Prediabetes Screening Among Commercial Motor Vehicle Drivers: Does the Use of the American Diabetes Association’s Type 2 Diabetes Risk Test More Effectually Identify Commercial Motor Vehicle Drivers at Risk of Diabetes Versus Sole Reliance on Glycosuria? by Erin Ruiz A DNP Project Submitted to the Faculty of the Department of Nursing In Partial Fulfillment of the Requirements For the Degree of Doctor of Nursing Practice In the Graduate College Bloomsburg University 2022 Project Manager: Dr. Lori Parke Clinical Expert: Dr. Kimberly Olszewski Date of Submission: April 24, 2022 TABLE OF CONTENTS LIST OF TABLES ...........................................................................................................................5 ABSTRACT.....................................................................................................................................6 INTRODUCTION..........................................................................................................................8 Background Knowledge/Significance...........................................................................................8 Commercial Motor Vehicle Drivers .................................................................................8 Diabetes Statistics.............................................................................................................11 Early Risk Factor Identification .....................................................................................12 Conclusion ........................................................................................................................13 Local Problem ..............................................................................................................................14 Intended Improvement ................................................................................................................15 Project Purpose ................................................................................................................15 Project Question ...............................................................................................................15 Project Objectives ............................................................................................................15 Framework ...................................................................................................................................16 Literature Synthesis .....................................................................................................................17 Evidence Search ...............................................................................................................17 Comprehensive Appraisal of Evidence ..........................................................................19 General Driver Health ......................................................................................................19 Diabetes Prevention and Screening .................................................................................20 A1C Point of Care Testing ................................................................................................22 Conclusion.........................................................................................................................24 METHODS ...................................................................................................................................25 Project Design...............................................................................................................................25 Model for Implementation ..........................................................................................................25 Setting and Stakeholders .............................................................................................................28 Planning the Intervention ...........................................................................................................28 TABLE OF CONTENTS - Continued Participants and Recruitment.....................................................................................................29 Consent and Ethical Considerations ..........................................................................................30 Data Collection .............................................................................................................................30 Data Analysis ................................................................................................................................31 Conclusion ....................................................................................................................................32 RESULTS .....................................................................................................................................32 Statistical Methods .......................................................................................................................32 Descriptive Summary of Study Population ...............................................................................33 Association of ADA T2DM Risk Test and Glycosuria with A1C ............................................35 Influence of Age, Gender, and BMI on Screening for Diabetes Risk......................................37 Summary of Results .....................................................................................................................39 DISCUSSION ..............................................................................................................................40 Summary.......................................................................................................................................40 Interpretation ...............................................................................................................................41 Implications ..................................................................................................................................41 Limitations ....................................................................................................................................42 DNP Essentials Addressed .........................................................................................................43 Conclusions ...................................................................................................................................43 Future Implications for Clinical Practice ......................................................................44 Plan for Dissemination ....................................................................................................44 Funding .........................................................................................................................................45 APPENDIX A: LETTERS OF AUTHORIZATION: SITE APPROVAL ...........................................................................................46 IRB APPROVAL .............................................................................................47 SITE AUTHORIZATION ...............................................................................48 PROTECTED HEALTH INFORMATION AND DE-IDENTIFICATION APPROVAL ....................................................................................................49 APPENDIX B: CONSENT DOCUMENT ...............................................................................51 APPENDIX C: EVALUATION INSTRUMENTS: PERMISSION FOR USE ................................................................................53 AMERICAN DIABETES ASSOCIATION TYPE 2 DIABETES RISK TEST ................................................................................................................55 APPENDIX D: PARTICIPANT MATERIAL: PERMISSION FOR USE ................................................................................53 AMERICAN DIABETES ASSOCIATION PREDIABETES EDUCATIONAL HANDOUT ........................................................................56 APPENDIX E: FRAMEWORK DOCUMENTS: PERMISSION FOR USE ................................................................................58 IOWA MODEL REVISED..............................................................................59 APPENDIX F: PROJECT TIMELINE .....................................................................................60 APPENDIX G: LITERATURE REVIEW GRID......................................................................61 APPENDIX H: DNP ESSENTIALS .........................................................................................64 APPENDIX I: BUDGET .........................................................................................................67 REFERENCES ..........................................................................................................................68 TRUCK DRIVERS AND DIABETES 5 LIST OF TABLES Table 1. Cohort age, gender, BMI.................................................................................................34 Table 2. Cohort A1C results..........................................................................................................34 Table 3. Distribution of ADA T2DM Risk Test results..................................................................35 Table 4. Association between screening tools and presences of prediabetes/diabetes .................36 Table 5. Diagnostic test measures for screening tools in predicting presence of prediabetes/diabetes ......................................................................................................................36 Table 6. Association between screening tool and presence of diabetes........................................37 Table 7. Diagnostic test measures for each screening tool in predicting presence of diabetes ...37 Table 8. Association of age, gender, and BMI with A1C ..............................................................38 TRUCK DRIVERS AND DIABETES 6 Abstract Purpose: To determine whether use of the American Diabetes Association’s Type 2 Diabetes Risk Test is more effective at identifying commercial motor vehicle drivers at risk of diabetes versus current practice of sole reliance on glycosuria. Background: In the United States, 34 million adults have diabetes of which 21% are undiagnosed. Commercial motor vehicle drivers are twice as likely as the national working population to have diabetes due to multiple occupational related factors. Medical complications from uncontrolled diabetes place both the commercial motor vehicle drivers and the public at heightened risk of collision or accident. Commercial motor vehicle drivers are required to complete periodic fitness for duty physical examinations at least every two years. Current practice relies on glycosuria obtained by urinalysis dip stick. However, glycosuria is not detected on urinalysis dip stick unless the blood sugar level is >180 mg/dL, which is >50 mg/dL above diabetes diagnostic standards, if fasting. Methods: The Iowa Model Revised framework was used to guide design of this quality improvement initiative. Participants underwent their routine fitness for duty physical but also completed the American Diabetes Type 2 Diabetes Risk Test and had a point of care A1C done. Results of the American Diabetes Type 2 Diabetes Risk Test were reviewed by the certified medical examiner and compared to the results of the urine glucose dip. Participants identified as being as risk of diabetes were provided an educational handout from the American Diabetes Association on prediabetes and advised to follow up with their primary care provider for additional evaluation. Quantitative methods were used to compare the screening methods