions such as Type II diabetes in home health patients. At the project site, there was no standardized process for identifying and addressing the patient’s medication adherence. The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks. The nursing theory and change model that will guide this project is Orem’s Self-Care Deficit Theory and Diffusion of Innovation Model. Data extrapolated from Cradle Solutions and analyzed utilizing a chi-square analysis to determine the statistical significance. The clinical significance could be noted with the nurses using the tool consistently and performing the medication adherence screenings on each visit to help the patient remain compliant. The findings suggested that implementing the medication adherence program could improve patient compliance rates.  A future recommendation is to conduct the project using larger populations of home health patients for a longer timeframe.

Keywords: diabetes mellitus type II, Diffusion of innovation model, home-based care, medication adherence, MAP resources, Orem’s self-care deficit theory

Dedication

An optional dedication may be included here. While a practice improvement project is an objective, scientific document, this is the place to use the first person and to be subjective. The dedication page is numbered with a Roman numeral, but the page number does not appear in the Table of Contents. It is only included in the final practice improvement project and is not part of the proposal. If this page is not to be included, delete the heading, the body text, and the page break below.

Acknowledgments

An optional acknowledgements page can be included here. This is another place to use the first person. If it applies, acknowledge and identify grants and other means of financial support. Also acknowledge supportive colleagues who rendered assistance. The acknowledgments page is numbered with a Roman numeral, but the page number does not appear in the Table of Contents. This page provides a formal opportunity to thank family, friends, and faculty members who have been helpful and supportive. The acknowledgements page is only included in the final practice improvement project and is not part of the proposal. If this page is not to be included, delete the heading, the body text, and the page break below. If you cannot see the page break, click on the Show/Hide button (go to the Home tab and then to the Paragraph toolbar).

Table of Contents

Chapter 1: Introduction to the Project 1

Background of the Project 2

Problem Statement 4

Purpose of the Project 5

Clinical Question(s) 7

Advancing Scientific Knowledge. 7

Significance of the Project 9

Rationale for Methodology. 10

Nature of the Project Design. 11

Definition of Terms. 13

Assumptions, Limitations, Delimitations. 14

Summary and Organization of the Remainder of the Project 15

Chapter 2: Literature Review.. 18

Theoretical Foundations. 20

Review of the Literature. 21

Theme 1. 22

Theme 2. 26

Summary. 29

Chapter 3: Methodology. 31

Statement of the Problem.. 32

Clinical Question. 33

Project Methodology. 34

Project Design. 34

Population and Sample Selection. 37

Instrumentation or Sources of Data  39

Validity. 40

Reliability. 40

Data Collection Procedures. 41

Data Analysis Procedures. 42

Potential Bias and Mitigation. 43

Ethical Considerations. 43

Limitations. 44

Summary. 45

Chapter 4: Data Analysis and Results. 68

Descriptive Data. 69

Data Analysis Procedures. 72

Results. 73

Summary. 79

Chapter 5: Summary, Conclusions, and Recommendations. 81

Summary of the Project 82

Summary of Findings and Conclusion. 83

Implications. 85

Theoretical Implications. 85

Practical Implications. 85

Future Implications. 85

Recommendations. 86

Recommendations for Future Projects. 87

Recommendations for Practice. 88

References. 90

Appendix A.. 92

Appendix B.. 94

What is my DPI project design?. 94

Appendix C.. 96

Power Analysis Using G Power 96

Appendix D.. 97

Example SPSS Dataset & Variable View.. 97

List of Tables

Table 1. Characteristics of Variables. 42

Table 2. Type of Methodology and Rationale for Selecting It 45

Table 3. A Sample Data Table Showing Correct Formatting. 71

Table 4. t-Test for Equality of Emotional Intelligence Mean Scores by Gender 75

List of Figures

Figure 1. Approaches to Collecting the Data to Answer the Clinical Questions. 43

Figure 2. Parametric Statistics for Analysis of Ratio or Interval Level Dependent  Variable. 58

Figure 3. Non-Parametric Statistics for Analysis of Nominal or Ordinal Level Dependent Variable  59

Chapter 1: Introduction to the Project

According to the Centers for Disease Control and Prevention (2020), diabetes impacts one in ten Americans. Furthermore, the prevalence of diabetes continues to rise and is projected to increase by 0.3% per year until 2030 (Lin et al., 2018). For individuals with Type II diabetes, proper and effective medication adherence is critical (Kvarnström et al., 2018). This is particularly significant among healthcare patients because diabetes is one of the leading diagnoses for admission into a home health care facility (Sertbas et al., 2019). In this population, approximately 45% of the patients fail to maintain glycemic control (HgbA1c < 7%) (Polonsky & Henry, 2016).   Poor medication adherence is linked with increased morbidity and mortality rates, increased financial expenses for the patient, hospital, and insurance companies, frequent hospitalizations, and lower quality of life (Polonsky & Henry, 2016).

At the project site, the primary investigator, in collaboration with the stakeholders, noted that the healthcare providers documented ten percent of the patients were not adhering to their medication regimen. This prompted frequent hospitalizations, infections, and other diabetic complications. In further investigation, it was found that there was not a standardized method for the healthcare providers to evaluate the patients regarding medication adherence. Hence, the introduction of the MAP resources and education intervention will be implemented.

            The project is worth conducting because the primary investigator focuses on diabetic home health patients who are not the focal point of many literature reviews. Furthermore, little information is noted regarding the impact the healthcare team plays in addressing this population’s lack of medication adherence. The primary investigator aims to introduce a standardized method of addressing patient’s medication adherence using the MAP resources and education to minimize frequent hospitalizations, infections and increase their quality of life (Starr & Sacks, 2010). For this project’s purpose, the primary investigator (PI) will examine the impact/role healthcare team members play in addressing patient-related factors that affect medication adherence among home healthcare diabetic patients.

Chapter 1 introduces the project, background, and problem statements. Other segments include the purpose of the project, clinical question, advancing scientific knowledge, and project significance. The last sections consist of the rationale for using a quantitative method and quasi-experimental design, definition of operational terms, assumptions, limitations, and delimitations. The last few sentences are transitional ones providing a preview into Chapter 2.

Background of the Project

Home-based healthcare has existed since 1909 (Choi et al., 2019).  Present-day, home-based healthcare is often selected due to an individual’s personal preferences. While home-based healthcare is not appropriate for all patients, Szanton et al. (2016) noted that this care option is best when an individual’s condition can be managed without admission to a hospital. Patients who have diabetes or hypertension are often recipients of home-based healthcare (Wong et al., 2020).

Adhering to diabetes medication regimen requirements can be complex. Raoufi et al. (2018) conducted a study using a multi-stage stratified cluster sampling method to recruit its participants. Two thousand one-hundred eight three diabetic patients participated in the study. Of the participants, 51.4% tested their glucose level more than once a month (Raoufi et al., 2018). The authors also noted that 10% of the participants did not monitor the glucose levels correctly or adhere to the medication requirements.

Patients with diabetes often express difficulties adhering to medication regimens, thereby reinforcing the critical role of receiving education from home healthcare providers (Wong et al., 2020). This is in part to the patients not having sufficient knowledge and education regarding diabetes and proper management of the disease (Wong et al., 2020). With diabetes being one of the leading diagnoses for patients needing home health services, healthcare agencies must educate their staff to evaluate the factors prohibiting patients from adhering to their medication regimen.

Problem Statement

It is not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas. The population affected are home health Type II diabetic patients in an urban healthcare agency in Texas. At the project site, nursing administration and staff cited that medication adherence among diabetic patients is lacking. According to data obtained from the site’s electronic health record (EHR), home healthcare providers documented that ten percent of diabetic home healthcare patients are not adhering to their medication regimen. Although this percentage four to six percent lower than other percentages cited in the literature for medication non-adherence, in terms of chronic disease management, various researchers have noted the implications associated with lacking adherence to medication regimens (Camacho et al., 2020; Hamrahian, 2020; Misquitta, 2020).

The lack of medication adherence can be attributed to inadequate drug-related knowledge, medication costs, poor understanding of medication regimen, etc., reinforcing the need for this quality improvement project (Heath, 2019; Sharma et al., 2020). Kvarnström et al. (2017) emphasized healthcare providers play a critical role in ensuring medication adherence. To promote medication adherence among patients of a home healthcare facility, the primary investigator will introduce a standardized method for the healthcare providers to assess the patient’s medication adherence. The staff will achieve greater insight by using MAP resources and an education intervention created by Starr and Sacks (2010). The tools utilized in this study, which are from Starr and Sacks’s (2010) MAP Toolkit and Training Guide resources, include: (1) the questions to ask poster, (2) an adherence assessment pad, and (3) my medications list.

The project contributes to solving the problem by introducing a standardized method of evaluating the patient’s medication adherence. It will improve the healthcare provider’s knowledge and awareness regarding the obstacles or factors the patient may face in maintaining a medication regimen. This would help the facility adhere to the current Centers for Disease Control and Prevention (2020a) guidelines in the participants maintaining their normal daily glucose levels, deter healthcare costs, frequent hospitalizations, and infections.

Purpose of the Project

The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks. The independent variable is the MAP resources and educational intervention. The dependent variable is medication adherence rates. A quantitative methodology will be used for the project to learn about this population (home health patients) (Allen, 2017).

The specific population that will be addressed are adult home health patients ages 35 to 64 years old. The primary investigator chose this population because of the prevalence of Type II diabetes rising in children, adolescents, and young adults in the United States (12:100000) (Centers for Disease Control and Prevention, 2020; Kao & Sabin, 2016; Reinehr, 2013). The selected site serves approximately 100 patients annually, and 30 patients are diagnosed with Type II diabetes. The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria are individuals with language or cognitive deficits and diagnosed with Type I diabetes. Five female staff nurses will be trained to help implement the quality improvement project. They are individuals who are registered nurses, work full-time, and have been employed with the facility for over one year.

The geographic location of the project is in an urban area of Houston, Texas. The County statistics show that approximately 17.6% of the population have Type II diabetes (Houston, 2021). During 2016-2018, 20.2% of the population was hospitalized due to diabetic complications (Houston, 2021). There are over 700 000 Medicare participants in a three-county radius, which is higher than the national average (Understanding Houston, 2021). Data further showed that preventable hospital stays occur in older adults 65 and above (Understanding Houston, 2021). This suggested a trend to overuse the hospitals as a primary source of care (Understanding Houston, 2021).

The project contributes to the nursing field by increasing the healthcare providers’ knowledge and awareness of the obstacles and other risk factors involved in a patient not adhering to their medication regimen. Furthermore, it would help increase dialogue between the provider and patient in sharing the details of their behavior (Bussell et al., 2017). This creates a positive, blame-free atmosphere allowing the patients to discuss their medication-taking behavior (Bussell et al., 2017).

Clinical Question

A well-developed clinical question must be related and relevant to patient care. This helps the primary investigator search for evidence-based answers. The clinical question that will direct this quality improvement project is: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks?

The independent variable is the MAP resources. The dependent variables are the medication adherence rates.

To address the clinical question, the medication adherence rate for 30-days before and 30-days after the implementation of MAP resources will be compared using a chi-square test. The chi-square test will allow for a comparison of the medication adherence rate for patients 30 days before and 30 days after the implementation, thereby answering the clinical question. The level of significance will be set to .05, indicating a p-value of less than .05 would reveal statistical significance.

Advancing Scientific Knowledge

This direct practice improvement project seeks to enhance medication adherence among diabetic home healthcare patients using the MAP resources. Various researchers have cited the benefits associated with patient-provider engagement and collaboration to improve medication adherence (Ong et al., 2018; Polonsky & Henry, 2016; Wong et al., 2020). The advancement of a small step forward at the clinical site is that by improving medication adherence rates among diabetic patients’ positive patient-related outcomes will likely occur using the MAP protocol. This will add to the current literature and address the gap found regarding non-medication factors among home health diabetic patients.

The theoretical framework that will be used in this quality improvement project is Orem’s self-care deficit theory (1995) was developed to improve patient health outcomes in in the context of nursing contribution (Yip, 2021). The theory is comprised of three related sections: theory of self-care, self-care deficit, and the nursing system (RenpenningcN et al., 2003).  It fits the project because it includes healthcare providers assisting patients in their self-care and management to improve their function at a home level (RenpenningcN et al., 2003). The patients cannot effectively manage medication adherence for diabetes, which affects their quality of life and health Orem’s self-care deficit theory advances the project by contributing to previous research conducted on Type II diabetic patients using the theory (Borji et al., 2017; Ghafourifard & Ebrahimi, 2015; Shahbaz et al., 2016). This project, the theory, helps to advances the clinical practice by improving the participant’s quality of life by providing a self-care program as a solution using the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources. The theory guides the primary investigator to increase the patient’s awareness about their disease and minimize their non-compliance with their regimen (Borji et al., 2017). The theory helps to identify the educational needs of home healthcare patients, which is more needed than the proper treatment (Borji et al., 2017). Implementing Orem’s self-care deficit theory is recommended to increase a patient’s knowledge level and adherence to self-care behaviors (Shahbaz et al., 2016).

            The change model that will be used in this quality improvement project is the Diffusion of Innovation Model developed by Rogers (2003). There are five stages: a) knowledge or awareness, b) persuasion or interest, c) decision or evaluation, d) implementation or trial, e) confirmation or adoption (Rogers, 2003). Diffusion is defined as a social process, which occurs among individuals in response to knowledge regarding a new strategy for improving their health (Dearing & Cox, 2018). It is also the process communicated within a specific timeframe (four weeks) (Dearing & Cox, 2018). This change model can provide the primary investigator with methods to share and educate regarding a new diabetic prevention strategy (Lien & Jiang, 2016). The model has been utilized in various fields to help healthcare providers understand and translate new concepts, treatments, disease knowledge, and educational methods (De Civita & Dasgupta, 2007; Lien & Jiang, 2016). For this project, the primary investigator using the MAP resources provide the participants a new approach to be integrated into the daily practices to improve quality of life and diabetic outcomes. Utilizing these methods will help the project advance by helping the healthcare providers to implement a standardized method in evaluating the patient’s medication-taking behaviors.

Significance of the Project

The significance of the project is that there continues to be a steady rise in chronic diseases has resulted in more patient care options (Polonsky & Henry, 2016). To meet various population groups’ unique needs, home-based care has gained popularity (Holly, 2020). Type II diabetes patients who qualify for home-based care options must demonstrate their willingness to work with the home healthcare agency at the selected project site. When patients who receive home-based care fail to adhere to the care requirements set forth, adverse outcomes can ensue (Polonsky & Henry, 2016).

The possible results based on the clinical question and problem statement should increase patient compliance related to medication adherence. The project also helps to empower healthcare providers to adequately address medication questions and patient concerns and ensure the patients keep track of their medication regimen, resulting in a reduction in adverse events. Holecki et al. (2018), when the MAP resources were utilized, medication adherence increased significantly.

The findings noted by Holecki et al. (2018) reinforce the beneficial nature of implementing the MAP resources, as this can improve the quality of patient care received. For this quality improvement project, it fits within helping to correct the gap noted in the literature (regarding medication adherence) for this population. Furthermore, it contributes to the clinical site by helping the patients maintain their medication regimen. Hence, decreasing potential infections, hospitalizations, and incurring financial costs to (patients and the facility).

Rationale for Methodology

The methodology chosen for this quality improvement project is quantitative.

Creswell and Creswell (2018) noted a quantitative methodology is best suited for projects that require data in numerical form. In this project, the numerical data will be presented using charts and graphs. These charts and graphs will allow readers to compare medication adherence rates pre-project implementation and post-project implementation.

While qualitative research studies are beneficial, they examine experiences, perspectives, and beliefs about a specific issue (Creswell & Creswell, 2018). The data collection used in this type of methodology is interviews (semi-structured, one-on-one, and focus groups). For this project, the primary investigator is not seeking to understand the participants’ feelings, behaviors, or lived experiences related to medication adherence.

A quantitative methodology supports the project because it will permit the primary investigator to remain objective in providing the project’s findings (Leedy & Ormord, 2020). Furthermore, the methodology allows the primary investigator to summarize the data that could support generalizations for a larger or similar population. The methodology is less costly with easy replication for future quality improvement projects to obtain the same results.

Nature of the Project Design

A quasi-experimental design will be used for this project. Quasi-experimental designs are used to compare data before and after the implementation of an initiative/intervention. Price et al. (2017) state in a pretest-posttest design, the dependent variable is measured once before the treatment is implemented after it is implemented. Often, these designs are used when research occurs in a controlled environment. While this project will not be conducted in a controlled environment, the primary investigator selected a quasi-experimental design because it is more cost-effective than an experimental project design (Schweizer et al., 2016). Furthermore, since data pre-project implementation and post-project implementation need to be collected and analyzed to explore the intervention’s impact, a quasi-experimental design is most appropriate.

A correlational design was considered but not appropriate for the project because the primary investigator is not seeking to understand the relationships occurring among the variables (Creswell & Creswell, 2018). This design is typically descriptive relying on a hypothesis (Leedy & Ormord, 2020). The primary investigator will not seek the relationships between the independent variable (MAP resources and education intervention) and the dependent variable (medication adherence rates).

The targeted population are home health patients ages 35 to 64 years old. The selected site serves approximately 100 patients annually, and 30 patients are diagnosed with Type II diabetes. The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria are individuals with language or cognitive deficits and diagnosed with Type I diabetes.

The data collection process will begin once approved by Grand Canyon University Institutional Review Board (IRB). Recruitment will occur from informational flyers given to the patients during their home health visits with the providers. The nurses will answer any questions regarding the project’s risks, benefits, and purpose and be instructed that the participation is voluntary. The primary investigator will use a convenience sample because of the access to the participants.

Data will be collected retrospectively four weeks prior to project implementation from the electronic medical records (Cradle Solutions software) (medication adherence rates) (Cradle Solutions, 2021). In the last three days of the first week the primary investigator will educate the healthcare providers regarding using the MAP resources. The staff will begin implementing the tool, and the post medication adherence rates will be assessed four weeks post-implementation. The primary investigator will document the data in a Microsoft Excel 2016 codebook developed by the primary investigator. Once completed, it will be exported into the SPSS-27 and analyzed using an independent t-test. A five-item demographic questionnaire will be used for descriptive statistics of the population. The survey will include (age, gender, years with Type II diabetes, oral or insulin, and education).

Pre-intervention and post-intervention data will be obtained via the project site’s EHR. The questions that will be analyzed are: (1) “Have you experienced any increase in thirst?” (2) “How often do you urinate?” (3) “Do you often feel fatigued even when doing little tasks?” and (4) “Do you experience blurred vision?” In addition to the questions, home healthcare providers will ask the patient “Are you taking your medications?” Any information attained from the question and due to probing, observation of patient’s medications, and patient-related medication adherence will be documented in the project site’s EHR. The data will be analyzed using an independent t-test to determine the statistical significance.

Definition of Terms

The following operational terms will be used interchangeably throughout the manuscript:

Adherence Assessment Pad.

The Adherence Assessment Pad is part of the MAP resources that explores answers via the patient perspectives. Using the Adherence Assessment Pad, nursing staff members will be able to explore the concerns of patients and adjust, pending further project team review, to the patient’s medication regimen (Starr & Sacks, 2010).

Home-based Healthcare.

The term home-based healthcare or home healthcare references the medical care that is provided to patients in the comfort of the patient’s home (Polonsky & Henry, 2016). Home-based healthcare services differ depending on a patient’s needs, diagnosis, and other factors.

Medication Adherence.

The term medication adherence references the extent to which a patient, caregiver, or home nurse follows the recommended guidelines on managing a medical condition (Ahmed et al., 2018).

My Medications List.

Is a list that provides a breakdown of the patient’s medications, in an easy-to-follow chart format, thereby improving patient medication adherence (Starr & Sacks, 2010).

Questions to Ask Poster.

Is a part of the MAP toolkit, which will be utilized during this project. When using the Questions to Ask Poster, home healthcare providers answer six questions to patients about medication adherence and medication knowledge. The questions that providers will answer include: (1) “Why do I need to take this medicine?,” (2) “Is there a less expensive medicine that would work was well?,” (3) “What are the side-effects and how can I deal with them?,” (4) “Can I stop taking any of my other medicines?,” (5) “Is it okay to take my medicine with over-the-counter drugs, herbs, or vitamins?,” and (6) “How can I remember to take my medicine?” Providers must answer all the questions and should assume that individuals have no medication knowledge, thereby confirming that patients know and understand these critical answers (Starr & Sacks, 2010).

Type II Diabetes.

For this project, Type II diabetes is the topic of exploration. It is described as an impairment of the body regulating and using glucose as a fuel source. Type II diabetes is a chronic condition where an excess amount of sugar is circulating in the blood stream (Mayo Clinic, 2019).

Assumptions, Limitations, Delimitations

As with all practice improvement projects, assumptions, limitations, and delimitations must be addressed. Assumptions are considered self-evident truth (Grand Canyon University, 2021). They are statements that are deemed plausible by other individuals and peers who read the project. The first assumption is that the participants will self-report honestly to the best of their recollection. To minimize social-desirability bias, the primary investigator will compare the participant’s answers with other data (laboratory values for glucose levels) (Leedy & Ormrod, 2020).

The second assumption is that the primary investigator will provide an accurate description of the current situation at the project site. To ensure that fabrication and falsification of the project findings do not occur, the primary investigator will observe the nurses during the patient visit to monitor the interactions. The primary investigator will use an outside source as a statistician so that the project results are not skewed.

Leedy and Ormrod (2020) stated that limitations are factors that the primary investigator has no control over. The first limitation is the primary investigator’s lack of control over the environment related to the novel coronavirus pandemic (COVID-19). The pandemic has affected the method in which the project will be implemented. The primary investigator will not interact with the participants during the project. Instead, five registered nurses were educated to implement the project. The pandemic has increased many patients’ fear related to one-on-one interaction with their primary care providers. The primary investigator does not know if there is a possibility with the new variant (Delta-variant) if the project will be modified to virtual monitoring to minimize the participant’s risk of COVID-19 infection.

The second limitation is conducting the project (four weeks versus longer) (cross-sectional versus longitudinal). A cross-sectional project allows for a snapshot of a specific moment (Leedy & Ormrod, 2020). A longitudinal project would have allowed the primary investigator to provide a richness of data regarding the topic. The primary investigator could identify and convey the findings related to the participants’ behaviors, patterns of change, experiences, and reduce recall bias (Coolican, 2014). Furthermore, this type of project would allow the primary investigator to test whether the variables were casual or the result of other differences (Leedy & Ormrod, 2020).

Delimitations are choices the primary investigator made, describing the boundaries placed on the project. One project delimitation noted is the inclusion criteria of the participants. Patients with diabetes, ages 35 to 64, are included in the project. Since this project’s focus is to explore medication adherence among diabetes patients, which is a concern at the project site, it has narrowed the field to learn about other patients and their compliance issues. The second delimitation is where the project was conducted, an urban area located in the southeastern region of the United States, thereby impacting the generalizability of its findings.

Summary and Organization of the Remainder of the Project

The aging population is growing at an increasing rate in the United States, hence snowballing the number of individuals taking medications to manage their Type II diabetes. Kyarnstrom et al. (2018) emphasized that for Type II diabetics, it is essential that proper and effective medication adherence be maintained. For home healthcare patients, 45% of this population fail to maintain glycemic control < 7% (Polonsky & Henry, 2016). This is attributed to poor medication adherence (Polonsky & Henry, 2016). Healthcare providers are a critical component in making a difference by helping patients learn and maintain medication adherence.

The quality improvement project will use a quantitative methodology. The rationale for using this method is to collect numerical data that can be statistically analyzed. A quasi-experimental design will answer the clinical question to determine if the outcome impacted the medication adherence rates. The project will be guided by Orem’s self-care deficit theory and Roger’s diffusion of innovation model (Rogers, 2003).

Chapter 1 provided detailed support for utilizing the MAP resources to improve medication adherence among diabetic patients of the project site. A quantitative, quasi-experimental design was used to explore the impact of the MAP intervention on improving medication adherence among Type II diabetes patients of the selected project site. Other portions of the chapter included advancing scientific knowledge using Orem’s self-care deficit theory and Roger’s diffusion of innovation model. A detailed description was given related to the project’s significance, project’s methodology, and design. The last few sections of the chapter comprised the definition of terms, assumptions, limitations, delimitations, and a summarization of the chapter.

Chapter 2 presented a detailed summary of the literature collected related to the project’s clinical question. Information about the theoretical framework and change model is detailed. The chapter comprises five sections, which highlight information about literature obtained from 2016 to 2021. The information presented provides readers in-depth knowledge and the importance of each chosen section.

Chapter 3 offered research methodology details that the primary investigator employed. The information presented in the chapter included the selected research design, the target population, and the sample size. Furthermore, data collection tools (specifically the MAP’s resources) and data analysis procedures are discussed. The reliability and validity of the project instruments are detailed. Lastly, ethical considerations for collecting data are addressed.

Chapter 4 presented the project’s findings, which were analyzed using chi-square analysis. Results regarding the descriptive and inferential data analyses will be offered. Furthermore, a brief discussion of project-related findings is delivered. The information will be presented using graphics, figures, and tables. Chapter 5 delivered the conclusions and recommendations drawn from the project’s results. The impact of the findings, in terms of practical and theoretical knowledge, will be offered.

Chapter 2: Literature Review

Diabetes mellitus (DM) is a global epidemic in this era, and many diabetic patients comprise Type II diabetes mellitus (Rana et al., 2019). Medication adherence is a critical component and key determinant in obtaining therapeutic success and reducing diabetic complications (Rana et al., 2019). For Type II diabetic home health patients, this is vital in self-care and management of the disease. Unfortunately, approximately 30% to 50% of patients adhere to their medication regimen (Hennessey & Peters, 2019).

Diabetes is a lifestyle disease, which can be prevented or avoided by making lifestyle changes. Disease management can also occur through adhering to one’s prescribed medication regimen(s). Medication adherence is important since it can help to reduce the likelihood of diabetes-related challenges and complications. In the United States (U.S.), the problem is associated with increased morbidity and mortality rates, with approximately 125,000 deaths and 10% of hospitalizations annually (Hennessey & Peters, 2019). Furthermore, medication nonadherence costs the U.S. healthcare systems roughly $100 billion to $317 billion yearly (Kini & Ho, 2018). The purpose of this quantitative quasi-experimental project is to determine to what degree the implementation of the Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients, ages 35 to 64 of a home healthcare organization located in urban Texas.

Chapter 2 reintroduced the project’s subject matter, background, theoretical framework, and change model. Other segments include a review of literature on previous and current empirical research related to medication adherence in Type II diabetic home health patients. The chapter’s themes are related to patient-related factors (non-pharmacological and pharmacological lifestyle changes, patient beliefs). Socio-economic factors (medication costs, health literacy, lack of social support), health system factors (trust in the healthcare provider, complicated medication regimen), and interventions (patient education, motivational interviewing, and MAP resources).

The primary investigator conducted a literature review utilizing peer-reviewed articles from 2016 to current. The inclusion criteria were articles written in English, topics specific to the project such as barriers to medication adherence, MAP resources, medication adherence, and Type II diabetes. The exclusion criteria were articles not written in English, more than six years, Type I diabetes, or involved children. Databases reviewed were PubMed, Google Scholar, CINAHL, Cochrane Library, EBSCOhost, and Grand Canyon University online library. The review revealed over 632,000 plus results; however, the primary investigator selected 30 articles for this chapter for this project.

One of the most problematic issues associated with home care for diabetes patients is adherence to medications. According to Bonney (2016), patients take their medication as prescribed only 50% of the time. Furthermore, patients are often reluctant to share medication compliance details, thereby resulting in health-related complications (Bonney, 2016). Type II diabetes mellitus is at an epidemic proportion globally (Centers for Diseases and Prevention Control, 2020). The incidence of the disease will continue to rise from 382 million individuals to 417 million by 2035 (Polonsky & Henry, 2016; Rana et al., 2019). Healthcare experts are becoming increasingly concerned because of the costs, morbidity, and mortality rates linked with the disease (Polonsky & Henry, 2016). One of the elements contributing to the problem is poor medication adherence (Rana et al., 2019). This is particularly true in-home health Type II diabetic patients. Medication adherence in adults with chronic conditions is roughly between 30% to 50% (Kini & Ho, 2018; Neiman et al., 2017). Furthermore, the healthcare system associated with medication nonadherence is costing the U.S. healthcare system $100 billion to $317 billion annually (Rana et al., 2019). (background)

As adults in this country age, many are afflicted with chronic diseases such as diabetes (Type II). It is one of the main reasons for admission to home health agencies (Sertbas et al., 2020; Wong et al., 2020). Home health agencies have been in existence for over 30 years (Choi et al., 2019). These organizations will continue to grow and impact medical advances and technology (Wong et al., 2020). Hence, there is a need for healthcare providers to become familiar with strategies and barriers linked with medication adherence for this population. Many home health patients have difficulty adhering to their medication regimens. They often express difficulty adhering to the regimens, which reinforces the critical role of home healthcare providers (Wong et al., 2020). This is partly due to them not having knowledge and education related to the disease and proper self-management (Wong et al., 2020).

Theoretical Foundation

Orem’s self-care deficit theory was selected to guide this quality improvement project. The theory was chosen because of its expectations that an individual must be self-reliant and responsible for their care (Orem, 1985). Dorothea Orem’s theory states self-care is an activity that a person engages in to maintain, restore, or enhance their health (Orem, 1985). The theory further states that nurses should not consider patients as inactive or sheer recipients of healthcare; instead, they should be considered reliable, responsible individuals who can make informed decisions and be active in their health care (Orem, 1985).