then, point of care A1C was used to evaluate effectiveness of the screening tests. TRUCK DRIVERS AND DIABETES 7 Results: Chi-square tests and Fisher’s exact tests were used to evaluate association between screening tests and A1C level. Based on 117 participants, both screening tests had a poor diagnostic measure. Conclusions: Although the American Diabetes Association’s Type 2 Diabetes Risk Test was not an accurate diagnostic measure, it created a discussion pathway with participants about the importance of diabetes screening among commercial motor vehicle drivers. TRUCK DRIVERS AND DIABETES 8 Prediabetes Screening Among Commercial Motor Vehicle Drivers: Does the Use of the American Diabetes Association’s Type 2 Diabetes Risk Test More Effectually Identify Commercial Motor Vehicle Drivers at Risk of Diabetes Versus Sole Reliance on Glycosuria? INTRODUCTION Background Commercial Motor Vehicle Drivers There are approximately 3.5 million commercial motor vehicle (CMV) drivers in the United States (Day & Hait, 2019). CMV drivers are required to follow rules and regulations set forth by the Federal Motor Carrier Safety Administration (FMCSA), a subdivision of the U.S. Department of Transportation (DOT), to promote safe CMV operation and reduce accidents, injuries, and deaths related to CMV’s (FMCSA, 2013). Part of the FMCSA’s strategy to promote safety includes requiring those with commercial driver’s licenses (CDL) to undergo periodic physical examinations (a.k.a. DOT exams) to determine medical safety of the drivers and fitness for duty (FMCSA, n.d.). DOT exams are completed by a certified medical examiner (ME), which is a medical provider (physician, nurse practitioner, physician assistant, or chiropractor) who has undergone specialized training and is registered with the FMCSA’s National Registry of Certified Medical Examiners (FMCSA, n.d.). The FMCSA allows for MEs to utilize their medical knowledge to determine whether additional information or testing is needed to determine if the driver meets safety standards (FMCSA, n.d.) Part of the DOT exam includes a urinalysis to test for specific gravity, protein, glucose, and blood in the CMV driver’s urine (FMCSA, 2018). If a driver tests positive for glycosuria on the urinalysis, a random capillary blood glucose level may be checked. Drivers are not required to be fasting for these examinations. If a driver happens to be fasting and has a blood glucose TRUCK DRIVERS AND DIABETES 9 level ≥126 mg/dL or a random glucose ≥200 mg/dL, they are referred to their primary care provider (PCP) for follow up (American Diabetes Association [ADA], 2021). MEs could order laboratory blood work such as a hemoglobin A1C. However, due to the transient nature of CMV driver schedules, it becomes difficult to assure the driver follows through with the order, and it can be difficult to follow up with the CMV driver regarding the results. Also, many times DOT exams are conducted in occupational health clinics, which do not provide primary care (diagnosis or treatment of diseases). Therefore, a screening process for diabetes that can be completed while the CMV driver is in the MEs office is most ideal for this population. In 2018, the FMSCA updated their regulations to allow for drivers with insulin treated diabetes to obtain their DOT certificate of physical examination, given they abide by the new regulations set in place. Qualifications of Drivers and Longer Combination Vehicle Driver Instructors, Subpart E- Physical Qualifications and Examinations (2019), stipulated that CMV drivers with insulin treated diabetes must have form MCSA-5870 completed by their treating medical provider within forty-five days prior to their DOT physical examination and that the ME conducting the DOT physical examination is to utilize the completed form to aid in their determination whether the driver’s condition is stable to operate a CMV safely (FMCSA, n.d.). According to the DOT FMSCA’s 2020 Commercial Driver Safety Risk Factors report, drivers diagnosed with and being treated for diabetes were half as likely to be involved in a motor vehicle crash than those who did not have diabetes, possibly due to the frequency of medical monitoring required for the treatment (Hickman et al., 2020). From the most recent National Survey of Long-Haul Truck Driver Health and Injury, (2014), CMV drivers self-report a current diagnosis and treatment for diabetes two times more than the national working population. Presently, DOT physical examinations rely on CMV driver TRUCK DRIVERS AND DIABETES 10 history to identify diabetic symptoms. DOT exams require MEs to obtain certain objective data including height, weight, blood pressure, pulse, and urinalysis, which includes measuring specific gravity, and the presence of protein, blood, and glucose in the urine (FMCSA, 2018). Unless a driver tests positive for glycosuria or they show overt signs or symptoms of diabetes, drivers are not routinely screened for diabetes risk factors as part of the physical examination. Secondary to the nature of the CMV driving profession, drivers face multiple health disparities that can impact their health including long hours of sitting/driving, lack of healthy food options at truck stops, and elevated stress levels due to tight delivery schedules (Apostolopoulos et al., 2016). Because of these health disparities, CMV drivers’ risk of developing chronic diseases, such as cardiovascular disease, diabetes, hypertension, dyslipidemia, sleep apnea, and cancer, are higher than the average working population (Apostolopoulos et al., 2016; FMCSA, 2014). CMV drivers are more likely to be uninsured for healthcare coverage (15%) than the average working population (10%) (Day & Hait, 2019). The inconsistency and transient nature of truck drivers’ work schedules can make finding time to schedule and keep medical appointments difficult (Robbins et al., 2020). Unfortunately, the work environment of CMV drivers contributes to unhealthy lifestyle habits, which increases their risk of chronic diseases. Without proper screening methods in place, CMV drivers are at increased risk for health issues which may put their own safety, as well as the safety of others, at risk (FMCSA, 2014). Since CMV drivers must have periodic DOT exams to maintain their CDL, MEs are well positioned to screen CMV drivers for chronic diseases, provide education about the diseases, and educate drivers how to appropriately follow up with a primary care provider. TRUCK DRIVERS AND DIABETES 11 Diabetes Statistics The United States is amid a national diabetes epidemic, where currently over 10% of the country has diabetes (Centers for Disease Control and Prevention [CDC], 2020a). Diabetes occurs when the body either cannot produce enough insulin or cannot utilize insulin appropriately, leading to elevations in blood glucose (CDC, 2020b). There are three main types of diabetes: Type 1 (due to an autoimmune reaction which inhibits the body from producing insulin), gestational diabetes (diabetes that develops during pregnancy), and Type 2 diabetes (CDC, 2020b). Type 2 diabetes (T2DM) is the most common form of diabetes in the United States accounting for 90-95% of all cases, and it develops due to a gradual decline of β-cell insulin secretion (ADA, 2021; CDC, 2019b). Out of the 34 million adults who have diabetes, greater than 21% are undiagnosed (CDC, 2020a). Men are more likely to have T2DM than woman, and it is more common among American Indian/Alaskan Natives, non-Hispanic blacks, Hispanics, and Asian Americans, than non-Hispanic whites (CDC, 2020a). Prediabetes is classified as an A1C level between 5.7-6.4%, which increases one’s risk of T2DM and cardiovascular disease (ADA, 2021). Diagnostic criteria for T2DM includes an A1C ≥6.5%, fasting plasma glucose ≥126 mg/dL, or a 2-hour plasma glucose level ≥200 mg/dL (ADA, 2021). Individuals at higher risk of developing T2DM may have any of the following factors: a first degree relative with diabetes, obesity, hypertension, dyslipidemia, a sedentary lifestyle, a history of gestational diabetes and/or polycystic ovarian syndrome or belong to an ethnic/racial group identified as being high risk (ADA, 2021). Identification of adults within the prediabetic range and initiation of healthy lifestyle improvements can prolong or potentially prevent progression into T2DM, making risk identification and education vital (CDC, 2020b). With early identification of risk factors associated with T2DM, individuals can help reduce their TRUCK DRIVERS AND DIABETES 12 risk of the serious medical complications associated with T2DM including stroke, myocardial infarction, end-stage renal disease, peripheral neuropathy, retinal neuropathy, and death (ADA, 2018b; CDC, 2020b). The Diabetes Prevention Act of 2009 established the National Diabetes Prevention Program (NDPP), a lifestyle education program aimed at implementing CDC-recognized weightloss and healthy living plans to help reduce the risk of type 2 diabetes and associated cardiovascular disease (CDC, 2019a). There are CDC-recognized lifestyle programs throughout each state; participants embark on a year-long educational program focusing on how to choose healthy food options, incorporation of physical activity into busy daily schedules, stress management, and how to stay ‘on track’ throughout daily life (CDC, 2019a). The American Medical Association (AMA) (2020) has joined the NDPP prediabetes education efforts with their program “Prevent Diabetes STAT” by providing a “Diabetes prevention toolkit” for medical providers, which includes resources for the providers such as educational material for their offices and their patients. With a national effort towards prediabetes identification and education, MEs conducting DOT exams are well positioned to increase prediabetes screenings and lifestyle change education. Early Risk Factor Identification MEs should increase attention to identifying truck drivers with diabetic risk factors for multiple reasons. Sole reliance on symptomatic indicators and/or glycosuria could mean MEs are missing the opportunity to reach and educate a large population at risk of developing prediabetes and T2DM, who may not otherwise regularly see a medical provider. Glycosuria, despite a 99% specificity rate, the low sensitivity rate (14%) and high false negative rate (15%) of those tested who did have A1C levels >6.5%, demonstrates this may not be an ideal screening method for TRUCK DRIVERS AND DIABETES 13 T2DM (Storey et al., 2018). In non-diabetic patients, the renal threshold for glucose generally corresponds with a blood glucose level of 180 mg/dL and leads to urinary excretion of the excess glucose (positive glycosuria on urinalysis) (Hieshima et al., 2020). However, due to microvascular renal damage and diminished renal function, diabetics may not excrete excess urine even with a blood glucose concentration >200 mg/dL (Hieshima et al., 2020). Waiting for drivers to become symptomatic or test positive for glycosuria may be a missed opportunity to prevent significant microvascular damage. By the time a person is in the prediabetic range (A1c 5.7 - 6.4%), they already show early forms of microvascular damage, including nephropathy, small fiber neuropathy, and diabetic retinopathy (Tabák et al., 2012). Without early intervention, microvascular disease may increase risk of macrovascular disease (myocardial infarction, stroke, or cardiovascular death); however, it should be noted that risk of macrovascular disease is increased by many risk factors of diabetes, including obesity, and early identification of diabetes or prediabetes has not been shown to decrease risk of cardiovascular death (Tabák et al., 2012). Microvascular disease creates a potential safety risk for CMV drivers, as peripheral