This theory describes nursing as an action between two or more individuals (RenpenningcN et al., 2003). Furthermore, it assumes that a successful patient with self-care understands it is a primary element in health prevention and illness (RenpenningcN et al., 2003). The theory fits the project because the healthcare providers are in supportive educational roles, which assists the patient when they are ready to learn or cannot complete a task without guidance (Orem, 1985). Also, the theory relates to healthcare providers assisting patients in their self-care and management to improve their function at a home level (RenpenningcN et al., 2003). The theory has been used in multiple studies regarding patients with chronic diseases (Afrasiabifar et al., 2016; Borji et al., 2017; Khademian et al., 2020).

            The change model that will be used is the diffusion of innovation developed by Rogers (2003).  There are five components of the theory are a) knowledge, b) persuasion, c) decision, d) implementation, and e) adoption (Rogers, 2003). The model is defined as a social process, which occurs among individuals in response to knowledge regarding a new strategy for improving their health (Dearing & Cox, 2018). It is a process communicated within a specific timeframe (for this project, four weeks) (Dearing & Cox, 2018). This change model can provide the primary investigator with methods to share and educate regarding a new diabetic prevention strategy (Lien & Jiang, 2016). The model has been utilized in various fields to help healthcare providers understand and translate new concepts, treatments, disease knowledge, and educational methods (De Civita & Dasgupta, 2007; Lien & Jiang, 2016). For this project, the primary investigator using the MAP resources provide the participants a new approach to be integrated into the daily practices to improve quality of life and diabetic outcomes. Utilizing these methods will help the project advance by helping the healthcare providers to implement a standardized method in evaluating the patient’s medication-taking behaviors.

Review of the Literature

Diabetes is prevalent in the United States and globally (Rana et al., 2019). It is one of the primary diagnoses for being admitted into home health care (Sertbas et al., 2019). Hence, the usage of home health services has become increasingly popular because it allows patients to remain in a comfortable atmosphere and decrease hospitalizations (Sertbas et al., 2019). There are many studies regarding older adults and diabetes, but minimum regarding home health care patients with diabetes (Sertbas et al., 2019). The review of literature is based on themes centered on patient-related factors, socioeconomic factors, and interventions.

Patient-related Factors

The World Health Organization (2017) stated patient related factors encompass an individual’s resources, knowledge levels, belief system, perspectives, and expectations. These factors can vary dependent on the non-pharmacologic and pharmacologic lifestyle changes that the person maintains (Nduaguba et al., 2017). Type II diabetes management involves not just medication adherence but observance to monitoring diet and exercise, follow-up, and self-care (Nduaguba et al., 2017).

Medication adherence.

Medication adherence is a term that refers to one taking medication as prescribed by their healthcare practitioner (Ahmed et al., 2018). Healthcare providers must ensure that the prescriptions provided to patients are suitable to the individual’s conditions.

While medication adherence is important, there is a plethora of literature available that expresses the prevalence of medication non-adherence among patients. Various factors continue to impact medication adherence, which includes, but are not limited to, fear, costs, misunderstanding, too many medications, lack of symptoms, mistrust, worry, and depression (American Medical Association [AMA], 2020). To prevent medication non-adherence, providers can seek to understand the needs of patients and provide them with resources that can aid in overcoming non-adherence.

Ahmed et al. (2018) emphasized that the quality of healthcare can be influenced by the body’s ability to respond to the treatment. A study conducted by Rana et al. (2019) was related to exploring medication adherence to prescribed treatments as a crucial factor for hospitalized Type II diabetic patients in a Bangladesh hospital. The quantitative, descriptive cross-sectional study involved 112 Type II diabetic patients recruited from medical and endocrinology wards. Much of the sample size age was 57.46, 60.7% were male and married. The patient’s medication adherence was measured using the 7-item MCQ scale modified by Ahmad et al. (2013). Data were analyzed using SPSS-21. Descriptive statistics were used to measure the participants’ demographics. An independent sample t-test and one-way ANOVA with post hoc comparisons were used to evaluate the relationships between the variables (p =.05).

The results from the Rana et al. (2019) study showed 72.3% of the participants forgot to take their medications, 96.4% chose not to take the medication or miss a dose when feeling better. Most of the patients, 81.3%, did not take their medications with them when traveling. The mean scores of the MCQ were 26.46 (SD =1.58). The study’s results concluded that the level of medication adherence among Type II diabetic patients was suboptimal (Rana et al., 2019). The authors recommended that more attention needed to be given to varied age groups related to medication adherence.

Lee et al. (2017a) conducted a quantitative study to determine the medication adherence among Type II diabetic patients in an Asian community. This cross-sectional study involved 382 Asian participants from a primary outpatient care clinic in Singapore. The patient’s medication adherence was measured using a five-item Medication adherence report scale (MARS-5). A low medication adherence score was <25. The sample size was predominately female, with a mean age of 62 years. Using univariate analysis, the results showed 57% of the participants had a low medication adherence score, which was attributed to them being married or widowed, taking fewer than four medications daily, and poor glucose control. The study concluded that younger patients were susceptible to low medication adherence scores (Lee et al., 2017).

Although the studies were conducted in different settings (primary care and hospital), the results demonstrated a need for healthcare providers to focus on different age groups and their reasons for not adhering to their medication regimen. The studies were cross-sectional, which indicated the authors were unable to evaluate the participant’s habits and trends. This could have changed if they could assist the patients with barriers they faced during the studies.

To handle the issue of medication adherence among the diabetic patients who have had an issue with adherence to medication needs to come up with a variety of strategies that have been attained from scholarly reviews as well as journals for purposes of well researched data on the concept. Appropriate types of medications are usually considered to be the identified cornerstone regarding the prevention as well as disease treatment yet according to numerous research carried out, there is solely about half of the individual patients who adhere to the instructions of their prescribed medication (Bosworth, 2015). This usually causes a common as well as costly public health-associated challenge especially for the healthcare system in the U.S.

This topic was chosen because inefficient medication adherence is complex, with a variety of contributing causes; hence, there is no universal solution (Rodriguez-Saldana, 2019). For a patient to succeed with medication adherence, the healthcare provider must understand the underlying reasons that are barriers that could be removed or diminished. Teaching the patient new strategies that are patient-centered will help them achieve the new normal.

Non-pharmacological indicators.

There are many medications used for the effective management of diabetes (Raveendran et al., 2018). Effective non-pharmacological therapy should be explored with all Type II diabetics. The measures could include nutrition and exercise. Nutrition interventions are critical in a person with diabetes maintaining an optimal glucose level (80-120mg). The dietary pattern that must be encouraged is consuming fruits, vegetables, low-fat dairy foods, whole grains, and minimal red meat (Asif, 2014). Khazrai et al. (2014) study emphasized that food intake is associated with obesity. However, it is not just the volume of food but the quality of one’s diet. High ingestion of red meat, sugary items, and fried foods contributes to insulin resistance and Type II diabetes (Khazrai et al., 2014). People with diabetes should be educated regarding consuming fruits and vegetables in protecting them since they are high in nutrients, fiber, antioxidants, and a protective barrier against diseases (Khazrai et al., 2014).

This topic was selected because educating Type II diabetic home healthcare patients regarding their dietary habits is an integral part of diabetes care. Failure to incorporate healthy eating habits along with medication adherence can lead to severe complications of the disease. Healthcare providers must teach home healthcare patients dietary guidelines according to their food selection, cultural, and personal preferences to change their eating patterns.

Pharmacological factors.

Type II diabetic patients typically take multiple medications for their condition and other comorbidities (Kirkman et al., 2015). Following one’s medication regimen and treatment improves patient outcomes, reduces healthcare costs, hospitalizations, and mortality (Kirkman et al., 2015). A retrospective study conducted by Kirkman et al. (2015) determined patient, medication, and prescriber factors that influenced diabetic patients and medication adherence. A sample size of 200,000 participants (from 50 states, including the Virgin Islands) was extracted from a pharmacy database (Medco Health Solutions). The participants’ eligibility was based on the medication, benefits, and prescription history. Each patient was followed for one year from the medication date to post-implementation of the study.

Medication adherence was described as a medication possession ratio > 0.8 (Kirkman et al., 2015). Logistic regression analyses were conducted to evaluate factors independently linked with adherence. The results demonstrated that 69% of the participants were adherent. Other findings illuminated that adherence was associated with one’s age (older), male, higher education and income, and the use of the mail order versus retail pharmacies. Individuals with a new diagnosis of diabetes were less likely to be compliant with their medication regimen.

The authors concluded that demographic, clinical, and system-level factors influenced the participants’ medication adherence regimen (Kirkman et al., 2015). The authors emphasized that younger individuals, newly diagnosed and had minimal medications to take, were at a higher risk for non-adherence. Individuals who used mail-order pharmacies resulted in higher medication adherence due to lower out-of-pocket costs (Kirkman et al., 2015).

Patient’s belief system.

One’s culture influences a patient’s beliefs regarding medications, which ultimately affects their medication adherence (Lemay et al., 2018). This remains a challenge for healthcare providers in helping patients to understand the significance of medication adherence (Shahin et al., 2019). A study conducted by Shahin et al. (2019) used a systematic review to determine the importance of an individual’s cultural belief influenced medication adherence. A total of 2,646 articles were selected from various databases such as PubMed, CINAHL, EMBASE, and PsychINFO. Twenty-five of them met the inclusion criteria. The studies focus on diabetes or hypertension.

The study results from Shahin et al. (2019) revealed personal and cultural factors linked with medication adherence. Ten articles (40%) demonstrated an individual’s perception of the illness, five (20%) were affiliated with health literacy, four (16%) cultural beliefs, three (12%) self-efficacy, and five (20%) knowledge illness (Shahin et al., 2019). Shahin et al. (2019) study concluded that one’s cultural influences affect their perception of the importance of medication adherence. Healthcare providers must understand their patients’ pre-existing perspectives of diabetes before offering new information. This is an opportunity for healthcare professionals and patients to have a dialogue to diffuse misconceptions related to the patient’s perceptions. The authors suggested that future research should identify the religious beliefs associated with disease knowledge and medication adherence.

Healthcare providers and the relationships with patients.

Patients usually consider their healthcare providers (HCPs) as the most dependable source of data regarding their health condition and treatment. Patients are highly likely to effectively follow the treatment plan when they are involved in having a good relationship with their HCP due to the confidence and trust that has been built over time. Relationship building in healthcare is a vital aspect in the day to day lives of healthcare practitioners due to the nature of their job, which necessitates that they maintain long-term relationships with their patients for enhanced medication and treatment outcomes (Heston, 2018).

Trust is critical to developing, specifically since patients can experience improve health-related outcomes when they value relationships with their HCPs. Patients who have trust in their HCP often believe that their provider has a high level of competence and truly cares about their health-related outcomes (Heston, 2018). Mistrust develops when the patients attain unrealistic, inconsiderate, or insensitive advice from their HCPs, as well as feel emotional distance from them.

Health literacy.

            Health literacy is described as one’s ability to obtain, communicate, process, and comprehend basic health information and navigate health services to make an informed decision (Sawkin et al., 2015). Medication adherence is broadly identified as a patient’s ability to follow a prescribed medical treatment (Sawkin et al., 2015). Researchers Glanz et al. (2015) have explored the impact of low health literacy rates on patient compliance with medications and health-related advice. The authors stated that approximately 35% of American adults possess basic or below basic health literacy levels (Glanz et al., 2015).      Chima et al. (2020) conducted a systematic review to evaluate the impact of health literacy and medication adherence. Literature searches were performed using Ovid Medline, CINAHL, EMBASE, Scopus, and PsycInfo. The inclusion criteria for the articles were conducted in the United States, 18 years or older with a diagnosis of Type I or II diabetes, medication adherence was an outcome variable, quantifiable measure reported, and was a full text journal article. Articles were graded using Joanna Briggs Institute Critical Appraisal Checklists, which is appropriate for the respective study designs identified. Thirteen articles were retained in the review, most of which used a cross-sectional design.

            The results demonstrated four of the 11 studies found a positive association between health literacy and medication adherence (Chima et al., 2020). Two of the four studies had methodological shortcomings. The authors concluded there was some evidence that health literacy is linked with medication adherence among diabetic adults in the United States. Recommendation for future research to design and execute longitudinal studies to determine a deeper relationship between the variables (health literacy and medication adherence (Chima et al., 2020).

Given inadequate literacy rates, among members of the general population, world practitioners continue to create unique strategies that can be used to reduce lacking health adherence among patients with diabetes. Improved literacy is a theme that should be of the utmost priority, specifically since it creates the foundation for long-term sustained profitability. Furthermore, as patients can understand the importance of medication compliance, adherence to medication regimens improves (Glanz et al., 2015).

Using universally implemented and published resources that can improve medication adherence is important. Tools and resources can be utilized by HCPs to identify patients who are not taking their prescribed medications. Prescriptions need to be taken seriously for exceptional results and for the continued well-being of patients who have critical illnesses like diabetes.

The use of simple language by HCPs, as well as by medication manufacturers, can encourage providers to meet patients where they are and utilize teach-back techniques to ensure a patient’s understanding of his/her prescribed medication regimen. Teach-back methods have been utilized to enhance medication adherence among many types of non-adhering patients. Most of the time people opt to not take their medication as they cannot read all the instructions written on the medicine and are afraid that they will die, especially in the cases that they mistake those drugs for poison or some drug that may look like a famous poison causing death. This is a key issue that has left most of the people victims of non-adherence (National Academies of Sciences, Engineering, and Medicine, 2018). 

Huang et al. (2020) conducted a cross-sectional study aimed to identify patient factors linked with diabetes medication adherence and health literacy levels. One hundred and seventy-five participants were involved in the study and recruited from two family medical clinics. All the participants were over the age of 20, diagnosed with Type II diabetes, taken one oral diabetic medication, and understood English. The authors evaluated the participants’ health literacy levels using the Newest Vital Sign, a six-item questionnaire, and an eight-item Morisky Medication Adherence Scale.

The results showed a self-reported status of (β = 0.17, p = 0.015) and medication self-efficacy (β = 0.53, p, 0.001), which were positively associated with diabetes and medication adherence (Huang et al., 2020). Health literacy was neither associated with diabetes medication adherence (β = −0.04, p = 0.586). The authors concluded that health literacy measured using the Newest Vital Sign did not correlate with medication adherence or glucose control among Type II diabetics. They recommended that healthcare clinics develop interventions to improve their patients’ self-efficacy of medication to improve the medication adherence rates (Huang et al., 2020).