neuropathy alone has been shown to slow breaking response time by 0.70 seconds, making screening for diabetic risk factors not only a safety concern of CMV drivers and company owners, but a public safety issue as well (Spiess et al., 2017). Conclusion The intent of this quality improvement (QI) initiative was to determine whether use of the ADA Type 2 Diabetes Risk Test helped identify CMV drivers at risk for diabetes, with the hope of providing early intervention with education on diet and lifestyle changes prior to the development of Type 2 diabetes mellitus (T2DM). The NDPP highlights the need for all TRUCK DRIVERS AND DIABETES 14 healthcare providers to promote prediabetes screening and education, and MEs conducting DOT medical examinations are well positioned to provide such screenings. Early identification of T2DM risk factors can lead to earlier education and intervention, such as lifestyle changes, with the intention to intervene prior to manifestation of diabetic related microvascular and macrovascular changes, and potentially increasing the safety of the CMV driver. Local Problem According to the ADA, (2018b), Pennsylvania’s state average of adults with diabetes is slightly higher (12%) than the national average (10%). The costs associated with diabetes in Pennsylvania include more than $9 billion per year on direct medical related costs (office visits, hospitalizations, medication, diabetes supplies, etc.) in addition to the $3.5 billion on costs associated with diabetes related productivity loss (time away from work, diabetes related disability claims, etc.) (ADA, 2018a; ADA 2018b). The National Institute of Diabetes and Digestive and Kidney Disease, along with the Division of Diabetes Translation of the CDC have invested millions of dollars in research and education programs for diabetes prevention in Pennsylvania (ADA, 2018b). Current practice during DOT exams includes screening for hypertension and sleep apnea, but diabetes is not routinely screened for unless the driver is obviously symptomatic or tests positive for glycosuria on the routine urinalysis, triggering evaluation of a random blood glucose via glucometer (FMCSA, n.d.). The goal of this project was to determine whether a simple screening mechanism, in this case the ADA T2DM Risk Test, accurately identified CMV drivers at risk of diabetes versus current practice of sole reliance on glycosuria. To evaluate the effectiveness of the ADA T2DM Risk Test, a point-of-care hemoglobin A1C (POC A1C) was obtained during the appointment. CMV drivers identified with an A1C ≥ 5.7% were provided TRUCK DRIVERS AND DIABETES 15 education about pre-diabetes and diabetes and instructed to follow up with their primary care provider. The outcome measured in this project was whether use of the ADA T2DM Risk Test properly identified CMV drivers’ risk category, as well as comparing their identified diabetes risk level to the glycosuria result. Intended Improvement Project Purpose The purpose of this project was to determine whether ADA T2DM Risk Test was an accurate tool to identify CMV drivers at risk of diabetes versus current practice of sole reliance on glycosuria. Incorporating an accurate and simple diabetes screening tool could help identify drivers at risk of diabetes and the opportunity to provide education and interventions to help slow or reverse development of diabetes. Project Question Does the use of the American Diabetes Association’s Type 2 Diabetes Risk Test more effectually identify commercial motor vehicle drivers at risk of type 2 diabetes (a hemoglobin A1C greater or equal to 5.7%) versus sole reliance on glycosuria results? Project Objectives The ADA T2DM Risk Test was administered during routine DOT exams and results were quantified with POC A1C testing which evaluated the effectiveness of the screening tool. The results were then compared to the urine glucose dip. CMV drivers identified with a POC A1C ≥ 5.7% were provided brief education about pre-diabetes and instructed to follow up with a primary care provider. TRUCK DRIVERS AND DIABETES 16 Framework The multifaceted influences and barriers that effect CMV driver health need to be addressed when developing health improvement initiatives for this population. The Integrative and Dynamic Healthy Commercial Driving (IDHCD) paradigm aids in development of health promotion quality initiatives by targeting the most essential barriers interfering with CMV driver health and using ecological models of health behavior to guide health promotion initiatives (Lemke et al., 2016). The IDHCD paradigm focuses on using holistic approaches and maximizing resources to advance healthy driver initiatives by focusing on seven specific facets that influence driver health, which include: “access to health resources, barriers to health behaviors, recommended alternative settings, constituents of health behavior, motivation for health behaviors, attitude toward health behaviors, and trucking culture” (Lemke et al., 2016). By focusing on the unique issues that influence CMV driver health, quality improvement initiatives directed towards this population are more likely to succeed. This project originated due to the recognized need for a simple way to screen CMV drivers for prediabetes and to provide education and resources to those identified as being at risk before the physiological effects of diabetes lead to safety concerns of CMV operation. Using the IDHCD paradigm combined with the experience of interacting with CMV drivers in an occupational health clinic, helped guide the development of the prediabetes screening intervention with POC A1C testing for evaluation of screening accuracy so appropriate interventions could be initiated while the driver was in the clinic. Since the IDHCD paradigm is explicitly for CMV driver health, it was more likely to contribute to success of the diabetes screening intervention, versus trying to adapt a concept to the unique complexities of CMV driver health. TRUCK DRIVERS AND DIABETES 17 Literature Synthesis Evidence Search The literature review process began with an investigation into current literature on CMV drivers and T2DM, prediabetes screening, and diabetes prevention. The term “truck driver” was used in place of CMV driver for database searches since it produced greater results. Searching multiple databases (CINAHL Complete, MEDLINE, and PubMed) for related articles within the past five years produced few to no articles directly related to CMV drivers and T2DM, prediabetes screening, or diabetes prevention. Therefore, the research strategy shifted to multiple topic searches including general CMV driver health, prediabetes screening, diabetes prevention, and POC A1C testing. The following is a review of the separate research topics, and how the information may or may not pertain to the proposed QI initiative. The literature review grid (Appendix G) presents the database search information, criteria, and results. All searches used the Boolean/Phrase search mode and included the limitation of scholarly peer reviewed articles within the past five years. Expanders included applying equivalent subject. The searches that yielded the greatest number of articles were the least specific and provided articles that were too broad regarding subject matter. Search terms “diabetes AND truck driver” provided the best results with high quality articles regarding CMV driver health but were not specific to diabetes. In contrast, using the search terms “truck driver AND health” provided seventy-eight articles, the majority of which were international studies and many solely focused on OSA or specific medical conditions not including diabetes. When searching for literature regarding best methods of screening for prediabetes several search term combinations were used. The search that yielded the most results used the search TRUCK DRIVERS AND DIABETES 18 term “undiagnosed diabetes,” however, the results were too broad and many focused on gestational diabetes. After trying several search phrases, including “undiagnosed diabetes AND truck,” “diabetes screening NOT gestational,” “diabetes screening workplace,” the search with the best results was “prediabetes AND prevention AND (screening or assessment or test or diagnosis) NOT (gestational diabetes or GDM or gestational diabetes mellitus, or diabetes in pregnancy).” This search provided thirteen articles that reviewed evidence-based tools for diabetes screening. The CINAHL Complete search for articles regarding A1C POC testing using the phrasing “A1C point of care testing” produced three articles. However, when the phrasing was changed to “(A1C or glycemic control or HbA1C) AND point of care testing,” thirteen articles, most with high relevancy, were produced. Similar searches in PubMed resulted in sixty-seven and one hundred twenty-six articles, respectively. The large difference in results between databases appears to be based on the ability to set more specific search parameters in CINAHL Complete, such as peer reviewed articles, subject age to only include adults, and English language. Articles within these searches could be further divided into articles that were aimed at reviewing accuracy of POC testing, and articles aimed at using POC testing in diabetes screening. Collectively, these articles provided high quality research in evidence-based application of POC A1C use in diabetes screening in the general population. Overall, thirty-two articles were reviewed for this project, twelve of which were focused on CMV driver health, thirteen on POC A1C testing, and six on T2DM screening. TRUCK DRIVERS AND DIABETES 19 Comprehensive Appraisal of Evidence General Driver Health Although search results yielded no results within the past five years that focused solely on CMV drivers and diabetes, there were several that examined various health issues of CMV drivers. A group of authors collaborated on multiple studies based on a nonexperimental, descriptive, cross-sectional survey of CMV drivers at a major truck stop on the east coast of the United States (Apostolopoulos et al., 2016; Hege et al., 2017; Hege et al., 2018; Lemke et al., 2016). Upon reviewing, they were found to have reliable and valid measured outcomes, with pertinent information regarding lifestyle and health habits of CMV drivers (Apostolopoulos et al., 2016; Hege et al., 2017; Hege et al., 2018; Lemke et al., 2016). The survey of CMV drivers supports the inherent nature of the trucking industry which contributes to higher rates of chronic diseases, such as cardiometabolic issues (including diabetes) and sleep apnea (Apostolopoulos et al., 2016; Hege et al., 2017; Hege et al., 2018; Lemke et al., 2016). Based on the participants in this survey, nearly 90% of drivers were overweight, obese, or extremely obese, with a mean age of forty-six years, and 20% had a fasting glucose level ≥110 mg/dL (Hege et al., 2017; Lemke et al., 2016). One large scale, retrospective study examined nearly 90,000 CMV examinations from 2005-2012 to determine medical conditions which lead to medical certificate limitations or disqualifications (Thiese et al., 2021). While this study is thorough and found that diabetes was one of the top three medical issues causing limitations or disqualification for CMV medical certificate, the data was based on old FMCSA standards which have significantly changed since 2018 (Thiese et al., 2021). As of November 2018, diabetic drivers requiring insulin are permitted TRUCK DRIVERS AND DIABETES 20 to qualify for medical certification given a medical clearance form is completed by their treating physician and the driver abides by the regulations for insulin use (FMSCA, 2018). The new diabetes standard allows more diabetic CMV drivers to maintain their medical certificate but places a one-year limit on the length of the certification (FMCSA, 2018). Additional studies, based in the United States and abroad, support the findings linking unhealthy lifestyles of CMV drivers to increased rates of chronic diseases and the need for manageable interventions, indicating this is an international concern among this population (Bachmann et al., 2018; Okorie et al., 2019; Olson et al., 2016; Ravi et al., 2020; Riva et al, 2018). These findings support the need for diabetes and prediabetes screening in the CMV driver population. By focusing on effective, evidence-based screening tools currently used in other health care settings, MEs could provide prediabetes screening to a population at high risk for T2DM. Diabetes Prevention and Screening There are various recommendations in the United States pertaining to screening adults for diabetes. In 2021, the U.S. Preventive Services Task Force (USPSTF) updated recommendations for screening asymptomatic adults by lowering the starting age for screening to thirty-five years (previously forty), due to growing evidence showing increasing rates of adults under the age of forty being diagnosed with T2DM (Greiner et al., 2020). The USPSTF recommends testing adults thirty-five to seventy who are overweight or obese for prediabetes and T2DM by oral glucose tolerance test, fasting plasma glucose level, or A1C. The American Association of Clinical Endocrinology (AACE) (n.d.) and the American Diabetes Association (ADA) (2021) have similar screening recommendations and recommend testing by oral glucose tolerance TRUCK DRIVERS AND DIABETES 21 testing, fasting plasma glucose level, or A1C. The AACE and ADA screening recommendations include adults with a body mass index (BMI) >25kg/m² (or >23kg/ m² in Asian Americans) who have one or more risk factors and all adults forty-five years and older without risk factors (AACE, n.d.; ADA, 2021). Risk factors include: first degree relative with diabetes, high-risk ethnicity/race (Asian American, African American, Native American, Latino, and Pacific Islander), personal history of cardiovascular disease, hypertension, hyperlipidemia, women with polycystic ovarian syndrome, women who were diagnosed with gestational diabetes mellitus (these women should be tested at least every three years), sedentary lifestyle, and acanthosis nigricans (AACE, n.d.; ADA, 2021). The AACE also includes antipsychotic therapy for schizophrenia and/or severe bipolar disease, chronic glucocorticoid exposure, and sleep disorders including obstructive sleep apnea (OSA) (AACE, n.d.). Much of the research and information about diabetes screening and/or prevention focused on reviewing current evidence-based practice strategies, surveying the public about perceived risks of diabetes and diabetes prevention, and methodology for diabetic screenings. Results of the research showed a need for improved implementation of evidence-based diabetes screening and education (Bowen et al., 2018; Giblin et al., 2016; Kirkman et al., 2019). Offering risk assessments via questionnaires, POC testing when laboratory or glucose tolerance testing options are not available, community education, and facilitating referrals for follow up care were essential components of increasing identification of diabetics and prediabetics (Bowen et al., 2018; Giblin et al., 2016). All facets of health care providers within the community have a joint obligation to promote diabetic screenings and education as part of the National Diabetes Prevention Program (CDC, 2019a). TRUCK DRIVERS AND DIABETES 22 One study compared the application of three risk-factor based screening tools and used POC A1C technology to verify results. The study, conducted by Gamston et al., (2020) compared the CDC’s Prediabetes Screening Test, the ADA’s T2DM Risk Test, and the ADA guidelines from 2016, using POC A1C to validate screening results of 740 participants who were enrolled in an employer-based wellness program. The study discovered that the ADA T2DM Risk Test demonstrated the highest levels of sensitivity and specificity for identifying prediabetes confirmed by POC A1C compared to the other two screening tools (Gamston et al., 2020). However, this is the only study of its kind which examines clinical recommendations and risk-factor tools to determine which method is most beneficial for identifying prediabetes. A1C Point of Care Testing Hemoglobin A1C testing has been identified by the ADA as one potential method for diagnosing diabetes (2021). A1C testing has been shown to be equally appropriate for use of diagnosis as fasting plasma glucose and two-hour oral glucose tolerance testing (ADA, 2021). However, individuals with “hemoglobinopathies including sickle cell disease, pregnancy (second and third trimesters and postpartum period), glucose-6-phosphate dehydrogenase deficiency, HIV, hemodialysis, recent blood loss or transfusion, or erythropoietin therapy” may cause an inaccurate A1C reading and, therefore, should use alternative methods when testing for T2DM (ADA, 2021). POC A1C testing has been proven as an accurate way to monitor currently diagnosed diabetics (ADA, 2021). In 2021, the ADA did revise testing method recommendations, permitting use of POC A1C for diagnosing diabetes if POC A1C assays are certified by the National Glycohemoglobin Standardization Program (NGSP) and cleared by the U.D. Food and Drug Administration (FDA) (ADA, 2021). TRUCK DRIVERS AND DIABETES 23 Two studies, one systematic review and one meta-analysis, produce supportive evidence that POC A1C testing is reliable within an acceptable margin of error (<6%) as compared to laboratory analyzed A1C levels and support use of POC technology for diagnosis of diabetes and compare multiple A1C POC devices (Arnold et al., 2020a; Arnold et al., 2020b; Hirst et al., 2016; Sobolesky et al., 2018). Of the studies supporting POC A1C technology for diagnosis, device variations and user technique were found to influence reliability of results (Arnold et al., 2020a; Arnold et al., 2020b; Hirst et al., 2016; Sobolesky et al., 2018). The study from Arnold et al., (2020a & 2020b) included a small number of subjects (sixty-one) and analyzed one POC A1C device (Afinion AS100 Analyzer) against laboratory data. Whereas the study conducted by Sobolesky et al., (2018) had a larger sample of subjects (618), analyzed the same POC A1C device (Afinion AS100 Analyzer) and produced similar results to the study by Arnold et al., (2020a & 2020b). The systematic review and meta-analysis conducted by Hirst et al., 2016, reviewed a large amount of data which included analysis of scholarly articles reviewing twelve POC A1C devices in total. It should be noted that for consistency, all studies comparing POC to laboratory data used the same blood samples and were conducted by trained laboratory personnel (Arnold et al., 2020a; Arnold et al., 2020b; Hirst et al., 2016; Sobolesky et al., 2018). POC testing was generally not conducted by clinicians (Arnold et al., 2020a; Arnold et al., 2020b; Hirst et al., 2016; Sobolesky et al., 2018). This leads to question whether a clinician obtaining a capillary blood sample would produce similar results as the laboratory personnel using a venous blood sample. Multiple studies have found POC A1C testing to be comparable to laboratory testing regarding accuracy of results for monitoring (Fellows & Cipriano, 2019; John et al., 2019; Jones 2016; Lynn et al., 2018; Mardis 2018; Nathan et al., 2019; Toro-Crespo et al., 2017; Whitley et TRUCK DRIVERS AND DIABETES 24 al., 2017). All studies took place in a variety of settings (clinics, pharmacies, dental offices, etc.) but had large subject sample sizes over three months to one-year periods. Whitley et al., (2017) recognize that there were not specifications in place to select subjects and retrospectively excluded participant data in the final study due to incomplete data. Many of the studies did compare clinician conducted POC A1C with capillary blood samples to venous blood samples analyzed in the laboratory (drawn the same day from the same subjects), which may indicate why these studies had similar results and concluded recommendation for POC A1C use for A1C monitoring but not diabetes diagnosis (Fellows & Cipriano, 2019; John et al., 2019; Jones 2016; Lynn et al., 2018; Mardis 2018; Nathan et al., 2019; Toro-Crespo et al., 2017). Therefore, utilizing POC A1C testing to evaluate efficacy of diabetes risk factor screening recommendations may be beneficial for the transient CMV population; however, these results should not be interpreted as an implied clinical diagnosis of prediabetes or T2DM. Conclusion While there are research gaps in recent years directly related to the risk of diabetes among CMV drivers, there is current research that identifies lifestyle disparities among CMV drivers that increases risk of diabetes. There are ongoing initiatives, such as the National Diabetes Prevention Program, that focus on diabetes screening and education methods in community clinics. The 2021 ADA diabetes diagnostic criteria recognize NGSP certified, and FDA cleared POC A1C devices for diagnostic purposes. A cumulative review of the research identifies the opportunity for MEs to screen and educate CMV drivers during their DOT exam for prediabetes and T2DM risk factors using a validated screening tool such as the ADA T2DM Risk Test, using POC A1C testing to evaluate the effectiveness of the screening tool. TRUCK DRIVERS AND DIABETES 25 METHODS This DNP project was a quality improvement (QI) initiative focused on implementing an evidence-based screening method for identifying CMV drivers at risk of diabetes within a rural/suburban occupational health clinic serving a population at high risk for diabetes, potentially, without access to routine health screenings. Current practice relies solely on glycosuria for diabetic screening. This QI initiative was intended to improve upon current practice by implementing use of the ADA T2DM Risk Test, an evidence-based tool, to help identify CMV drivers at risk for diabetes. Project Design This quality improvement initiative used a quantitative approach to determine whether implementing the ADA T2DM Risk Test was an effective method in screening CMV drivers for being at risk of T2DM, using POC A1C technology to validate results. For this project the ptsDiagnostics A1CNow+ Professional POC A1C device was used (which is certified by the NGSP and cleared by the FDA). Results of the ADA T2DM Risk Test and POC A1C were then compared to the current screening practice of collecting a urine glucose test. To produce results that were bias free, participant anonymity was maintained, and only quantifiable data was obtained. Because DOT physicals follow FMCSA regulations and guidelines, results of the ADA T2DM Risk Test did not impact the participants DOT physical outcomes. Additional quantifiable data collected during DOT physicals, (gender, BMI, and age) were used to analyze whether these traits influenced screening method results. Model for Implementation The Iowa Model is an evidence-based (EB) practice framework widely used in implementing evidence-based changes in nursing practice (Buckwalter et al., 2017). In 2017, TRUCK DRIVERS AND DIABETES 26 revisions to the Iowa Model focused on expanding the sections on piloting and establishing change, based on feedback from over 600 users of this framework (Buckwalter et al., 2017). There are seven steps in the Iowa Model: identify triggering issues/opportunities, state the question or purpose, form a team, assemble, appraise, and synthesize body of evidence, design and pilot the practice change, integrate and sustain the practice change, and disseminate results (Zaccagnini & Pechacek, 2021). Users of the model need to be educated in