Reading instructions and making a patient understand what is written on a medicine bottle or package should never be taken for granted as it is key for determining how patients will effectively or ineffectively adhere to the given drugs for treatment and disease control purposes. For the medical practitioner to be aware and sure that what they have explained to the patients has been delivered safely and appropriately, there is the need for a verification test. The patients as well as their identified support individuals need to be asked to explain in their own words stating what they have understood from everything the practitioner has told them regarding their health, along with drug management and intake. This teaching back method is vital in offering additional data on the key topic of interest; thus, it should be used often.

Concerns associated with the issues of side effects can be challenges to medication regimen adherence, especially when the given advantages associated with taking the medication are not properly comprehended. To minimize the potential concerns that are associated with the side effects of drugs, since this can be identified as one of the reasons why patients may opt to not adhere to medications in fear that they will experience the side effects and be greatly inconvenienced, there is the need for HCPs to offer the relevant data regarding the common types of side effects when they are in the prescription process.

There have been issues of people and patients dying or experiencing negative and disturbing side effects when it comes to them taking the medication prescribed by their doctors. These cases have always been used as examples to explain the reason why people have been reluctant to take medications for prolonged periods. When an individual has a critical illness, it is not uncommon that he/she needs to take the prescribed medication for a long period, as this can result in improved medication efficiency. Lacking understanding of medication-related details has caused patients to withdraw from their prescribed medication regimen, which is due to lacking knowledge and prolonged side effect issues that are associated with their medication (Institute of Medicine [IOM], 2011). 

For example, when offering metformin, to enable adherence to the drug there is a need to inform patients that are suffering from diarrhea during their time of prescription to anticipate that the loose bowel issues will be over in about a week if the drug is continued. It is also vital to offer brief explanations about medication side effects and benefits due to time limitations. If a patient cannot have additional time with his/her provider, then other members of the health care team should aid in answering their questions and provide additional education. Education can be in the form of printed handouts, websites, or a teaching module that should be readily available for use with the identified patient.

Socioeconomic Factors

            Socioeconomic-related factors that affect medication adherence include one’s location of residence, medical costs of treatment, and finances (Yeam et al., 2018). Other factors that could influence medication adherence are low health literacy, education level, lack of social support, living conditions, and medication costs (Hennessey & Peters, 2019). Health care providers must conduct a thorough assessment before providing a patient the prescription and consider any of the factors as mentioned above.

Medication costs.

            Kang et al. (2018) conducted a quantitative, longitudinal study to examine factors that affected cost-related medication nonadherence. Cost-related medication nonadherence (CRMN) is defined as taking medication then indicated or prescribed due to costs (Kang et al., 2018). Unknown sample size noted, but the Behavioral Risk Factor Surveillance System data for 2013–2014 was used to identify individuals with diabetes and their CRMN. Weighted multivariable logistic regressions were used, and analyses were conducted using the Survey suite of programs in Stata SE version 14. The survey weights were used to obtain population-level estimates and subpopulation methods to estimate standard errors for the subgroup’s analyses (Kang et al., 2018).

            The results demonstrated that CRMN among American adults was 16.5% (Kang et al., 2018). Individuals with an annual income of < $50k and without health insurance had the highest rates of CRMN. Insulin users had a 1.24 times higher risk of CRN than those not using insulin. Factors influencing CRMN were diabetes care and lifestyle factors, depression, arthritis, and asthma (Kang et al., 2018). Health insurance was the most significant factor for the participants < 65 years of age and depression for respondents > 65 years (Kang et al., 2018).

            The authors (Kang et al., 2018) concluded that one’s annual income and health insurance status were the most significant factors for younger adults, while depression was for older adults > 65 years. When the younger and older groups were combined, it showed the largest impact of CRMN affecting individuals < 55years of age and having higher rates of non-medication adherence (Kang et al., 2018). Recommendations were for healthcare organizations to develop policies, resources, and support systems that address the factors to help improve CRMN.

            Social Support.

            Various factors impact medication adherence. However, Linni et al. (2015) emphasized that social support must be considered a core component in interventions that improve the management of Type II diabetic patients. The social support theory has three components a) subjective support (emotional experience and fulfillment of the individual being respected and understood; b) objective support (direct material help from the social network in the communities; c) support utilization (various support strategies from family, friends, and colleagues) (Linni et al., 2015; Shao et al., 2017).

            A quantitative study conducted by Linni et al. (2015) determined whether social support was linked with medication adherence in patients with Type II diabetes. The study was conducted in a Beijing hospital with a random sampling of 412 participants with Type II diabetes. The adult patients’ assessment of their social support was retrieved from medical records and self-reported surveys (Social Support Rate Scale 14-item questionnaire). The support scale measured objective, subjective, and support utilization. The Chinese version of the Morisky Medication Adherence Scale, eight-item, was translated for the participants to complete. Three hundred and thirty participants completed the self-report measure medication adherence six months after the initial data collection.

t-test demonstrated a significant difference in social support between the low and high medication adherence groups (= -2.11, p= 0.036) (Linni et al., 2015). A regression analysis was used to determine the subscales of the support, which presented statistical significance and association with medication adherence (β = 0.29, p = 0.011), rather than another two subscales of subjective (β = −0.02, p = 0.80) and objective support (β = −0.04, p = 0.33) (Linni et al., 2015). The authors concluded that social support was a critical factor in improving medication adherence in diabetic patients. It must be impressed on this population to have open attitudes to receiving help from friends, family, and outside organizations.

A quantitative, longitudinal study conducted by Shao et al. (2017) determined the impact of social support and medication adherence among 532 Chinese patients from an outpatient and inpatient endocrine clinics. The authors used the ten-item Social Support Rating Scale for data collection related to social support. It measured the three components of social support (objective, subjective, and support utilization). A six-item self-efficacy scale was used to measure (emotional control, communication with physicians, symptom management, role function, and perceived adaptability to chronic diseases). Shao et al. (2017) developed a 13-item adherence scale that was divided into three subscales a) Do you take the medicine every day according to the doctor’s advice? b) Do you take the dosages according to the doctor’s advice? c) Do you take the medication on time?

Data were collected and entered into EpiData 3.1 software (Shao et al., 2017). A Pearson’s correlation coefficients were calculated to evaluate the pairwise associations between the social support scores, self-efficacy, and adherence (Shao et al., 2017). The descriptive data showed the participants were mostly older females. The coefficients for the three components were statistically significant demonstrated the goodness-of-fit indices (χ2 = 2 47, P = 0 12; GFI = 0 99; AGFi = 0 98; CFI = 0 98; and RMSEA = 0 05) (Shao et al., 2017).

            Both studies, Linni et al. (2015) and Shao et al. (2017) utilized an adequate number of participants for their quantitative studies. They used the same support rating scale, which validated their findings. The key difference is that the studies were conducted in various settings (hospital and endocrine outpatient/inpatient clinics). In conclusion, the studies validated the role of social support in managing Type II diabetic patients. Hence, it must be considered as a key component in any intervention a healthcare provider develops to improve self-managing and glycemic control (Linni et al., 2015; Shao et al., 2017).

Interventions

Using tools and instruments that are considered effective and appropriate is vital in supporting adherence in different ways and in achieving self-efficacy among the various patients. Positive family and social support are vital aspects associated with adherence to the issue of diabetes management (Rodríguez-Saldana, 2019). The engagement of family members can enhance self-care activities for patients suffering from diabetes, including eating effective and healthy foods, keeping fit, monitoring blood glucose, and adhering to medication.

A web-based portal is an innovative resource that can be used to assist patients. This web-based portal can improve medication reconciliation processes among patients and providers. The web-based portal can help patients with various regimens navigate challenges. Furthermore, this medication information, available through the portal can help individuals understand medication requirements, as the portal often helps to clarify and verify inaccuracies. The web portal aims to enhance medication adherence and prevent the improved use of the medication (Forman & Shahidullah, 2018). 

When patients can verify information in their electronic medical records to ensure proper medication adherence, this can enhance patient well-being. The EMR provides an accurate list of a patient’s medications and provides detailed medication information (e.g., type of drug, what the drug is used to treat, frequency of drug use, etc.). Also, the use of screening tests is vital in understanding how well patients are taking their drugs. If there is no consistency in medication-taking then motivation aspects should be utilized to enhance adherence (Eskola et al., 2017).

Medication Adherence Project (MAP).

            The MAP resources were introduced, developed, and implemented by the New York City Department of Health and Mental Hygiene in response to clinicians and pharmacists working in primary care practices (Starr & Sacks, 2010). It serves patient populations impacted by several chronic diseases (Starr & Sacks, 2010). The resources provide practical tools to help practitioners communicate with patients related to medication adherence. It consists of a training course and toolkit that was piloted and assessed by doctors, nurses, pharmacists, medical assistants, nutritionists, and healthcare educators (Starr & Sacks, 2010).

            The objectives of the tool are to acquaint healthcare providers with the obstacles associated with medication adherence with individuals who have chronic diseases:

Other aspects include a) evidence-based solutions that improve adherence, b) educate healthcare providers to engage in conversations regarding medication taking, c) help practitioners to combine the tool into the clinical practices and quality improvement methods, and d) help providers train their peers to use the resources effectively (Starr & Sacks, 2010).

Patient-Centeredness Care.

Patient-centeredness entails ensuring that all the identified interventions described in the first theme are focused on the individual patient who is being helped to effectively adhere to the given medication during home care settings. Patients who have been diagnosed with various critical illnesses and have been asked to go home for home-based care have been associated with poor adherence to the medications they are given when they are discharged from the hospital (Steinberg & Miller, 2015).

Practice recommendations, whether they are focused on evidence or expert opinion, are intended to offer the desired guidance on an overall approach to care (da Costa et al., 2018). The science, as well as the art associated with medicine, usually come together when the identified clinician is experiencing or has experienced some sort of situation whereby, they must make treatment recommendations for any patient who would be considered to not have effectively met the eligibility criteria for the studies on which the given guidelines were based.

Patient Advocacy.

Advocacy is a vital aspect in healthcare since it addresses the needs of the patient who need the utmost help and care, thereby allowing them to go back to their previous health state (D’Onofrio et al., 2018). Advocacy is an aspect that can be referred to as active support, as well as engagement, that aims to effectively develop a cause as well as a policy (Mollaoglu, 2018). Furthermore, advocacy is usually needed to enhance the lives of individuals suffering from diabetes. The various issues that diabetic patients experience, such as obesity, physical inactivity, and societal challenges reinforce the need for advocacy (Firstenberg & Stanislaw, 2017). 

In summary, these topics were selected because they contributed to helping the healthcare provider understand the challenges noted for this population. This contributes significantly to the challenge’s healthcare providers face in caring for Type II diabetic patients.

Summary

            The prevalence of Type II diabetes is affecting one in ten Americans (Ahmed et al., 2018). The disease is expected to continue rising higher by 2030 (Lin et al., 2018). Medication adherence for Type II diabetic home health patients is critical in decreasing the poor patient outcomes associated with the disease. Medication adherence with Type II diabetic patients remains a challenge for many healthcare professionals. Education for the healthcare providers and the patients can make a difference in this population’s lives.

Chapter 2 discussed reintroduced the topic and presented the theoretical framework and change model to guide the project. Other sections include the literature review related to patient-related, socio-economic, and health system factors.

A summary of the chapter was provided with an introductory sentence that previews Chapter 3.

            Chapter 3 reinstated the selected topic. Other segments presented the project’s methodology, design, population, and sample selection. A description of the MAP resources and the electronic medical record (EPIC) are provided. The validity and reliability of the instrument was demonstrated along with the data collection and analysis procedures, potential bias. The last few sections discuss the ethical considerations, limitations, and a summary that leads into Chapter 4.

Chapter 3: Methodology

Medication adherence is a critical component in minimizing adverse patient-related outcomes among individuals with chronic illnesses (Type II diabetic patients). Ahmed et al. (2018) stated medication adherence for this quality improvement project refers to the extent to which a home healthcare patient can correctly take their medications in the absence of their health care providers. Medication adherence requires an individual to adhere and comply with all the medical instructions provided (Bellou et al., 2018).

Type II diabetes affects one in ten Americans (Ahmed et al., 2018). Furthermore, due to the increase in older-aged adults and the rising prevalence of the disease, it is expected to elevate higher by 2030 (Lin et al., 2018). The home health services continue to grow, hence illuminating the need for education regarding medication adherence. Roughly 45% of the patients cannot maintain their glucose levels (Polonsky & Henry, 2016). Poor medication adherence is associated with higher financial obligations for the patient, hospital, and insurance companies. Polonsky and Henry (2016) emphasized the adverse outcomes cause frequent hospitalizations and lower quality of life for patients and their families.

Chapter 3 reestablished the selected topic. Other sections of the chapter include the statement of the problem, clinical question, project methodology (quantitative), and project design (quasi-experimental). The chapter described the population and sample selection, the instrumentation (MAP resources), validity, reliability, and data collection procedures. The last few segments included the data analysis procedures, potential bias, ethical considerations, limitations, and a summary.

Statement of the Problem

It is not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas. The targeted population is Type II diabetic patients in an urban healthcare facility in urban Texas. In collaboration with the stakeholders, it was noted that medication adherence among the patients was lacking. The information will be obtained from the electronic medical records (Cradle Solutions), which showed that ten percent of the diabetic patients were not adhering to their medication regimen.

Factors that influence poor medication adherence are numerous and include poor knowledge or awareness of the disease, medication costs, and lack of understanding of the medication treatment, which reinforced the project’s purpose (Heath, 2019; Sharma et al., 2020). Healthcare providers play an essential role in assisting patients with medication adherence. The primary investigator will introduce a standardized strategy for the facility’s healthcare providers to assess the patients’ medication adherence using MAP resources (Starr & Sacks, 2010).

Using a standardized method will help to solve the facility’s problem with medication adherence rates. It will also help improve the healthcare providers’ knowledge levels and awareness regarding the barriers associated with medication adherence. Complying with the new guidelines developed by the Centers for Disease Control and Prevention (2020) could help patients control their glucose levels, minimize healthcare costs, hospitalizations, and potential infections.

Clinical Question

The clinical question that will direct the primary investigator’s answer is: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks? The independent variable is the MAP resources. The dependent variables are the medication adherence rates. The data collection process will not begin before approval is received by Grand Canyon University IRB. The primary investigator developed informational flyers for the nurses to give their patients during their home health visits. The staff answered questions related to the project regarding risks, benefits, and purpose while instructing that participation is voluntary. A convenience sample will be used because of the easy access to the participants for the primary investigator.

The primary investigator will collect data retrospectively (four weeks) prior to implementation of the project. The data will be collected from the electronic medical records using Cradle Solutions for the impact of the MAP resources and medication adherence rates. In the first week, the primary investigator will educate the staff to use the MAP resources. Once the staff begins to implement the tool, post-medication rates will be assessed post-four weeks. The data will be inserted into a Microsoft Excel 2016 codebook developed by the primary investigator. It will then be exported into SPSS-27 and analyzed by using a chi-square test. The five-item demographic survey will collect the descriptive statistics of the home healthcare patients. The questionnaire comprises (age, gender, years with Type II diabetes, oral or insulin, and education).