EB research appraisal and change implementation for the model to be an effective tool (Buckwalter et al., 2017). The Iowa Model Revised (Appendix E) was used as the framework for this QI initiative because of the validity of the model and the appropriateness of the algorithm in the occupational health clinic setting. The first step in the process was identifying an issue in daily clinical practice that could be improved with an EB practice change, in this case it was the lack of effective diabetes screening among CMV drivers. The second step was to state the purpose: use an EB screening tool to improve identification of CMV drivers at risk for T2DM. At this point in the algorithm, there was the first of three decision points. The first decision point questioned whether the issue was a priority. Due to the potential significant health consequences of T2DM which could result in serious accidents on our nation’s roadways, screening CMV drivers for T2DM was indeed a priority. The third step in the process was to form a team; due to the small size of the clinic, the team consisted of the nurse practitioner and the supervisor/mentor. Reviewing available evidence was the next step in the Iowa Model Revised algorithm. Limited information or research studies were found that specifically addressed screening for T2DM in CMV drivers; however, there were several high-quality studies focused on overall health disparities among CMV drivers TRUCK DRIVERS AND DIABETES 27 which mentioned T2DM. When reviewing EB screening tools used in settings like occupational health clinics, the ADA’s T2DM Risk Test was a valid tool. Once the evidence was synthesized, the second decision point in the algorithm was reached. The second decision was whether sufficient evidence existed. Despite the lack of evidence available on screening CMV drivers for T2DM, sufficient evidence existed supporting use of the ADA T2DM Risk Test for identifying people at risk of diabetes. Therefore, the next step was to design and pilot the practice change. The clinical team decided to pilot this QI initiative in one office, then review and analyze the data before deciding if it should be implemented across all five office locations. Once the QI initiative was approved by Bloomsburg University’s IRB, approval was obtained by the clinic’s administrative team (Appendix A). CMV drivers who presented for a DOT exam, who were not diabetic and/or being treated for diabetes, were given the opportunity to participate in the QI initiative. Those who chose to participate signed an informed consent which was obtained by the ME. The participants then completed the ADA T2DM Risk Test. Results of the risk test were evaluated using a POC A1C via finger stick. Participants identified as at risk of diabetes (ADA T2DM Risk Test score ≥5) and/or have an A1C result ≥5.7% were provided educational material on prediabetes and advised to follow up with their PCP (information for a local free clinic was provided to those drivers without health insurance). Once all data was collected, it was analyzed to determine whether the ADA T2DM Risk Test was an effective method for screening CMV drivers for being at risk of diabetes. Following the pilot period, the third decision point was reached: Was the change appropriate for adoption in practice? If it was not, other alternatives may be considered. If the pilot proved appropriate for clinical integration, the team would work to incorporate the ADA T2DM Risk Test into other TRUCK DRIVERS AND DIABETES 28 clinics within the corporation. Finally, all results of this QI initiative were disseminated to help educate others on the process and results to aid in future EB initiatives Setting and Stakeholders This QI initiative took place in an occupational health clinic in a town in central Pennsylvania of approximately 5,300 residents and surrounded by multiple rural communities. The occupational health clinic conducted approximately 110 DOT exams per month. Staffing in this office consisted of three medical assistants and one ME (in this case a certified registered nurse practitioner). CMV drivers evaluated in this clinic were a combination of independent drivers versus company-employed drivers, and a combination of CDL and non-CDL holders. Most of the drivers evaluated in this clinic were local to the town and the surrounding areas. However, due to the proximity of the clinic to two major interstates, there were often drivers from outside the region evaluated in this clinic. Stakeholders involved in this project included CMV drivers who visited the office for DOT exams, medical assistants in the office, local trucking companies and employers, primary care providers, and the public. Stakeholder involvement throughout the change process was inherent to the potential success of the proposed change, as each stakeholder contributed unique viewpoints which helped align project goals with outcomes (Albert et al., 2022; Hansen et al., 2019). The benefits and values varied between stakeholders, but the goal was to underscore the importance of early detection of prediabetes and T2DM in CMV drivers, thereby diminishing potentially costly outcomes due to medical events. Planning the Intervention The first step in planning this QI initiative was to seek permission from the ADA to use the T2DM Risk Test and Prediabetes educational handout, which was obtained (see Appendix C TRUCK DRIVERS AND DIABETES 29 and D). Prior to initiating this project, it underwent institutional review board (IRB) approval through Bloomsburg University to ensure safety and compliance of ethical standards (Appendix A) (Hickey & Brosnan, 2017). Since there was not IRB within the corporation that provides clinic oversight, the QI initiative and approval for use of protected health information was obtained by corporate administration (Appendix A). Local oversight of the project involved clinical expert, Dr. Kimberly Olszewski, DNP, CRNP and the clinic’s administrative team. Once approved, POC A1C device and test cartridges were purchased for the sole purpose of evaluating effectiveness of ADA T2DM Risk Test results. Once approved by all parties, this project was implemented at one occupational health clinical site conducted by one ME, using a voluntary, convenience population of CMV drivers during routine DOT exams. At the beginning of the examination the ME spoke to the CMV driver regarding the QI initiative and explain the voluntary participation. The ME obtained written consent from all CMV drivers who choose to participate (Appendix B). Participants were then asked to complete the ADA T2DM Risk Test in addition to the routine medical history form (Appendix C). During the exam, the ME reviewed the ADA T2DM Risk Test completed by the participant and obtained a POC A1C to determine accuracy of the recommendation outcome. If a driver’s A1C was ≥ 5.7%, they were provided education regarding pre-diabetes and instructed to follow up with their primary care provider (Appendix D). Participants and Recruitment Inclusion criteria for recruitment was anyone who presented to the clinic for a DOT exam who was eighteen years and older. Participation was voluntary. If a driver decided not to participate, that decision did not impact the result of their physical or length of medical card determination. Exclusion criteria was patients presenting for a DOT exam who were already TRUCK DRIVERS AND DIABETES 30 diagnosed with and/or treated for diabetes. During the data collection period, only Englishspeaking patients presented to the clinic for DOT exams, therefore, language was not an inclusion or exclusion factor. Consent and Ethical Considerations The obligation of healthcare providers is to maintain ethical standards, whether it is in providing patient care or with human subject research (Sylvia & Terhaar, 2018). In reviewing this project there did not appear to be any ethical concerns. The data collected included age (not date of birth), gender, body mass index, glycosuria results, ADA T2DM Risk Test score, and POC A1C result. Documents collected for the project did not contain any individually identifiable data, as the only document collected from the patients was the results of their ADA T2DM Risk Test and POC A1C results, which did not ask for or contain such data. The collected data was logged in an Excel spread sheet on a computer in the clinic designed to protect sensitive patient information and was password protected per individual log-in. Once information was entered into the spread sheet, the ADA T2DM Risk Test results were kept for one year following the QI initiative in a locked, fireproof box in a locked room in the clinic. After one year, all hard copies were discarded in the confidential document recycling container in the clinic. Data Collection Upon arrival to the clinic for a DOT exam, CMV drivers completed routine paperwork, including the FMCSA’s Medical Examination Report Form, MCSA-5875 to provide their selfreported medical, surgical, and medication history, which was then reviewed by the ME during their exam (FMCSA, 2018). Form MCSA-5875 also contained objective data collected during the physical, including height, weight, blood pressure, pulse, vision and hearing acuity, urinalysis results, and physical examination findings. Objective data from this form collected as part of the TRUCK DRIVERS AND DIABETES 31 QI initiative, included height and weight (to calculate BMI), age, gender, and urine glucose dip results. The ME discussed the QI initiative with each driver and obtained written, voluntary consent for participation during the DOT exam, which was then placed in a locked box. Participants completed the ADA T2DM Risk Test, and the ME reviewed the results. To validate the results of the risk test, a POC A1C was obtained via finger stick. A1C readings 5.7-6.4% correlated with prediabetes and readings ≥6.5% correlated with diabetes per the 2021 ADA criteria for the screening and diagnosis of prediabetes and diabetes. The ME wrote the results of the POC A1C on the top of the ADA T2DM Risk Test and participants were given a copy of their results. The participants did not write their name on the ADA T2DM Risk Test, instead, the copy the ME retained had an identifying number (1, 2, 3, etc.) written at the top for record keeping purposes. Participants who were identified by the results of their ADA T2DM Risk Test (score ≥ 5) and/or POC A1C as being at risk for diabetes (≥5.7%) were provided brief, basic education in the form of the ADA’s prediabetes handout (Appendix D) and were encouraged to follow up with their PCP to discuss their results. Those participants who did not have medical insurance were provided a pamphlet with information to a local free clinic. Data Analysis Data from this QI initiative was analyzed using a quantitative method. This QI initiative goal was to determine whether implementing an evidence-based diabetes screening tool, the ADA’s T2DM Risk Test, was more effective in screening CMV drivers for being at risk of type 2 diabetes versus sole reliance on glycosuria. A convenience sample was used since all participants were CMV drivers presenting to the occupational health clinic for routine DOT exam. The data collected included both continuous data (BMI, age, urine glucose dip results, TRUCK DRIVERS AND DIABETES 32 POC A1C, and ADA T2DM Risk Test results) and categorical data (gender) (Giuliano & Polanowicz, 2008). Data analysis compared each participants’ results in two ways. First, the results of the ADA T2DM Risk Test (≥5= at risk for T2DM) to the POC A1C results (≥5.7% at risk for T2DM), to determine if the ADA T2DM Risk Test was an effective method to screen for CMV drivers at risk of T2DM. Then, the participants’ urine glucose dip results were compared to the POC A1C to determine the accuracy of this screening method. The height and weight collected on each participant was used to calculate body mass index (BMI). Age, gender, and BMI were compared to the ADA T2DM Risk Test Results and POC A1C to determine whether there were any correlations to identified risk level validity or lack of validity. Conclusion The Iowa Model Revised framework guided design of this QI initiative. A plan was developed to implement an EB screening method for identifying CMV drivers at risk of diabetes. By using the ADA T2DM Risk Test during routine DOT exams (evaluated by POC A1C), at risk drivers were educated on prediabetes and lifestyle changes they could make to help prevent or slow disease development/progression. Upon completion of the QI initiative, an evaluation was conducted to determine potential expansion of the QI initiative within the corporation’s other clinics. RESULTS Statistical Methods The characteristics of the study cohort were described using means for continuous data (with standard deviation and range) and percentages for categorical data. Chi-square tests and Fisher’s exact tests (when expected counts were <5) were used to evaluate the association between the screening tests (ADA T2DM risk and glucose urine test) and A1c level. In addition, TRUCK DRIVERS AND DIABETES 33 diagnostic test measures including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated to evaluate the utility of these screening tests for identifying diabetes risk. The AUC of the screening tools were compared using chi-square tests from logistic regression. For these analyses, the A1c result was classified in two different ways including pre-diabetes/diabetes (A1c ≥5.7%) and diabetes only (A1c ≥6.5%). The influence of age, gender, and BMI on screening for diabetes was evaluated using logistic regression and evaluating changes in the AUC. Statistical Analysis System (SAS) version 9.4 was used for statistical analysis. All tests were two-sided and p-values <0.05 were considered significant. Descriptive Summary of Study Population There was a total of seventeen people excluded for pre-existing known diabetes, and another twenty-four declined participation. The remaining participants were included in the study (n=117). The table below includes a descriptive summary of age, gender, and BMI for the study cohort. The study cohort compromised 97% male, had a mean age of 45.2 years (SD=13.4), and a mean BMI of 31.2 kg/m². TRUCK DRIVERS AND DIABETES Age (years) Mean (SD) [Range] Age category <30, % (n) 30-39, % (n) 40-49, % (n) 50-59, % (n) 60-69, % (n) 70+, % (n) Gender Male, % (n) Female, % (n) 2 BMI (kg/m ) Mean (SD) [Range] BMI category Normal (18.5-24.9), % (n) Overweight (25-29.9), % (n) Obese I (30-34.9), % (n) Obese II (35-39.9), % (n) Obese III (40+), % (n) Table 1: Cohort age, gender, BMI 34 Overall study cohort N=117 45.2 (13.4) [19, 71] 11% (n=13) 29% (n=34) 19% (n=22) 24% (n=28) 12% (n=14 6% (n=6) 97% (n=114) 3% (n=3) 31.2 (6.5) [20.2, 52.7] 15% (n=17) 33% (n=39) 32% (n=38) 13% (n=15) 7% (n=8) Each of the study participants had a POC A1C measured at time of study. These were categorized into normal (A1C < 5.7%), prediabetes (A1C 5.7-6.4%), and diabetes (A1C 6.5%+), as described in the following table. The study cohort included 27% with pre-diabetes and 4% with diabetes. The remaining 69% had an A1C within the normal range. Of the 117 participants, there were only two (2%) with a positive glycosuria result. A1c category Normal (<5.7%) Pre-diabetes (5.7%-6.4%) Diabetes (6.5%+) Table 2: Cohort A1C results Overall study cohort N=117 69% (n=81) 27% (n=31) 4% (n=5) Each of the study participants had the ADA T2DM risk test measured at time of study. These were categorized into minimal risk (test result <5) versus at risk for T2DM (test result ≥5). The table below includes the distribution of the ADA T2DM risk test result. The study cohort included 49% that screened positive for diabetes risk using the ADA T2DM risk test. TRUCK DRIVERS AND DIABETES ADA T2DM risk test Minimal risk, % (n) 35 Overall study cohort N=117 51% (n=60) 1 n=5 2 n=15 3 n=17 4 n=23 At risk for T2DM, % (n) 49% (n=57) 5 n=23 6 n=15 7* n=14 8 n=4 9 n=1 Table 3: Distribution of ADA T2DM Risk Test results. *There was one with a result of 6.5 that was rounded up to 7. Association of ADA T2DM Risk Test and Glycosuria with A1C The A1C result was classified in two ways for the following analysis, including prediabetes/diabetes (A1C ≥ 5.7%) and diabetes only (A1C ≥ 6.5%). For each of these outcomes, the results of the ADA T2DM risk and the glucose urine test were evaluated for association using Chi-square tests or Fisher’s exact tests and by calculating diagnostic test measures including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Finally, the utility of the screening tests was compared using the area under the curve (AUC). There were 36 of 117 (31%) that had pre-diabetes or diabetes (a1c ≥5.7%). The table below evaluates the association between the screening tools and presence of prediabetes/diabetes (A1c ≥5.7%). For those with the ADA T2DM risk test <5 and ≥5, there were 28% and 33% with A1C ≥5.7% (p=0.558). For those with negative and positive glucose urine test, there were 30% and 100% with A1C ≥5.7% (p=0.093). TRUCK DRIVERS AND DIABETES 36 N ADA T2DM risk test <5 (minimal risk) 60 % with A1c ≥5.7% 28% p-value 0.5581 ≥5 (at risk for 57 33% T2DM) Glycosuria Negative 115 30% 0.0932 Positive 2 100% Table 4: Association between screening tools and presence prediabetes/diabetes. 1 Chi-square test, 2Fisher’s exact test The table below includes the diagnostic test measures for each screening tool in predicting presence of prediabetes/diabetes (A1C ≥5.7%). When comparing the AUC results between the screening tests, the differences were not significant (p=0.886). NPV 72% AUC 0.53 [21%, [59%, 47%] 83%] Glycosuria 100% 100% 70% [96%, [16%, [61%, 100%] 100%] 79%] Table 5: Diagnostic test measures for screening tools in predicting presence of prediabetes/diabetes [0.43, 0.63] 0.53 [0.48, 0.57] ADA T2DM test Sensitivity 53% Specificity 53% [35%, 70%] Measurement 6% 95% CI [1%, 19%] [42%, 64%] Measurement 95% CI PPV 33% Overall, results revealed that neither test had significant association with presence of prediabetes/diabetes using A1C ≥5.7%. In general, the diagnostic test measures were poor as measured by AUC of 0.53 for both screening tests (p-value for difference in AUC between screening tests = 0.886). There were five of 117 (5%) that had diabetes (A1C ≥6.5%). The table below evaluates the association between the screening tools and presence of diabetes (A1C ≥ 6.5%). Those with ADA T2DM Risk Test score ≥5 had a higher percent with A1C ≥ 6.5% as compared to those with ADA T2DM Risk Test score <5 (9% versus 0%, p=0.025). Those with a positive urine TRUCK DRIVERS AND DIABETES 37 glucose test had a higher percent with A1C ≥6.5% as compared to those with a negative urine glucose test (100% versus 3%, p=0.0015). N ADA T2DM risk test <5 (minimal risk) 60 % with A1c ≥6.5% 0% ≥5 (at risk for T2DM) 57 9% Glycosuria Negative 115 3% Positive 2 100% Table 6: Association between screening tool and presence of diabetes 1 Chi-square test, 2Fisher’s exact test p-value 0.0252 0.00152 The table below includes the diagnostic test measures for each screening tool in predicting presence of diabetes (A1C ≥6.5%). When comparing the AUC results between the screening tests, the differences were not significant (p=0.575). Both tests had significant association with presence of diabetes using A1C ≥6.5% (p=0.025 for the ADA T2DM Risk Test, and p=0.0015 for glycosuria). In general, the overall diagnostic test measures were relatively low as measured by AUC of 0.77 for ADA T2DM Risk Test and 0.70 for glycosuria (p-value for difference in AUV between screening tests = 0.575). ADA T2DM test Measurement 95% CI Sensitivity 100% Specificity 54% PPV 9% NPV 100% [48%, 100%] 40% [5%, 85%] [44%, [1%, 16%] [94%, 63%] 100%] Glycosuria Measurement 100% 100% 97% 95% CI [97%, [16%, [93%, 100%] 100%] 99%] Table 7: Diagnostic test measures for each screening tool in predicting presence of diabetes AUC 0.77 [0.72, 0.81] 0.70 [0.46, 0.94] Influence of Age, Gender, and BMI on Screening for Diabetes Risk To determine the influence of age, gender, and BMI on the screening tests, they were first evaluated for association with A1c level. Next, to determine if they can be used to increase the TRUCK DRIVERS AND DIABETES 38 area under the curve (AUC) for each screening test, they were added to logistic regression model for each screening test. The following table examines association of age, gender, and BMI with A1C. Age and BMI were relatively useful predictors of A1c ≥5.7%. In fact, if age and BMI were used as a “screening test” for A1c ≥5.7%, the resulting AUC was 0.70 (95% CI=[0.61, 0.80]). [As a side note, this AUC is higher than the AUCs for ADA T2DM risk and glycosuria]. To determine the screening tests do better after adjusting for age and BMI, ADA risk was added to the analysis. This resulted in the AUC increasing from 0.70 to 0.77 (95% CI=[0.67, 0.86]). However, this was not a significant improvement in the AUC (p=0.147). If glycosuria was added to the analysis, the AUC increases to 0.71 (95% CI=[0.60, 0.81]), but this was not a significant improvement in the AUC (p=0.388). N Age category (years) <30 13 % with A1c ≥5.7% 8% p-value 0.0361 % with A1c ≥6.5% 0% p-value 0.0981 30-39 34 24% 3% 40-49 22 36% 0% 50-59 28 43% 7% 60-69 14 29% 7% 70+ 6 50% 17% 2 Gender Male 114 32% 0.552 4% 0.9992 Female 3 0% 0% 1 BMI category Normal (18.517 12% 0.0074 0% 0.00971 (kg/m2) 24.9) Overweight (2539 28% 0% 29.9) Obese I (30-34.9) 38 26% 3% Obese II (35-39.9) 15 60% 20% Obese III (40+) 8 50% 13% 1 Table 8: Association of Age, Gender, and BMI with A1C. Cochran-Armitage exact trend test, 2 Fisher’s exact test. Like above, age and BMI were relatively useful predictors of A1c ≥6.5%. If age and BMI are included as a “screening test” for A1c ≥6.5%, the resulting AUC was 0.89 (95% TRUCK DRIVERS AND DIABETES 39 CI=[0.75, 1.00]). [As a side note, this AUC is higher than the AUCs for ADA T2DM risk and glycosuria]. If ADA risk was added to the analysis, the AUC increased from 0.89 to 0.90 (95% CI=[0.78, 1.00]), but this was not a significant improvement in the AUC (p=0.539). If glycosuria was added to the analysis, the AUC increases to 0.95 (95% CI=[0.87, 1.00]), but this was not a significant improvement in the AUC (p=0.117). Older age (p=0.036) and higher BMI (p=0.0074) were significantly associated with presence of pre-diabetes/diabetes (A1c ≥5.7%). Higher BMI (p=0.0097) was significantly associated with presence of diabetes (A1c ≥6.5%). When the screening tests were added to the logistic models for prediction of pre-diabetes/diabetes, the AUC increased, but the improvement was not statistically significant. Summary of Results There were 117 participants included in the study and included the following descriptive profile: 97% male, mean age of 45.2 years (SD=13.4), and a mean BMI of 31.2 kg/m². Of the participants, 27% had a POC A1C within pre-diabetes range, 4% had a POC A1C within diabetic range, and the remaining 69% had a POC A1C within normal range. Of the 117 participants, only two had positive glycosuria. The results of the ADA T2DM Risk Tests indicated 49% screened positive for diabetes risk. Both screening tests (ADA T2DM Risk Test and urine glucose dip) were not associated with A1C ≥5.7% and had a poor diagnostic test measure. For those with the ADA T2DM Risk Test <5 and ≥5, there were 28% and 33% with A1C ≥5.7% (p=0.558). For those with negative and positive glucose urine test, there were 30% and 100% with A1C ≥5.7% (p=0.093). In general, the diagnostic test measures were poor as measured by AUC