Project Methodology

A quantitative methodology will be used for this quality improvement project. According to Creswell and Creswell (2018), a quantitative methodology is appropriate for projects that use data in its numerical form. For this project, the data will be presented using figures, graphs, charts, and tables. This will allow the readers to compare the medication adherence rates pre-implementation and post-implementation of the project.

A qualitative methodology was considered but not used, although they are beneficial. It explores the patient’s experiences, perspectives, and lived experiences regarding a phenomenon (Creswell & Creswell, 2018). Data collected using this methodology is semi-structured interviews, one-on-one interviews, and focus groups (Creswell & Creswell, 2018). The primary investigator aims not to understand the home health participants’ emotions, behaviors, or experiences related to medication adherence.

A quantitative methodology supports the project because it will permit the primary investigator to remain objective in providing the project’s findings (Leedy & Ormord, 2020). Furthermore, the methodology allows the primary investigator to summarize the data that could support generalizations for a larger or similar population. The methodology is less costly with easy replication for future quality improvement projects to obtain the same results.

Project Design

Quasi-experimental designs are utilized to compare data before and post-implementation of an intervention (Price et al., 2017). The design is frequently used in a controlled environment. For this project, the design was chosen because it is cost-effective versus an experimental project design (Schweizer et al., 2016). A quasi-experimental design allows the primary investigator to analyze the impact of MAP resources on medication adherence rates.

An experimental design was not considered because the primary investigator is not seeking to conduct the project under a controlled environment (Leedy & Ormrod, 2014). This design observes the independent variable (MAP resources) and the dependent variable (medication nonadherence rates). It is a simple test that is performed in various physical and natural settings (Leedy & Ormrod, 2014).

A correlational design was considered but not appropriate for the project because the primary investigator is not seeking to understand the relationships occurring among the variables (Creswell & Creswell, 2018). This design is typically descriptive relying on a hypothesis (Leedy & Ormord, 2014). The primary investigator will not seek the relationships between the independent variable (MAP resources and education intervention) and the dependent variable (medication adherence rates).

The targeted population are home health patients ages 35 to 64 years old. The selected site serves approximately 100 patients annually. The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria are individuals with language or cognitive deficits and diagnosed with Type I diabetes. Five registered nurses will help to implement the project. They are individuals who work full-time and have been employed over a year.

The data collection process will begin once approved by Grand Canyon University IRB. Recruitment will occur from informational flyers given to the patients during their home health visits with the providers. The nurses will answer any questions regarding the project’s risks, benefits, and purpose and be instructed that the participation is voluntary. The primary investigator will use a convenience sample because of the access to the participants.

Data will be collected retrospectively four weeks prior to project implementation from the electronic medical records (Cradle Solutions) (medication adherence rates). In the last portion of the first week, the primary investigator will educate the healthcare providers regarding using the MAP resources. The staff will begin implementing the tool, and the post medication adherence rates will be assessed four weeks post-implementation. The primary investigator will document the data in a Microsoft Excel 2016 codebook developed by the primary investigator. Once completed, it will be exported into the SPSS-27 and analyzed using a chi-square analysis. A five-item demographic questionnaire will be used for descriptive statistics of the population. The survey will include (age, gender, years with Type II diabetes, oral or insulin, and education).

Pre-intervention and post-intervention data will be obtained using the MAP resources. The questions that will be analyzed are: (1) “Have you experienced any increase in thirst?” (2) “How often do you urinate?” (3) “Do you often feel fatigued even when doing little tasks?” and (4) “Do you experience blurred vision?” In addition to the questions, home healthcare providers will ask the patient “Are you taking your medications?” Any information attained from the question and due to probing, observation of patient’s medications, and patient-related medication adherence will be documented in the project site’s EHR. The data will be analyzed using an independent t-test to determine the statistical significance.

The electronic medical record that will be used to collect data is Cradle Solutions a software for home health companies. It serves the specialized needs of home health care providers that give a web-based point-of-contact information entry and management (Cradle Solutions, 2021). It is compliant with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solutions, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator will obtain the measurement of the medication adherence rates and align it with new protocols and guidelines developed by the facility.

Population and Sample Selection

The specific population that will be addressed are home health patients ages 35 to 64 years old. The selected site serves approximately 100 patients annually, and 30 patients are diagnosed with Type II diabetes. A G* power analysis was conducted using version 3.1.9.2, the alpha measure of 0.05, effect size of 0.5, power of 80% to calculate the lowest sample size needed, which was (n=34). The inclusion criteria are males and females diagnosed with Type II diabetes, oral medication or insulin, and home health patients. The exclusion criteria are individuals with language or cognitive deficits and diagnosed with Type I diabetes. Five staff nurses will help to implement the quality improvement project. They are individuals who are registered nurses, work full-time, and have been employed with the facility over one year, and have access to Cradle Solutions EHR system.

The geographical location of the project is in a metropolitan area of Houston, Texas. The County statistics show that approximately 17.6% of the population have Type II diabetes (Houston, 2021). During 2016-2018, 20.2% of the population was hospitalized due to diabetic complications (Houston, 2021). There are over 700 000 Medicare participants in a three-county radius, which is higher than the national average (Understanding Houston, 2021). Data further showed that preventable hospital stays occur in older adults 65 and above (Understanding Houston, 2021). This suggested a trend to overuse the hospitals as a primary source of care (Understanding Houston, 2021).

The informed consent process will consist of the nurses explaining the project’s purpose, risks, and benefits. The participants will be informed that participation is voluntary and can withdraw without repercussions to their professional or personal lives. No compensation will be provided to the participants in the project. The participants’ identities and privacy will be protected throughout the project by the primary investigator not using their names or other identifiable information. The participants will be assigned a random number for security purposes. The primary investigator will abide by the University’s IRB guidelines and the Belmont Report (justice, respect for persons, and beneficence) (U.S. Department of Health & Human Services, 2018).

Hard copies of the data will be stored on a flash drive and kept in the primary investigator’s home office (in a locked file cabinet). The data files will be kept on the primary investigator’s laptop, which is digitally protected. The data will be stored for three years according to Grand Canyon University procedures (June 2023). Once the project is completed and the requirements met, the primary investigator will destroy the information using Iron Mountain shredding services and software ERASER on the laptop.

Instrumentation or Sources of Data  

The instruments to be used in the project are the MAP Toolkit and Training Guide resources, which includes (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list. The questions to ask poster encourages patients to ask the provider about their medication. For this project, the nurses will review the medications with the Type II diabetic patients. Six questions will be asked (1) Why do I need to take this medicine, (2) Is there a less expensive medicine that would work as well, (3) What are the side-effects and how can I deal with them, (4) Can I stop taking any of my medicines, (5) Is it okay to take my medicine with over the counter drugs, herbs, or vitamins, and (6) How can I remember to take my medicine?

The second section, the Adherence assessment pad, explores answers the barriers to the patient’s maintaining medication adherence. The questions include (1) makes me feel sick, (2) I cannot remember, (3) too many pills, (4) costs, (5) nothing, and (6) other.

The third component is my medication list. It provides detailed information in chart form, which is discussed between the patient and the healthcare provider. It comprises of (1) name and doses of my medicine, (2) this medication is for my diabetes, (3) when do I take and how much (options include: morning, noon, evening, or bedtime), and (4) I will remember to take my medicine (a blank that will be filled in).

The source of data for this project is the electronic medical record. The facility uses Cradle Solutions, software for home health companies. It serves the specialized needs of home health care providers that give a web-based point-of-contact information entry and management (Cradle Solution, 2021). It is compliant with HIPPA security features for billing, reporting, administrating, and managing patient information (Cradle Solution, 2021). Liss et al. (2020) emphasized that electronic health records can be used for quality measures as a snapshot or calendar year. The primary investigator will measure the medication adherence rates and align them with new protocols and guidelines developed by the facility.

Validity

Validity conveys how accurately a method is measured (Creswell & Creswell, 2017). If the method measures what it should and the findings correspond closely, it is considered valid. There are four types of validity are constructs, content, face, and criterion (Creswell & Creswell, 2018). For this project, construct and face validity is applicable to the instrument. A group of professionals developed the tool, which consisted of physicians, pharmacists, nurses, and medical assistants (Starr & Sacks, 2010). It was based on their years of work experience in their perspective fields. The toolkit’s improvements were adjusted and in alignment with the CDC and other healthcare governing bodies.

Reliability

Reliability refers to the consistency of instrument measuring something (Creswell & Creswell, 2018). If the same results occur regularly by using the same procedures under the same conditions, the measurement is reliable (Creswell & Creswell, 2018). For this project, the MAP toolkit reliability was confirmed by inter-rater reliability (Starr & Sacks, 2010). The observers noted the same results associated with using the instrument, which aligned with the literature findings regarding collecting data for medication adherence rates.

A study conducted by Harrell (2017), occurred over 90 days, where weekly medication adherence rates were assessed. Seventy-eight percent of the patients prior to the study’s implementation did not adhere to their prescribed medication regimen. Post three-months of the project, 56% of the patients improved regarding medication adherence rates. For this project, test-rest reliability will be noted, because the nurses will be using the MAP toolkit over time (two different times) (Creswell & Creswell, 2018).

Data Collection Procedures

The data collection process will begin once approved by Grand Canyon University IRB. Recruitment will occur from informational flyers given to the patients during their home health visits with the providers. The nurses will answer any questions regarding the project’s risks, benefits, and purpose and be instructed that the participation is voluntary. The primary investigator will use a convenience sample because of the accessibility to the participants. The goal is to achieve approximately 34 participants.

Five home healthcare nurses will be trained to implement the project. Training sessions will be offered twice so that the nurses working on the weekends can participate. The primary investigator will offer two 60-minute Zoom training sessions. During these sessions, the primary investigator will provide information regarding using the MAP toolkit and resources. A 10-minute PowerPoint presentation will be included during the 45-minute training session, along with a MAP toolkit binder for each participant.

            The participants will be educated by the nurses regarding the purpose of the informed consent and its contents. The participants will be informed regarding the benefits, risks (minimal), and purpose of the project. The potential risk (minimal) is related to emotional circumstances such as the stigma of the disease, anxiety, or depression. The participants will be instructed that if they felt increased anxiety, depression, or embarrassment during the project, they can withdraw without any reason, or the project will end for them immediately. They will be directed to a primary care physician or professional who will further help them. There is a slight chance that the hard copies (demographic and MAP surveys could be lost. To ensure that this does not occur, the primary investigator will use a digitally password-protected laptop to protect their privacy. The participants will be informed that the data will be kept in a password-protected folder on the laptop accessible only to the primary investigator. The nurses will collect the signed informed consents and return them to the primary investigator after their visits. The primary investigator will collect them daily during the first week of the project.

            The participants’ rights and well-being will be ensured by the primary investigator upholding the Belmont ‘s report principles a) justice, b) respect for a person, and c) beneficence. Furthermore, the primary investigator will adhere to Grand Canyon University’s IRB guidelines. The primary investigator will uphold justice by delivering fair treatment to all the participants. The participants will not exploit this population or manipulate their situation or disease. Respect for persons will be shown by treating the participants as autonomous individuals. All the participants will be treated using ethical conduct by respecting their answers and decision, thus protecting them from harm. Hence, this allows the primary investigator to abide by the beneficence guidelines.

The primary investigator will work with the information technology department, who will ensure that the three MAP resources are inserted into the Cradle Solution documentation software. During week one, the nurses will provide the patients with informed consent, answer questions related to the project, a five-item demographic survey, and a pre-MAP survey. The second to fourth week, the nurses will examine the patient’s medication list and adherence (ten minutes). Each week the nurses will record the medication adherence information in the patient’s electronic medical record.

Week four, all input by the nursing staff will be completed. If the patient expresses, they have not adhered to the medication regimen; it will be recorded in the system. Post scores will be collected by the primary investigator regarding the medication adherence rates. The results will be entered into the Microsoft Excel 2016 codebook developed by the primary investigator. The data will be exported into SPSS-27 be analyzed using a chi-square test.

The procedures adopted to maintain data security are the hard copies of the demographic and MAP surveys will be kept in a locked file cabinet in the primary investigator’s home, not accessible to anyone else. The Microsoft Excel 2016 codebook and SPSS results will be saved on the primary investigator’s digitally password-protected laptop. To ensure additional security, the primary investigator will install an encryption program (TrueCrypt) to prevent accidental access to the information. Per Grand Canyon University IRB guidelines, the data will be kept for three years (June 2024). At that time, the primary investigator will erase the information from the laptop using ERASER (computer software) and Iron Mountain shredding services to eliminate the data correctly.

Data Analysis Procedures

This quality improvement project is being conducted to address the issue noted of medication adherence among Type II diabetic patients in the home healthcare population. The information will be obtained from the electronic medical records (Cradle Solutions), which showed that ten percent of the diabetic patients were not adhering to their medication regimen. Data for the comparative and implementation patients will collected at the culmination of the four-week implementation period from the EMR and will be given to the primary investigator in a PDF report. The dependent variable (medication adherence rate) will be manually entered into a secure Microsoft Excel file (2016) for the comparative and implementation patients. All data collected will be in numerical values. Each patient will be given a unique identifier to organized data according to everyone.

The medication adherence rate is a nominal-level variable with two mutually exclusive options (adherent or non-adherent) for each patient and will be analyzed using a chi-square test as that is the most appropriate test for comparing two independent groups on a dependent categorical variable (Schober & Vetter, 2019). The patient groups are independent as patients in the comparative group (four weeks before implementation) were not matched for the implementation group. The project analysis will use a chi-square test, which is aligned to the project design as the test compares group differences when the dependent variable is measured at a nominal/categorical level (Schober & Vetter, 2019).

The clinical question that is guiding the project is: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks?

To address this question, the medication adherence rate for 30-days before and 30-days after the implementation of MAP resources will be compared. To answer the clinical question, a chi-square test will be conducted. The chi-square test will allow for a comparison of the medication adherence rate for patients 30 days before and 30 days after the implementation, thereby answering the clinical question. The level of significance will be set to .05, indicating a p-value of less than .05 would reveal statistical significance.

Raw data will be organized using a Microsoft Excel (2016) file with a unique identifier for each patient. Data on medication adherence rate for each patient in the 30-day comparative period and 30-day implementation period will be collected from quality department in a report and then manually entered into the Excel file as a categorical variable with numeric codes represented as 0 for non-adherent and 1 for adherent. After data entry in Microsoft Excel is completed, data will be exported to IBM SPSS version 27. To ensure data will be prepared for analysis, a preliminary analysis of all variables will be conducted to determine if the dataset has missing data or inaccurate entries. This will include frequency counts for variables to check for missing data and values outside of the possible range of 0 to 1 for the medication adherence rates.