of 0.53 for both screening tests (p-value for difference in AUC between screening tests =0.886). TRUCK DRIVERS AND DIABETES 40 The screening tests were associated with A1C ≥6.5% but had low diagnostic test measures. Those with ADA T2DM Risk test ≥5 had a higher percent with A1C ≥6.5% as compared to those with ADA T2DM Risk test <5 (9% versus 0%, p=0.0015). In general, the overall diagnostic test measures were low as measured by AUC of 0.77 for ADA T2DM Risk Test and 0.70 for glycosuria (p-value for difference in AUC between screening tests = 0.575). Age and BMI were associated with A1C and were relatively useful predictors of prediabetes/diabetes. However, after adjusting for age and BMI, the additional utility of the screening tests was not significant. Older age (p=0.036) and higher BMI (p=0.0074) were significantly associated with presence of pre-diabetes/diabetes (A1C ≥ 5.7%). Higher BMI (p=0.0097) was significantly associated with presence of diabetes (A1C ≥6.5%). When screening tests were added to the age and BMI adjusted logistic models for prediction of prediabetes/diabetes, the AUC increased but the improvement was not statistically significant. DISCUSSION Summary The aim of this quality improvement initiative was to determine whether implementing an evidence-based screening questionnaire, in this case the ADA T2DM Risk Test, was more effective at identifying CMV drivers at risk of T2DM during their DOT physical, versus current practice of sole reliance on glycosuria results. Results of the data collected during this QI initiative did not support the effectiveness of using the ADA T2DM as a potential screening method for active prediabetes/diabetes. Comparatively, the glycosuria data collected aligned with current research, showing low sensitivity but high specificity (Storey et al., 2018). The design of this QI initiative was based on the IDHCD paradigm, which focuses on improving access of health resources to CMV drivers and providing motivation and improving TRUCK DRIVERS AND DIABETES 41 attitudes towards healthy behavior among CMV drivers (Lemke et al., 2016). The strength of this project was the pathway for discussion it opened with CMV drivers about prediabetes, diabetes, appropriate screening, and lifestyle influences. Many participants vocalized enthusiasm of having a diabetes screening test done due to family history or wanting to gain information about their own health. By specifically broaching the topic of diabetes screening with CMV drivers allowed assessment of their baseline knowledge and meaningful education about influencing factors of diabetes. Interpretation When comparing the sensitivity and specificity of the ADA T2DM Risk Test results of this QI initiative to the results of Gamston et al., (2020), the results were not consistent. Gamston et al., (2020) found the ADA T2DM Risk Test to demonstrate high levels of sensitivity and specificity for identifying prediabetes using POC A1C for verification compared to two other screening methods. The notable variance in outcomes may be based on the population data was collected from the comparative study (little is known about the nature of work done by the participants), or the number of participants in each study (740 in Gamston et al., versus 117 in this QI initiative). Although this QI initiative did not have the anticipated results, it does highlight the need for educating CMV driver about diabetes and prediabetes screenings, how/where to have screenings done, and lifestyle modifications that can help decrease their risk of developing diabetes. Implications As previously mentioned, the results of the glycosuria data collected during this QI initiative were consistent with Storey et al., findings (2018). Although current practice of collecting a urine glucose level during a DOT exam does help identify some diabetic individuals, TRUCK DRIVERS AND DIABETES 42 it is not an effective method to capture diabetic individuals before potential microvascular damage occurs (Tabák et al., 2012). The most effective method would be to offer POC A1C, however, each clinic or ME would need to weigh costs involved with POC A1C testing and whether that is a service they would like to offer. Since DOT exam requirements are established by the FMCSA, MEs must complete the urine glucose test but may also request additional information (such as laboratory testing or POC testing) to help them decide if a CMV driver meets medical qualification. MEs could also consider working with local CMV employers to promote annual wellness exams or wellness fairs where they could offer body metrics (BMI, BP, pulse, etc.) and POC testing for A1C and cholesterol levels. Future research may consider comparing random glucose levels of CMV to POC A1C levels during the DOT exam. This could open the option of checking a random glucose level during the DOT exam for anyone who meets the current diabetes screening criteria. Limitations Of the study participants, although all were in the clinic for their DOT exam, not all participants were long-haul CMV drivers. Participants were a combination of long-haul drivers, short-distance drivers, mechanics, bus drivers, and others who require DOT medical cards. Majority of the background information gathered focused on the health disparities of long-haul CMV drivers. Focusing specifically on long-haul CMV drivers may provide different results than this study. Also, 117 participants may not be a large enough sample size to extrapolate accurate data from within this population. Another limitation was the subjectivity of the question on the ADA T2DM risk test regarding being physically active. Many drivers verbally questioned if there were parameters to TRUCK DRIVERS AND DIABETES 43 define what was meant by being physically active. Since there was not a definition to quantify physical activity on the ADA T2DM risk test, participants’ interpretation of this question varied. Other questions on the ADA T2DM Risk Test were more objective, leaving little room for misinterpretation. The purpose of the ADA T2DM Risk Test is to help identify individuals who could be at risk of diabetes, not a diagnostic tool. Participants who scored ≥5 on the ADA T2DM risk test, despite what their POC A1C resulted, have elevated risk factors for T2DM and could benefit from prediabetes education. Although the ADA T2DM Risk Test results may not be an accurate indicator of current prediabetes/diabetes, it is a helpful tool to raise awareness of prediabetes and diabetes. Raising awareness of prediabetes and diabetes and emphasizing the importance of proper screening methods among the CMV population continues to be essential to CMV driver and public safety. DNP Essentials The Essentials of Doctoral Education (aka DNP Essentials) are a set of eight competencies that are designed guide practice-focused doctoral nursing programs, to enhance and advance nursing practice (American Association of Colleges of Nursing [AACN], 2006). Each of the DNP Essential competencies were achieved throughout the course of Bloomsburg University’s DNP program. Appendix H demonstrates how each of the eight competencies were achieved during the development and implementation of this quality improvement initiative. Conclusions As previously stated, all facets of health care providers within the community have a joint obligation to promote diabetic screenings and education as part of the National Diabetes Prevention Program (CDC, 2019a). Although the ADA T2DM Risk Test had low sensitivity and TRUCK DRIVERS AND DIABETES 44 specificity for accurately identifying participants with an A1C ≥5.7%, it did open a discussion pathway for CMV drivers to discuss their questions and concerns about diabetes. Future Implications for Clinical Practice Since the data was consistent with the ADA screening recommendations for T2DM regarding age and BMI, it would be reasonable to educate those individuals meeting the screening criteria to speak to their PCP about diabetes screening. Having the ADA T2DM Risk Test visible in the clinic for patient education would be a potential option to help promote prediabetes awareness. The POC A1C provided a fast and easy way to evaluate the accuracy of the ADA T2DM Risk Test results. Due to the costs involved with POC A1C testing, using POC A1C testing is not a sustainable screening option for the occupational health clinic where this QI initiative took place, mainly due to the clinic not being a facility that diagnoses or treats chronic conditions such as diabetes. As highlighted in the research for this QI initiative, CMV drivers face multiple health disparities that elevate their risk of T2DM and could impact driver and public safety. A suggestion for future study would be for occupational health providers to work with CMV employers to incorporate yearly health screenings (including A1C) and diabetes prevention programs into their benefits packages. CMV driver health directly impacts road safety issues and a joint effort between CMV drivers, CMV employers, and occupational health clinics to promote best health practices and decrease risk of T2DM warrants further research. Plan for Dissemination The results of this QI initiative where shared with shared with MEs in clinics associated with the occupational health clinic it was originally implemented, as well as the nursing department of Bloomsburg University. Further dissemination of these results included a poster TRUCK DRIVERS AND DIABETES 45 presentation at the AAOHN 2022 National Conference and additional conferences to reach key stakeholders. Furthermore, an overview of this QI initiative will be submitted for consideration of publication to a related professional journal. Funding Funding for this quality improvement initiative was provided by the American Association of Occupational Health Nurses Foundation with the UPS Research Grant. This grant was used to purchase the necessary equipment needed to conduct this quality improvement initiative, as well as funding to aid in dissemination of findings. The funding organization requested findings expand on future research relative to the impact on CMV driver health and safety, as well as health and safety of the greater public (i.e., injury/crash prevention). For budget details please reference Appendix I. TRUCK DRIVERS AND DIABETES Appendix A: Site Approval 46 TRUCK DRIVERS AND DIABETES Appendix A: Bloomsburg University IRB Approval Letter 47 TRUCK DRIVERS AND DIABETES 48 Appendix A: Site Authorization From: Dossey, Amber Sent: Thursday, September 2, 2021 12:55 PM To: Ruiz, Erin Cc: Eades, David ; Reynolds, Morgan ; Garnier, Dana ; Olszewski, Kim Subject: FW: Protected health information form Here you go! Good luck with your study. We are very proud of the challenge you’ve taken on! Amber Amber Dossey Vice President, Human Resources DISA Global Solutions, Inc. 10900 Corporate Centre Dr., Suite 250 Houston, TX 77041 Main: 281-673-2400 Direct: 281-730-5545 Mobile: 713-298-4483 www.disa.com Drug Testin |Background Screenin |DOT & Transportation Complian |Occupational Healt g g ce h This email may contain confidential information belonging to the sender which is legally privileged. The information is intended only for the use of the individual or entity named in the email. Any review, use, distribution or disclosure by others is strictly prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and delete all copies of this message. TRUCK DRIVERS AND DIABETES Appendix A: Protected Health Information and De-identification of Information 49 TRUCK DRIVERS AND DIABETES 50 TRUCK DRIVERS AND DIABETES Appendix B: Consent Document 51 TRUCK DRIVERS AND DIABETES 52 TRUCK DRIVERS AND DIABETES Appendix C: Evaluation Instrument: ADA Diabetes Risk Test permission 53 TRUCK DRIVERS AND DIABETES Appendix