Potential Bias and Mitigation

The internal validity is related to the extent the primary investigator can be confident that the cause-and-effect relationship found cannot be explained by other factors (Leedy & Ormrod, 2020). This makes the project’s conclusions credible and trustworthy (Leedy & Ormrod, 2020). Two factors that affect the internal validity of the project are the participants’ maturation and the instrument (MAP resources). The participants’ maturation could be affected by their recollection, poor memory, or follow-thru. The outcomes of the project would vary over time, affecting the results. One way to decrease this occurrence is to have the participant take the survey during the best time for them and productivity. For example, if an individual is a morning person (have them take the survey in the morning versus the afternoon or late evening). The second factor is the instrumentation process (MAP resources). The primary investigator will educate the nurses regarding the purpose of providing the participants the same time (30 minutes) to ensure the same measures are used during the pre-implementation and post-implementation phases. For example, the pre-implementation test cannot be given for 15 minutes, while the post-implementation test is given for 30 minutes.

Bias is described as any tendency that prevents impartial consideration of a clinical question (Pannucci & Wilkins, 2010). It can occur at any stage of the research, study design, data collection or analysis, and publication (Pannucci & Wilkins, 2010). One potential bias is related to the selection process. The primary investigator will avoid bias by selecting individuals and using strict inclusion and exclusion criteria previously developed for the project. The participants will originate from the specified population.

The second bias is related to recall bias, a systematic error that occurs when the participants do not remember prior events or experiences accurately (Creswell & Creswell, 2018). The project could be affected because the participants are self-reporting to the nurses using the MAP resources. To avoid this type of bias, the nurses will be trained to carefully train each participant using the same method, which will prevent influencing their responses (Creswell & Creswell, 2018).

Ethical Considerations

The primary investigator will abide by the University’s IRB and Belmont report guidelines while conducting the project. The three principles to be followed are respect for participants, justice, and beneficence (Belmont, 1979). The primary investigator and the nurses will show the participants respect by listening, validating their feelings, and answering the questions regarding the education or project. The primary investigator will occasionally monitor the nurse’s interaction with the participants throughout the project. The participants will be instructed that there are no repercussions to their personal or professional lives upon withdrawing from the project. The primary investigator and the nurses will always protect the participants’ privacy and confidentiality by not discussing the project, the participants, or its findings with anyone not involved in the project or without the participant’s permission.

Beneficence will be shown to the participants by informing the participants that the primary investigator or the nurses will stop the questioning immediately if they feel emotionally harmed. A psychological resource will be provided to participants who feel affected by the questions or project. All participants will be informed of the risks, benefits, and minimal harm that can occur to them, such as loss of data, social or emotional conflict with family and friends, and anxiety or depression.

The Belmont Report (1979) states justice is the “distribution of the burden.” During this project, it is possible that the participants could perceive unwanted stigma from the colleagues, family members, or friends. Each participant will be treated uniformly following their wishes, so it will not affect the project’s findings. There could be a potential conflict of interest with the project since the primary investigator works at the facility. To minimize the conflict, the primary investigator will not interact with the participants.

Limitations

The limitations of the project are self-reporting of medication adherence by the patients. To minimize this limitation, the primary investigator has validated the self-reporting instrument (MAP resources) before utilizing it for data collection (Althubaiti, 2016). Furthermore, the patient’s self-reporting will be compared to their fasting blood glucose levels, medical records, or reports from family and friends (Althubaiti, 2016).

The second limitation is the healthcare organization being impacted by the COVID-19 pandemic. The new COVID-19 guidelines have affected the current healthcare delivery model. The pandemic has caused the primary investigator to redirect resources and halt in-person training sessions for the nurses. The recruitment process has been limited to Zoom meetings and telephone calls. The third limitation is the location of the project and its setting. The project findings cannot be generalized to other home healthcare agencies of similar populations. The fourth limitation is the time to conduct the project (four weeks). A longer timeframe would help the primary investigator analyze the site’s challenges, trends, and sustainability.

Summary

Medication adherence among Type II diabetic home health patients remains a critical factor in their quality of life. The purpose of this quantitative quasi-experimental project is to determine if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks. A quasi-experimental design will allow the primary investigator to evaluate the impact of the MAP resources and educational intervention on the dependent variable (medication adherence rates). The medication adherence rates, and weekly glucose levels will be collected before and after project implementation (four weeks). Data will be collected by the primary investigator and stored on the digitally protected laptop and hard copies will be locked in a secured file cabinet at the residence. Chapter 4 provided a summary of the topic, along with descriptive data of the participants. Other sections consisted of the data analysis procedure, project findings, and summary.

Chapter 4: Data Analysis and Results

It is not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas. The stakeholders have cited that medication adherence among diabetic patients is lacking. According to data obtained from the site’s electronic health record (EHR), home healthcare providers documented that ten percent of diabetic home healthcare patients are not adhering to their medication regimens.

 A quantitative quasi-experimental project will be conducted to address the clinical question: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks? Data on medication adherence will be collected for the comparative group and compared to an implementation patient group.

Chapter 4 presented the descriptive data for the patient sample. The data analysis procedures are outlined, and the results are presented using narrative and chart format. The chapter concluded with a summary of the findings regarding the clinical question and the significance of the data analysis.

Descriptive Data

The quality improvement project will use a quantitative, quasi-experimental approach for data collection. The targeted population for the project is from a home health care facility in urban Texas. The primary investigator used a G* power version 3.1.9.7, effect size 0.3, power 0.95, and df 0.5 to calculate the sample size needed for the project (N=220) for a significant level. The participants will complete a five-item demographic questionnaire comprised of (age, gender, education level, type of medication (oral or insulin), years as Type II diabetic).

A total of XX patients will be included in the project, n= XX in the comparative group and n= XX in the implementation group. The descriptive data will be displayed in Table 1. It shows X males (xx.x%) and x females (xx.x%) in the comparative group and x males (xx.x%) and x females (xx.x%) in the implementation group. The mean age of the participants was xx.xx (SD= xx.xx) with a range from x-to-x years of age for the comparative group and the mean age of the implementation group with a range from x to x years of age (SD = xx.xx). The educational level of the comparative and implementation patients x (xx.xx%) graduated from high school, x (x.xx%), did not graduate from high school, x (x.xx%), had some college but did not finish x (x.xx%), Associate degree, x (x.xx%), Bachelor’s degree, x (x.xx%), and higher-level doctorate, PhD, MD, or JD. A total of x participants (xx%) took insulin and x (x.xx%) are on oral medications.

Table 1

Descriptive Data for Comparative and Implementation Patients (N = XX)

VariableComparative (n = xx)Implementation (n = xx)
Gender    
   MaleXx.xxXx.xx
   FemaleXx.xxXx.xx
Did not Graduate High School    
   Graduate High SchoolXx.xxXx.xx
   Some CollegeXx.xxXx.xx
   Associate DegreeXx.xxXx.xx
   Bachelor’s degreeXx.xxXx.xx
   DoctorateXx.xxXx.xx
Oral or Insulin    
   OralXx.xxXx.xx
   Insulinxx.xxxx.xx
 MSDMSD
Agexx.xxxx.xxxx.xxxx.xx
Years with Type II Diabetesxx.xxxx.xxxx.xxxx.xx

Data Analysis Procedures

The data analysis procedures will include evaluating de-identified data of medication adherence rates four weeks prior and four weeks post-implementation of the project. The primary investigator will abstract a PDF report of the medication adherence rates for both the comparative and implementation groups. Raw data will be input into a Microsoft Excel (2016) file (codebook). The independent variable is the MAP resource implementation (categorical), and the dependent variable is the medication adherence rates (yes/no). After data entry in Microsoft Excel is completed, data will be exported to IBM SPSS version 27.

To ensure data will be prepared for analysis, a preliminary analysis of all variables was conducted to determine if the dataset has missing data or inaccurate entries. If data is missing, it will be assigned a -99. If 50% of the questionnaire is not completed, the data will not be used in the project. This included frequency counts for variables to check for missing data and values outside of the possible range of 0= no medication adherence and 1= medication adherence. A chi-square test will be conducted, and the results discussed to answer the clinical question. The chi-square test will compare the association between two independent categorical variables (Schober & Vetter, 2019), which will compare the medication adherence rate for patients 30 days before and 30 days after the implementation, thereby answering the clinical question. The significance level will be set to .05, indicating a p-value of less than .05 would reveal statistical significance.

The patient outcome-dependent variable will be collected from the electronic medical records (Cradle Solutions) within the project site. Electronic medical records are considered a reliable and valid source for data collection. A study conducted by McGinnis et al. (2009) examined EMR and written records. The results demonstrated the EMR-based data validity was shown to be moderate to excellent, with Pearson r correlations ranging from .875 to .99 for EMR and documentation records (McGinnis et al., 2009). Electronic medical records are considered a reliable source of data, as emphasized by Goulet et al. (2007), found strong agreement (Kappa between .86 and .99) and high sensitivity and specificity (≥.95) for quality measures based on electronically abstracted structured data compared with manual review.   

One identified potential error is related to the data is coverage error, which results in a difference between the sample size and the population measured (Qualtrics, 2020). To reduce the chances of this occurring, the primary investigator will utilize a recruitment method accessible to all potential participants (such as word of mouth, text messages, and emails). The random error related to the quality improvement project is the measurements (Leedy & Ormrod, 2020). The error could occur after the primary investigator collects the data while being processed (Leedy & Ormrod, 2020). To minimize the chances of errors, the primary investigator has hired a statistician to interpret the data patterns using statistical tests and perform data cleaning (Leedy & Ormrod, 2020).

Results

A chi-square test will be conducted to answer the clinical question: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks? The results are displayed in Table 2. There was an increase in medication adherence from the comparative (n = X, XX.X%) to the implementation group (n = X, XX.X%), X2 (1, N = xx) = x.xx, p =. xxx. The p-value was [less] than .05, which indicates that the increase in medication adherence was statistically significant.

Table 2

Medication Adherence Rates in the Comparative and Implementation Groups

  VariableComparative (n = xx)Implementation (n = xx)X2p-value
n%n%
Medication Adherencexxx.xxxx.xx.xx.xxx

The results of the chi-square test analysis support the implementation of MAP resources to improve medication adherence as compared to current practice among Type II diabetic home healthcare patients, ages 35 to 64 of a home healthcare organization. The rate increases in the implementation group and the p-value is less than .05 indicating (statistical or no statistical significance). Given these findings, the data analysis supports statistical and clinical significance of implementation of the MAP resources for improving medication adherence rates.

Summary

The purpose of this quantitative quasi-experimental project was to evaluate the impact of the Medication Adherence Project (MAP) resources on patient medication adherence rates for home health Type II diabetics. Data on medication adherence will be collected from the site’s EMR for four weeks before the SBAR intervention and for four weeks after the intervention. A total of XX patients were included in the study (n = xx in the comparative group and n = xx in the implementation group). The medication adherence rate was compared between the comparative and implementation patient groups using a chi-square test to address the project’s clinical question. There was an increased in medication errors from the comparative (n = X, XX.X%) to the implementation group (n = X, XX.X%), X2 (1, N = xx) = x.xx, p =. xxx. These results (showed or not showed) statistically significant increase in medication adherence after the MAP resource intervention compared to the comparative group and support the use of the MAP to improve medication adherence for adult home health patients with Type II diabetes.

Chapter 5 offered a summation of the results and conclusions based on the findings showing increased medication adherence after the MAP resource implementation. The theoretical and practical implications of the results will be summarized. The chapter concluded with recommendations for future projects, including adult home health patients with Type II diabetes, concerning the project findings that support MAP resources to improve medication adherence rates.

Chapter 5: Summary, Conclusions, and Recommendations

Diabetes impacts approximately one in ten Americans (Centers for Disease Control and Prevention, 2020). The prevalence of the disease continues to rise and is expected to grow by 0.3% annually until 2030 (Lin et al., 2018). This particularly troublesome for Type II home healthcare patients diagnosed with the disease. Polonsky and Henry (2016) emphasized that roughly 45% of this population fail in sustaining a normal glucose level. Poor medication adherence is associated with increased morbidity and mortality rates, finances, hospital readmissions, and diminished quality of life (Polonsky & Henry, 2016).

This quality improvement project was developed to address a standardized method for healthcare providers to assess their patients’ medication adherence. A quantitative, quasi-experimental design contributed to the participants promoting self-reliance and increased knowledge levels in maintaining healthier glucose levels. Furthermore, the project improved the practitioner’s awareness of the need to evaluate their patient regarding medication adherence frequently. The project provided current information related to Type II diabetic home health patients and medication adherence, which validated other studies such as Heath (2019) and Sharma et al. (2020).

Chapter 5 summarized the project related to Type II diabetic home health patients and medication adherence. Other segments comprised of the summary of the project’s findings and conclusions. The theoretical (Orem’s self-care deficit theory and Roger’s diffusion of innovation model), practical, and future implications were discussed. The last section consisted of recommendations for future projects and clinical practices.

Summary of the Project

The clinical question that directed the project was: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks?  A chi-square test was conducted for a comparison of the medication adherence rates for the patients 30 days prior and 30 days post-implementation. A level of significance was set to .05, which indicated a p-value of less than.05 would reveal statistical or non-statistical significance.

A convenience sampling was used to recruit N=XX participants for the comparative group and N=XX for the implementation group. The nurses (XX) were educated regarding the use of the MAP resources. A retrospective chart audit (n=XX) was done to evaluate the medication adherence rates before the project implementation. The chi-square test was utilized to determine the variations among the two groups for statistical difference.

Summary of Findings and Conclusion

A sample size of N=XX participants was compared utilizing a chi-square test with the significance level at p <.05. Two groups were compared comparative (n=XX) and implementation (n=XX). The number of medication adherence rates were evaluated four weeks pre-implementation and post-implementation of the project. The clinical question that was answered using the chi-square analysis was: To what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients of a home healthcare agency in urban Texas over four weeks? There was an increase or decrease in medication errors from the comparative group (n= XX%) to the implementation (n=XX%), X2 [1, N=XX] = X.XXX, p= X. XX. These results (showed or not showed) statistical significance increase in medication adherence rates after the MAP resource intervention compared to the comparative group. It demonstrated the need for consistent use and the importance of healthcare providers to evaluate their diabetic patients frequently for medication adherence with each home health visit.

Implications

Nursing is a practice discipline, therefore when a quality improvement project is conducted it should focus on issues that directly affected the nursing practice (Polit & Beck, 2018). In this project, the emphasis is on patient care and offering the potential clinical consequences that impacted the findings (Polit & Beck, 2018). The theoretical, practical, and future implications are based on the data from the project and the literature preceding it.