C: (continued) 54 TRUCK DRIVERS AND DIABETES Appendix C: American Diabetes Association Type 2 Diabetes Risk Test 55 TRUCK DRIVERS AND DIABETES Appendix D: Participant Material- Patient education 56 TRUCK DRIVERS AND DIABETES Appendix D: (continued) 57 TRUCK DRIVERS AND DIABETES 58 Appendix E: Email providing permission to use Iowa Model Revised (2015) Kimberly Jordan - University of Iowa Hospitals and Clinics Wed 7/14/2021 2:48 PM To: Erin M. Ruiz You have permission, as requested today, to review and/or reproduce The Iowa Model Revised: Evidence-Based Practice to Promote Excellence in Health Care. Click the link below to open. The Iowa Model Revised (2015) Copyright is retained by University of Iowa Hospitals and Clinics. Permission is not granted for placing on the internet. Reference: Iowa Model Collaborative. (2017). Iowa model of evidence-based practice: Revisions and validation. Worldviews on Evidence-Based Nursing, 14(3), 175-182. doi:10.1111/wvn.12223 In written material, please add the following statement: Used/reprinted with permission from the University of Iowa Hospitals and Clinics, copyright 2015. For permission to use or reproduce, please contact the University of Iowa Hospitals and Clinics at 319-384-9098. Please contact UIHCNursingResearchandEBP@uiowa.edu or 319-384-9098 with questions. TRUCK DRIVERS AND DIABETES 59 Appendix E: Iowa Model Revised (2015) Used with permission from University of Iowa Hospitals and Clinics TRUCK DRIVERS AND DIABETES 60 Appendix F: Project Timeline Spring 2022 June - August 2021 Present project to Finalize details of June 2020-May 2021 Project Development project: Introduction, Literature Review, September- December 2021 Data collection B.U. Nursing Dept faculty Methods _______________________________________________________________________ September 2020- ongoing August 2021 Literature Review Apply for B.U. IRB approval, obtain B.U. IRB approval, obtain site approval, PHI access consent, obtain permissions for project material January-May 2022 Data analysis, project completion Spring 2022 Disseminate Project Results TRUCK DRIVERS AND DIABETES 61 Appendix G: Literature Review Grid Literature Review Database CINAHL Complete Search Options: Limiters: Published date: 2016010120211231 Expanders: Apply equivalent subjects Search Modes: Boolean/Phrase CINAHL Complete Search Options: Limiters: Published date: 2016010120211231 Expanders: Apply equivalent subjects Search Modes: SmartText Searching CINAHL Complete Search Terms Diabetes AND truck Results 8 Truck Driver AND 78 Truck Driver AND Health NOT “sleep apnea” 72 Diabetes screening AND workplace 2 “diabetic screening” NOT gestational “undiagnosed diabetes” 6 “undiagnosed diabetes” AND truck 0 “undiagnosed diabetes” AND truck 1 Investigation into a traffic accident 3 Potentially good articles for comparing driver Health testing A1c point of care 146 Notes None of the articles are specific to diabetes and truck drivers, but a few look at health conditions in truck drivers Good choice of articles relating to overall health of truck drivers; many international studies; too broad Similar results as above search, minus articles specifically focused on OSA 1 Canadian study, the other focuses on financial incentives on workplace wellness programs Only 1/6 articles somewhat relevant Too broad, many articles about gestational diabetes TRUCK DRIVERS AND DIABETES Search Options: Limiters: Published date: 2016010120211231 Expanders: Apply equivalent subjects Search Modes: Boolean/Phrase CINAHL Complete Search Options: Limiters: Scholarly (peer reviewed) journals Published date: 2016010120211231 Subject age: All adult Language: English Expanders: apply equivalent subjects Search Modes: Boolean/Phrase PubMed Search Options: 2016010120200726 PubMed Search options: (2017-2021) (Journal article) (Adult: 19+years) 62 accuracies of phlebotomy drawn A1c vs point of care testing results. (a1c or glycemic control or hba1c) AND point of care testing testing A1c point of care (point of care hbA1c accuracy) 13 A good combination of articles reviewing reliability of POC A1c testing and articles focused on POC A1c testing in relation to prediabetes/diabetes screening. 67 This database provided a wider range of resources than CINAHL. 11 The second article listed was “Accuracy and Precision of a Point-of-Care HbA1c Test” (Arnold et al., 2019); Upon reviewing this article I looked under ‘Similar Articles’ and found four additional articles to review. TRUCK DRIVERS AND DIABETES PubMed Search options: (2016present) (Journal Article) (Humans) (English) (Male) (MEDLINE) (Nursing Journals) (Age 19-64) PubMed Search Options: 2016010120160726 CINAHL Complete, Medline, Medline Complete Search Options: Limiters: Published Date: 2016010120211231 English language; Human; Age groups (19-64) Expanders: Apply Equivalent subjects Narrow by Subject/Geographic: United States Search Modes: Boolean/phrase Diabetes screening workplace Diabetes truck driver Prediabetes AND prevention AND (screening or assessment or test or diagnosis) NOT (gestational diabetes or gdm or gestational diabetes mellitus, or diabetes in pregnancy) 63 46 Broad range of articles, a handful specifically about screening for type 2 diabetes, many others include looking at overall health risks in workplace. Some repetition from CINAHL searches but many that were not present in previous search results. 19 Best results from all searches 18 13 of the articles were relevant. This search was most helpful for finding evidence-based tools for diabetes screening. From articles found in this search, additional resources such as the AMA’s Prevent Diabetes STAT and the National Diabetes Prevention Program were found to be relevant. TRUCK DRIVERS AND DIABETES 64 Appendix H: DNP Essentials I. Scientific Underpinnings for Practice II. Organizational and Systems Leadership for Quality Improvement and Systems Thinking III. Clinical Scholarship and Analytical Methods for EvidenceBased Practice N602: Full Practice authority- review of literature and researching history in Pennsylvania. N604: Researching information for Covid-19 case study N608: Researching updates and background information for abstract, introduction, A1C POC technology. Conducting literature reviews and analyzing articles. N609: Reviewing and updating research for manuscript N610: Completing quality improvement initiative, data review, generating results and conclusions; developing methods for dissemination of project results; completing manuscript. N602: Reviewing fiscal note and cost considerations for Full Practice Authority proposed state legislation. N604: Shadowing clinical expert during leadership meetings. Viewing TED talks on leadership and system’s thinking N608: CITI Training N602: legislation research, reviewing full practice authority literature, researching history of Full Practice Authority in Pennsylvania and stakeholders. Researching voting records of State Legislators and Representatives. N606: Researching organizations that work with vulnerable populations; writing interview report; researching/developing nursing interventions; researching and recording podcast. N608: Reviewing SQUIRE formatting; researching background statistics; writing abstract; researching updates on diabetes, CMV driver health, A1C POC technology; writing and editing manuscript; CITI Training; literature review and article analysis; obtaining permissions from ADA; IRB application; writing participant consent; developing power point. TRUCK DRIVERS AND DIABETES IV. Information Systems/Technology and Patient Care Technology for the Improvement and Transformation of Health Care V. Health Care Policy for Advocacy in Health Care VI. Interprofessional Collaboration for Improving Patient and Population Health Outcomes 65 N609: IRB application/submission; discussing EBP/PHI approval with corporate representatives; communicating with clinical expert; IRB approval process; editing manuscript; compiling project supplies (forms, consents, equipment); data collection; grant documents. N610: Completing quality improvement initiative, data review, generating results and conclusions; developing methods for dissemination of project results; completing manuscript. N608: Researching A1C POC devices N609: Purchasing A1C POC device, reviewing instructions, and learning how to use the device. Using A1C POC device during data collection to evaluate effectiveness of ADA T2DM Risk Test. N602: Researching legislation; reviewing Full Practice Authority bill and history of the bill; attending PCNP conference where they discussed updates to Full Practice Authority bill; researching/learning about the legislative process in Pennsylvania; PSNA lobbyist office hour; researching cost considerations of Full Practice Authority bill; corresponding with State Representatives and Senators; speaking with Full Practice Authority stakeholders; researching ethical considerations of Full Practice Authority bill. N604: TED Talks related to healthcare policy and advocacy. N608: CITI Training N602: PCNP conference; office hours with Dr. Rick Garcia & Noah Logan (PSNA lobbyist); corresponding with State Representative and Senator; interviewing local Full Practice Authority stakeholders. N604: Shadow clinical expert in leadership meetings; TED Talks on interprofessional collaboration N606: Calling organizations that work with vulnerable populations; interview Lori Renne, director of Midwest Food Bank of Pennsylvania TRUCK DRIVERS AND DIABETES VII. VIII. 66 N608: Meetings with faculty advisor and clinical expert N609: Meetings with faculty advisor and clinical expert; meeting/ corresponding with corporate representatives regarding project and PHI approval; consulting with Biostatistician N610: Meetings with faculty advisor and clinical expert; corresponding with professional organizations regarding project dissemination. Clinical Prevention and Population N602: Researching ethical considerations for Health for Improving the Nation’s Full Practice Authority bill Health N604: Case studies- ‘On Being Transparent’, ‘Confidentiality’ and ‘Airforce One’ N606: Windshield survey; Interview with Lori Renne- director of Midwest Food Bank Pennsylvania; developing nursing interventions to address food insecurity N608: Developing Power Point Presentation and oral presentation on advocating for improving health of CMV drivers N609: Data collection- focus on improving health of CMV drivers N610: Dissemination of project findings Advanced Nursing Practice N604: Shadowing clinical expert in leadership meetings; Case study reviews. N609: Project Implementation/ data collection N610: Completing quality improvement initiative, data review, generating results and conclusions; developing methods for dissemination of project results; completing manuscript. TRUCK DRIVERS AND DIABETES 67 Appendix I: Budget Expense A1C Now+ (7 full kits with test strips) Lancets Fireproof/locked storage unit Printing of project materials (ADA T2DM Risk Test, consents, ADA Prediabetes educational handout) Biostatistician consultation AAOHN virtual conference- project dissemination via poster presentation Allowance for additional conference dissemination and taxes TOTAL: $1,429.05 $24.49 $32.99 $70.28 $600.00 $499.00 $2,344.19 $5,000 Cost TRUCK DRIVERS AND DIABETES 68 References Albert, N. M., Pappas, S. H., Porter-O’Grady, T., & Malloch, K. (2022). Quantum leadership: Creating sustainable value in health care. (6th ed.). Jones & Bartlett. American Association of Clinical Endocrinology. (n.d.). Screening and monitoring of prediabetes. https://pro.aace.com/disease-state-resources/diabetes/depthinformation/screening-and-monitoring-prediabetes American Association of Colleges of Nursing [AACN]. (2006). 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