Theoretical Implications

Orem’s self-care deficit theory was selected because it aligned appropriately with the clinical question that directed the project. The theory assisted the primary investigator and the staff nurses to implement the project solely based on the self-care requirements of the home health Type II diabetic patients. The theory helped build a foundation for the project by integrating strategies that aided the participants to understand their disease and sustain self-care management. Orem’s self-care deficit theory is comprised of three components: theory of self-care, self-care deficit, and the nursing system (RenpenningcN et al., 2003). The services designed were focused on the a) abilities and actions related to medication adherence of the participant, b) staff nurses coordinating resources for the diabetic patients, monitoring the disease, assessing the patient’s medication adherence using a patient-centered approach (Orem, 1985).

The strength of Orem’s self-care deficit theory allowed the primary investigator to provide the nurses an increased awareness in understanding their patients while addressing barriers that could impact them from understanding and maintaining medication adherence. In Chapter 2, the literature review examined how patients could effectively manage medication adherence while contributing to previous literature that utilized the theory on Type II diabetic patients (Borji et al., 2017; Ebrahimi, 2015; Ghafourifard & Shahbaz et al., 2016). One strength noted in the project was the increased curiosity and desire to learn exhibited by the patients. This was related to the nursing staff using a patient-centered approach in addressing their medication adherence. The patients verbalized that they appreciated the extra time that the nurses spent with them regarding how to maneuver the chronic disease.

The weakness of the project was teaching the nurses to become familiar with Orem’s self-care deficit theory, which was the foundation for the project. Orem’s theory can be utilized and implemented in other projects related to Type II diabetic home healthcare patients, since the findings cannot be generalized. A second weakness noted was the time restrictions to conduct the project (four weeks). A longer time frame would have allowed the primary investigator to observe the nurses and patient interactions, trends, and analyze obstacles that prevented an individual from maintaining medication adherence procedures.

Practical Implications

One practical implication included the agency evaluating and developing medication adherence guideline patient-specific using the MAP resources and Orem’s theory. Many of the nurses suggested that one of the monthly home visits should be dedicated solely to the patient’s current medication list and medication adherence. Another suggestion was to incorporate a text-messaging component from the primary nurse via the patient’s cell phone to remind them to take their medications. The last practical implication was related to the nursing staff not confronting the patient regarding their medication adherence status; instead, develop interventions tailored to their needs Sansbury et al., 2014). Using strategies such as goal setting, behavior contracts, or having an accountability partner could decrease the challenges in medication adherence (Sansbury et al., 2014).

Future Implications

One future implication related to the project is for other quality improvement projects to examine medication adherence rates among teenagers in the home healthcare settings. This should utilize medication adherence strategies specific to their age group. A second implication is related to diabetic medications, home health patients should be encouraged to participate in phase three trials for new diabetic products that would enhance medication adherence. These products are becoming available and provided to the participants at monthly or longer intervals. This would address some of the short-term barriers to sustaining medication adherence (Polonsky & Henry, 2016).

The second future implication is for the nurses to implement strategies for medication adherence based on the participant’s demographic characteristics (race, gender, age, personal preferences, culture, and social determinants) (Williams et al., 2014). The factors that affect the patient should be identified and addressed as they appear to allow greater control of the disease (Williams et al., 2014). A systems approach towards medication adherence would help achieve higher effectiveness, adherence, healthcare outcomes, and decrease healthcare costs (Williams et al., 2014).

Recommendations

Recommendations provide a firm foundation for the nursing workforce by ensuring they are adequately educated and prepared to implement the practice fully (Institute of Medicine, 2011). They are needed to meet their patients’ future health care needs and lead as change agents within the healthcare arena (Institute of Medicine, 2011). For this home health agency implementing and sustaining the recommendations will take time, finances, resources, and commitment from the staff. In the following few paragraphs, the primary investigator addressed recommendations for future projects and clinical practices.

Recommendations for Future Projects

The first recommendation is for those projects to utilize a standardized assessment strategy to evaluate their patient’s medication adherence behaviors and practices. Inaccurate medical records and inadequate medication assessment result in poor healthcare outcomes and minimum patient engagement in the decision-making. Educating the diabetic patients regarding the need for medication adherence would help them remain compliant. The best determinant for medication adherence is for patients to demonstrate via their behavior the change.

The second recommendation is to conduct the project use a larger population size focused on the caregivers of diabetic patients. Focusing the attention on this sector would emphasize the emotional and family support to help the patient remain compliant. Since many Type 2 diabetic patients have friends, family, or caregivers in their circle, it would be significant to include them in the discussion and the importance of medication adherence. This would allow a greater understanding of the subject and generalization of the project findings on this populace.

The next steps in moving this type of project forward are for the home health agency to implement and sustain the MAP resources for maximum impact on the patients. The continued use of the assess tool would help decrease frequent hospitalizations, financial expenses, and increased quality of health. Adopting the project should be specific to the home health agency’s specific needs and demands, which would enhance the project’s sustainability.

Recommendations for Practice

One recommendation for current practices is for home health nurses to offer other options to help their patients remain medication adherence compliant. Kirkman et al. (2015) suggested via their project findings that encouraging patients to use mail-order pharmacies increases the patient’s chance for medication adherence. An analysis conducted by Medicare Part D showed an increase in medication adherence by diabetic patients (Kirkman et al., 2015). Another suggestion is the use of a medication events monitoring system to evaluate the patient’s medication adherence. The device would be incorporated into the patient’s packaging of the prescription medication (Lam & Fresco, 2015). It records the dosing events and stores the information with audiovisual reminders. The last option is to receive automated electronic reminders such as (text messages) using REMIND software from the visiting home health nurse.

The second recommendation is for future clinic practices to establish and educate the nursing staff on cultural competency care. This type of nurse-patient relationship allows a stronger connection with the patient who feels comfortable expressing the concerns and knowledge deficits because of a non-judgmental environment that helps them maintain medication adherence behaviors. Effective communication restores and improves patients’ capability to cope with Type II diabetes and improve their patient outcomes (Aloudah et al., 2018).

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of Diabetes, 4(6), 270-281. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874486/#!po=10.0000

Rodríguez-Saldana, J. (2019). The diabetes textbook: Clinical principles, patient management and public health issues. Springer International Publishing.

Rogers, E. (2003). Diffusion of Innovations. (5th ed). Free Press

Sawkin, M., Deppe, S., Thelen, J., Stoner, S., Dietz, C., & Rasu, R.  (2015).

Health literacy and medication adherence among patients treated in a free health clinic. Health Services Research and Managerial Epidemiology2, 233339281558909. https://doi.org/10.1177/2333392815589094

Schober, P., & Vetter, T. (2019). Chi-square tests in medical research Anesthesia &

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Schweizer, M., Braun, B., & Milstone, A.  (2016). Research methods in healthcare epidemiology and antimicrobial stewardship—quasi-experimental designs. Infection Control & Hospital Epidemiology37(10), 1135-1140. https://doi.org/10.1017/ice.2016.117

Sertbas, M., Guduk, O., Guduk, O., Yazici, Z., Dagci, S., & Sertbas, Y. (2019). Current situation analysis of diabetic home care patients. Northern clinics of Istanbul7(2), 140–145. https://doi.org/10.14744/nci.2019.59751

Shahbaz, A., Maslakpak, M., Nejadrahim, R., & Khalkhali, H. (2016). The effect of

implementing Orem’s self-care program on self-care behaviors in patients with diabetic foot ulcer. Journal of Urmia Nursing and Midwifery Faculty, 14, 108-117.

Shahin, W., Kennedy, G., & Stupans, I. (2019). The impact of personal and cultural

beliefs on medication adherence of patients with chronic illnesses: a systematic review. Patient Preference and Adherence13, 1019–1035. https://doi.org/10.2147/PPA.S212046

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support on glycemic control in patients with type 2 diabetes mellitus: The mediating roles of self-efficacy and adherence. Journal of Diabetes Research, 2017, Article ID 2804178, 1-8. https://doi.org/10.1155/2017/2804178

Sharma, S. K., Kant, R., Kalra, S., & Bishnoi, R. (2020). Prevalence of primary non-adherence with insulin and barriers to insulin initiation in patients with type 2 diabetes mellitus – An exploratory study in a tertiary care teaching public hospital. European Endocrinology16(2), 143. https://doi.org/10.17925/ee.2020.16.2.143

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Appendix A

The 10 Strategic Points
Broad Topic AreaImproving Medication Adherence among Type II Home Healthcare Diabetic Patients
Literature Review
Problem StatementIt is not known if or to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients, ages 35 to 64 of a home healthcare organization located in urban Texas.  
Clinical/
PICOT Questions
To what degree does the implementation of Medication Adherence Project resources, which include the Questions to Ask Pad, the Questions to Ask Poster, an Adherence Assessment Pad, and the My Medications List impact medication adherence among Type II diabetic home healthcare patients, ages 35 to 64, of a home healthcare organization located in urban Texas over a period of four weeks? The following clinical question will guide this quantitative project: Q1: Does using the MAP resources improve medication adherence among home health diabetic patients?  
Sample 
Define Variables 
Methodology and DesignMethodology and Design: A quantitative methodology, which employs a quasi-experimental design, will be used to examine medication adherence rates pre-project implementation and post-project implementation. Statistical analyses will be used to compare pre-and post-project data. Demographic data will be collected because the prevalence of non-adherence is often high among certain groups (e.g., impacted by socioeconomic status, gender, age, etc.).  
Purpose StatementPurpose Statement: The purpose of this quantitative quasi-experimental project is to determine if or  to what degree the implementation of the New York City Department of Health and Mental Hygiene Medication Adherence Project (MAP) resources, which include (1) the questions to ask poster, (2) adherence assessment pad, and (3) my medications list, impact patient medication adherence rates when compared to current practice among Type II diabetic home healthcare patients, ages 35 to 64 of a home healthcare organization located in urban Texas.  
Data Collection ApproachData Collection Approach: The source of data for this project is the electronic medical record. Each week, nursing staff members will record medication adherence information in the patient’s EMR. If the patient expresses that he/she has not adhered to the medication regiment, during the previous week, lacking adherence information will be recorded in the system. Upon the completion of the four-week project, all information, input by nursing staff members into the EMR, will be assessed. The PI will compare pre-project implementation medication adherence rates to post-project implementation medication adherence rates. In addition to exploring medication adherence rates after the implementation of this project, pre-project implementation adherence rates will be explored over four weeks from April 1, 2021 to April 30, 2021. Once pre-project implementation data and post-project implementation data are obtained, the results will be statistically analyzed. The PI will work with a statistician, who will assist in the data analysis process. Data will be compared analyze using various statistical techniques.  
Data Analysis ApproachData Analysis Approach: The facility uses Cradle Solutions, software for home health companies. It serves the specialized needs of home health care providers that give a web-based point-of-contact information entry and management. The data will be collected using the project site’s EHR and will be presented to the PI by the secretary in a Microsoft Excel document. Data will be input into SPSS version 28 and analyzed using a t-test with a p-value of 0.05.
ReferencesBosworth, H. B. (2015). Enhancing medication adherence: The public health dilemma. Philadelphia, PA: Springer Healthcare. Brown, M. T., & Bussell, J. K. (2011). Medication adherence: WHO Cares? Mayo Clinic Proceedings86(4), 304-314. Retrieved from https://doi.org/10.4065/mcp.2010.0575 Hunter, J., & Maunder, R. (2016). Improving patient treatment with attachment theory: A guide for primary care practitioners and specialists. Switzerland: Springer International Publishing. Starr, B., & Sacks, R. (2010). Improving outcomes for patients with chronic diseases: The Medication Adherence Project (MAP). NYC Health. Retrieved from https://www.hfproviders.org/documents/root/pdf_9a3a46fa03.pdf Voortman, T., Kiefte-de Jong, J., Ikram, M. A., Stricker, B. H., van Rooij, F. J. A., Lahousse, L., … Schoufour, J. D. (2017). Adherence to the 2015 Dutch dietary guidelines and risk of non-communicable diseases and mortality in the Rotterdam Study. European Journal of Epidemiology32(11), 993-1005. https://doi.org/10.1007/s10654-017-0295-2  

 

Appendix B

MAP Resources

Appendix C

Permission to Use the MAP Resources

Per the website of Starr and Sacks (2010), the MAP tools are available free of charge. Tools can be downloaded from 

https://hfproviders.org/documents/root/pdf_9a3a46fa03.pdf

Appendix B 

What is my DPI project design?

THIS IS NOT PART OF THE PAPER JUST A REFERENCE FOR THE LEARNER

Appendix C

Power Analysis Using G Power

Note: Public source G-Power Software available https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html

Appendix D

Example SPSS Dataset & Variable View

The SPSS database is set up with all variables coded to compare between or within the comparison groups. A comparison may be made within the same individual and it coded 1 for before and 2 after the intervention.  Or if measuring between individuals, the data would be coded the same 1 for before and 2 after as noted in the Group Column. Software supplied by Grand Canyon University.

Appendix E

How to Make APA Format Tables and Figures Using Microsoft Word

 Tables vs. Figures

  • See APA Publication Manual, Chapter 7 for additional details (APA, 2019).
    • Tables consist of words and numbers where spatial relationships usually do not indicate any numerical information.
    • Tables should be used to present information that would be too wordy, repetitive, or difficult to read as text.
    • Figures typically communicate numerical information using spatial relations. For example, as you move up the Y axis of bar graph the scores usually go up. 
  1. Examples of APA Tables

   A. Descriptive table

Table 1

Characteristics of Variables

VariableVariable TypeLevel of Measurement
Group, Intervention or ToolIndependentNominal  
Rates or eventsDependentNominal  
Socio Economic Status or Categories in an orderDependentOrdinal  
Time, TemperatureDependentInterval  
Age, height, Scores of testsDependentRatio

Note. Add notes here = (Provide any reference, 2019).

Table 1

Number of Handoff Per Groups

Group# of Handoffs (%)
Pre-Intervention Group (Baseline)150 (50%)
SBAR Group150 (50%)
  

Note. SBAR handoff was defined as …. (IHI, 2020)

Table 1

Number of Hours Per Week Spent in Various Activities

GroupBaseline (n = 30)Post Intervention (n = 30)Total Sample  (n = 60)
 M (SD)M (SD)M (SD)
Schoolwork18.23 (7.79)16.23 (3.99)17.63 (1.2)
Physical activities19.54 (3.63)14.23 (2.84)*18.67 (1.0)
Socializing16.23 (3.99)17.63 (1.2)18.23 (7.79)
Watching television14.23 (2.84)18.67 (1.0)19.54 (3.63)
Extracurricular activities19.54 (3.63)18.23 (7.79)19.22 (5.45)

Note. Schoolwork was defined as time spent doing class work outside of regular class time.

*statistically significant at p <.05 

B. Chi-Square example (Group IV x Group DV) 

 Table 1

Crosstabulation of Gender and Chronic Pain

Chronic PainGender  
FemaleMaleχ2Φ
  Yes2 (-2.7)  8 (2.7)7.20**,60
  No8 (2.7)  2 (-2.7)  

Note. Adjusted standardized residuals appear in parentheses below group frequencies.

**= p < .01.

C. t-Test Example (Dichotomous Group IV x Score DV) –Notice two separate t-test results have been reported.

Table 1

Chronic Paint Score and Exercise time for Males and Females

 Gender  
FemaleMaleTdf
  Pain Score  3.33    (1.70)  3.75    (1.79)-2.20*175
  Exercise Time  4.28 (.7509)  3.87 (.9280)   4.2**176

Note. Standard Deviations appear in parentheses below means.

* = p < .05, *** = p < .001.

D. One Way ANOVA with 3 Groups Example (Group IV x Score DV)

Remember with an ANOVA, you have to report paired comparisons associated with post hoc or planned comparisons) for significant analyses. The results of paired comparisons are indicated by the subscripts on the means within rows. Also, notice in this table that we report the results of four separate analyses. This is the real power of tables: we can convey a large amount of information very concisely.

Table 1

Analysis of Variance for Sleep Times and Experimental Groups

 Experimental Group  
Aerobic ExerciseWeight LiftingNo ExerciseFη2
Total Sleep Time8.23a (.55)7.93b     (.90)7.73ab (.55)  3.98***  .18
Total Wake Time3.58a (.70)3.62a    (.55)3.54a    (.90).03.00
Total Light Sleep3.19c (.73)2.80a     (.72)3.02b    (.49)2.95*.06
Total Deep Sleep3.21b (.19)3.10a      (.28)3.30a     (.19).20.01

Standard deviations appear in parentheses bellow means. Means with differing subscripts within rows are significantly different at the p < .05 based on Fisher’s LSD post hoc paired comparisons.

* = p < .05, *** = p < .001.

E. Factorial ANOVA Example 2 x 3 between subject’s design.

Notice that two tables are used here. The first table reports the overall results for the 2×3 factorial ANOVA, which includes the Main Effects for the two IV’s and the Interaction Effect for the two IV’s. The second table reports the means and simple effects tests for the significant interaction effect.

Table 1

 Experimental Group x Sex Factorial Analysis of Variance for Sleep Scores

SourceDfFη2p
  Experimental Group  2    7.93  .17  .001
  Sex    1    31.41     .34    .001  
Group x Sex (interaction)  2   7.85  .17  .002  
Error (within groups)30   

Table 1

Analysis of Sleep Scores for Experimental Groups by Gender

 Aerobic ExerciseWeightliftingNo ExerciseSimple Effects: F df (2, 30)
  Males10.37a   (2.50)10.30a     (2.34)10.33a (1.63).04
  Females4.83a   (1.60)10.50b     (2.59)4.50a   (1.52)15.74**
  Simple Effects: F df (1, 30)  23.56**    .00  23.56** 

Note. Standard deviations appear in parentheses bellow means. Means with differing subscripts within rows are significantly different at the p < .05 based on Fisher’s LSD post hoc paired comparisons. 

** = p < .01

Notice that the simple effect comparing the 3 experiment groups only for females, requires follow up tests in order to determine which groups are significantly different. In this case, Fisher’s LSD test was used, and the results are represented with the different subscripts for each mean. In this case, female participants in the Aerobic exercise group did not differ from the no exercise group so they are given the same subscript (a). However, women in the control group and women in the Weightlifting group significantly differed from the Aerobic watching group and so the Weightlifting group was labeled with a different subscript (b). The male subjects did not differ from one another, so they all share the same subscript (a).

F. Correlations (Scores IV x Scores IV)

Table 1

Pearson’s Product Moment Correlations for Chronic Pain Score, Exercise Attitude Scores and Physical Activity

 Demographic Influences on Exercise
 
    WeightAge
 Chronic Pain Score   Pain Level       .39***     -.07
  Pain Intensity   .15    .22*
  Physical Exercise   Type of Exercise      -.26**    -.19
  Time of Exercise  -.13    -.21*  
Intent to Exercise .02-.10

Note. N = 96 for all analyses.

= p < .10, *= p < .05, **= p < .01, ***= p < .001.

  1. Examples of APA Figures

Generally, the same features apply to figures as have been previously provided for tables: They should be easy to read and interpret, consistent throughout the document when presenting the same type of figure, kept on one page if possible, and supplement the accompanying text or table.

Figure 1

Graph of Scores Before and After

Note: Reprinted from S. GCU. Or Adapted from or www.website.com. Reprinted with permission.

If the figure is not your own work, note the source or reference where you found the figure. Write, “Reprinted from” or “Adapted from,” followed by the title of the book, article, or website where you found the figure. Include the page number where you found the figure as well if you are citing a figure from a book. If you are citing a figure from a website, you may write, “Reprinted from The Huffington Post.”  Or include the author’s first and second initial as well as their surname. Use the author’s first and second initial, if available, rather than the author’s full first name. Note their last name as well.

References:

American Psychological Association [APA]. (2019). Publication manual of the American Psychological Association. (7th ed.). Washington, DC; Author

Microsoft Word ®. (2019). Retrieved from https://products.office.com/

Appendix F

Writing up your statistical results

Identify the analysis technique.

In the results section (Chapter 4), your goal is to report the results of the data analyses used to answer your project question. To do this, you need to identify your data analysis technique, report your test statistic, and provide some interpretation of the results. Each analysis you run should be related to your clinical question or PICOT. If you analyze data that is exploratory or outside your clinical question, you need to indicate this in the results.

Format test statistics.

Test statistics and p values should be rounded to two decimal places (If you are providing precise p-values for future use in meta-analyses, 3 decimal places are acceptable). All statistical symbols (sample statistics) that are not Greek letters should be italicized (M, SD, t, p, etc.).

Indicate the direction of the significant difference.

When reporting a significant difference between two conditions, indicate the direction of this difference, i.e. which condition was more/less/higher/lower than the other condition(s). Assume that your audience has a professional knowledge of statistics. Do not explain how or why you used a certain test unless it is unusual (i.e., such as a non-parametric test).

How to report p values.

Report the exact p value (this is the preferred option if you want to make your data convenient for individuals conducting a meta-analysis on the topic).

Example: t(33) = 2.10, p = .03.

If your exact p value is less than .001, it is conventional to state merely p < .001. If you report exact p values, state early in the results section the alpha level used as a significance criterion for your tests. For example: “We used an alpha level of .05 for all statistical tests.”

If your results are in the predicted direction but are not significant, you can say your results were marginally significant. Example: Results indicated a marginally significant preference for pie (M = 3.45, SD = 1.11) over cake (M = 3.00, SD = .80), t(5) = 1.25, p = .08.

If your p-value is over .10, you can say your results revealed a non-significant trend in the predicted direction. Example: Results indicated a non-significant trending in the predicted direction indicating a preference for pie (M = 4.25, SD = 2.21) over cake (M = 3.25, SD = 2.60), t(5) = 1.75, p = .26.

Descriptive Statistics

Mean and Standard Deviation are most clearly presented in parentheses:

• The sample as a whole was relatively young (M = 19.22, SD = 3.45).

• The average age of students was 19.22 years (SD = 3.45).

Percentages are also most clearly displayed in parentheses with no decimal places:

• Nearly half (49%) of the sample was married.

Frequencies or rates are reported including the range, mode, or median.

t-tests

There are several different designs that utilize a t-test for the statistical inference testing. The differences between one-sample t-tests, related measures t-tests, and independent samples t tests are clear to the knowledgeable reader so eliminate any elaboration of which type of t-test has been used. Additionally, the descriptive statistics provided will identify which variation was employed. It is important to note that we assume that all p values represent two-tailed tests unless otherwise noted and that independent samples t-tests use the pooled variance approach (based on an equal variances assumption) unless otherwise noted:

• There was a significant effect for gender, t(54) = 5.43, p < .001, with men receiving

higher scores than women.

• Results indicate a significant preference for pie (M = 3.45, SD = 1.11) over cake (M = 3.00, SD = .80), t(15) = 4.00, p = .001.

• The 36 study participants had a mean age of 27.4 (SD = 12.6) were significantly older

than the university norm of 21.2 years, t(35) = 2.95, p = .01.

• Students taking statistics courses in psychology at the University of Washington reported studying more hours for tests (M = 121, SD = 14.2) than did UW college students in general, t(33) = 2.10, p = .034.

• The 25 participants had an average difference from pre-test to post-test anxiety scores of -4.8 (SD = 5.5), indicating the anxiety treatment resulted in a significant decrease in

anxiety levels, t(24) = -4.36, p = .005 (one-tailed).

• The 36 participants in the treatment group (M = 14.8, SD = 2.0) and the 25 participants in the control group (M = 16.6, SD = 2.5), demonstrated a significance difference in

performance (t[59] = -3.12, p = .01); as expected, the visual priming treatment inhibited

performance on the phoneme recognition task.

• UW students taking statistics courses in Psychology had higher IQ scores (M = 121, SD = 14.2) than did those taking statistics courses in Statistics (M = 117, SD = 10.3), t(44) =

1.23, p = .09.

• Over a two-day period, participants drank significantly fewer drinks in the experimental group (M= 0.667, SD = 1.15) than did those in the wait-list control group (M= 8.00, SD= 2.00), t(4) = -5.51, p=.005.

ANOVA and post hoc tests.

ANOVAs are reported like the t test, but there are two degrees-of-freedom numbers to report. First report the between-groups degrees of freedom, then report the within-groups degrees of freedom (separated by a comma). After that report the F statistic (rounded off to two decimal places) and the significance level.

One-way ANOVA:

• The 12 participants in the high dosage group had an average reaction time of 12.3.

seconds (SD = 4.1); the 9 participants in the moderate dosage group had an average

reaction time of 7.4 seconds (SD = 2.3), and the 8 participants in the control group had a

mean of 6.6 (SD = 3.1). The effect of dosage, therefore, was significant, F(2,26) = 8.76,

p=.012.

• A one-way analysis of variance showed that the effect of noise was significant, F(3,27) = 5.94, p = .007. Post hoc analyses using the Scheffé post hoc criterion for significance indicated that the average number of errors was significantly lower in the white noise condition (M = 12.4, SD = 2.26) than in the other two noise conditions (traffic and industrial) combined (M = 13.62, SD = 5.56), F(3, 27) = 7.77, p = .042.

• Tests of the four a priori hypotheses were conducted using Bonferroni adjusted alpha

levels of .0125 per test (.05/4). Results indicated that the average number of errors was

significantly lower in the silence condition (M = 8.11, SD = 4.32) than were those in both

the white noise condition (M = 12.4, SD = 2.26), F(1, 27) = 8.90, p =.011 and in the

industrial noise condition (M = 15.28, SD = 3.30), F (1, 27) = 10.22, p = .007. The

pairwise comparison of the traffic noise condition with the silence condition was nonsignificant.

The average number of errors in all noise conditions combined (M = 15.2, SD

= 6.32) was significantly higher than those in the silence condition (M = 8.11, SD = 3.30),

F(1, 27) = 8.66, p = .009.

Multiple Factor (Independent Variable) ANOVA

• There was a significant main effect for treatment, F(1, 145) = 5.43, p < .01, and a

significant interaction, F(2, 145) = 3.13, p < .05.

• The cell sizes, means, and standard deviations for the 3×4 factorial design are presented

in Table 1. The main effect of Dosage was marginally significant (F[2,17] = 3.23, p =

.067), as was the main effect of diagnosis category, F(3,17) = 2.87, p = .097. The

interaction of dosage and diagnosis, however, has significant, F(6,17) = 14.2, p = .0005.

• Attitude change scores were subjected to a two-way analysis of variance having two

levels of message discrepancy (small, large) and two levels of source expertise (high,

low). All effects were statistically significant at the .05 significance level. The main

effect of message discrepancy yielded an F ratio of F(1, 24) = 44.4, p < .001, indicating

that the mean change score was significantly greater for large-discrepancy messages (M =

4.78, SD = 1.99) than for small-discrepancy messages (M = 2.17, SD = 1.25). The main

effect of source expertise yielded an F ratio of F(1, 24) = 25.4, p < .01, indicating that the

mean change score was significantly higher in the high-expertise message source (M =

5.49, SD = 2.25) than in the low-expertise message source (M = 0.88, SD = 1.21). The

interaction effect was non-significant, F(1, 24) = 1.22, p > .05.

• A two-way analysis of variance yielded a main effect for the diner’s gender, F(1,108) =

3.93, p < .05, such that the average tip was significantly higher for men (M = 15.3%, SD

= 4.44) than for women (M = 12.6%, SD = 6.18). The main effect of touch was nonsignificant, F(1, 108) = 2.24, p > .05. However, the interaction effect was significant,

F(1, 108) = 5.55, p < .05, indicating that the gender effect was greater in the touch

condition than in the non-touch condition.

Chi Square

Chi-Square statistics are reported with degrees of freedom and sample size in parentheses, the Pearson chi-square value (rounded to two decimal places), and the significance level:

• The percentage of participants that were married did not differ by gender, X2(1, N = 90) = 0.89, p > .05.

• The sample included 30 respondents who had never married, 54 who were married, 26

who reported being separated or divorced, and 16 who were widowed. These frequencies

were significantly different, X2 (3, N = 126) = 10.1, p = .017.

• As can be seen by the frequencies cross tabulated in Table xx, there is a significant

relationship between marital status and depression, X2 (3, N = 126) = 24.7, p < .001.

• The relation between these variables was significant, X2 (2, N = 170) = 14.14, p < .01.

Catholic teens were less likely to show an interest in attending college than were

Protestant teens.

• Preference for the three sodas was not equally distributed in the population, X2 (2, N =

55) = 4.53, p < .05.

Correlations

Correlations are reported with the degrees of freedom (which is N-2) in parentheses and the significance level:

• The two variables were strongly correlated, r(55) = .49, p < .01.

Regression analyses

Regression results are often best presented in a table. A

PA doesn’t say much about how to report regression results in the text, but if you would like to report the regression in the text of your Results section, you should at least present the standardized slope (beta) along with the t-test and the corresponding significance level. (Degrees of freedom for the t-test is N-k-1 where k equals the number of predictor variables.) It is also customary to report the percentage of variance explained along with the corresponding F test.

• Social support significantly predicted depression scores, b = -.34, t(225) = 6.53, p < .01. Social support also explained a significant proportion of variance in depression scores, R2 = .12, F(1, 225) = 42.64, p < .01.

Tables

Add a table or figure.

Adding a table of figure can be helpful to the reader. See the current APA Publication manual for examples. In reporting the results of statistical tests, report the descriptive statistics, such as means and standard deviations, as well as the test statistic, degrees of freedom, obtained value of the test, and the probability of the result occurring by chance (p value).

•APA style tables do not contain any vertical lines

•There are no periods used after the table number or title.

•When using columns with decimal numbers, make the decimal points line up.

•Use MS Word tables to create tables

American Psychological Association [APA].(2019). Publication manual of the American Psychological Association (7th ed.). Washington, DC: Author.