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 edu>
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). The essentials of doctoral
education for advanced nursing practice. https://www.aacnnursing.org/DNP/DNPEssentials?msclkid=8a483a93ae0011ec8cc05303be81b935
American Diabetes Association. (2018a). Economic costs of diabetes in the U.S. in 2017.
Diabetes Care 2018. https://doi.org/10.2337/dci18-0007
American Diabetes Association. (2018b). The burden of diabetes in Pennsylvania. American
Diabetes Association. http://main.diabetes.org/dorg/assets/pdfs/advocacy/state-factsheets/Pennsylvania2018.pdf
American Diabetes Association. (2021). Classification and diagnosis of diabetes: Standards of
medical care in diabetes- 2021. Diabetes Care, 44, S15-S31. https://doi.org/10.237/dc21S002
American Medical Association. (2020). Diabetes prevention strategy overview.
https://amapreventdiabetes.org/case-diabetes-prevention
Apostolopoulos, Y., Lemke, M. K., Hege, A., Sonmez, S., Sang, H., Oberlin, D. J., & Wideman,
L. (2016). Work and chronic disease: Comparison of cardiometabolic risk markers between
truck driver and the general US population. Journal of Occupational and Environmental
Medicine, 58(11), 1098-1105. https://doi.org/10.1097/JOM.0000000000000867

TRUCK DRIVERS AND DIABETES

69

Arnold, W. D., Kupfer, K., Little, R. R., Amar, M., Horowitz, B., Godbole, N., Swensen, M. H.,
Li, Y., & San George, R. C. (2020a). Accuracy and precision of a point-of-care HbA1c test.
Journal of Diabetes Science and Technology, 14(5), 883-889. https://doi.org/10.11771932296819831292
Arnold, W. D., Kupfer, K., Swensen, M. H., Fortner, K. S., Bays, H. E., Davis, M., Klaff, L. J., &
San George, R. C. (2020b). Fingerstick precision and total error of a point-of-care HbA1c
test. Journal of Diabetes Science and Technology, 14(5), 890-895. https://doi.org10.1177/
1932296819831273
Bachmann, L. H., Lichtenstein, B., Lawrence, J. S., Murray, M., Russell, G. B., & Hook, E. W.
(2018). Health risks of American long-distance truckers: Results from a multi-site
assessment. Journal of Occupational and Environmental Medicine, 60(7), 349-355.
https://doi.org/10.1097/JOM.00000000000013191
Bowen, M. E., Schmittdiel, J. A., Kullgren, J. T., Ackermann, R. T., & O’Brien, M. J. (2018).
Building toward a population-based approach to diabetes screening and prevention for US
adults. Current Diabetes Reports, 18(104). https://doi.org/10.1007/s11892-018-1090-5
Buckwalter, K. C., Cullen, L., Hanrahan, K., Kleiber, C., McCarthy, A. M., Rakel, B., Steelman,
V., Tripp, R. T., & Tucker, S. (2017). Iowa model of evidence-based practice: Revisions and
validation. Worldviews on Evidence-Based Nursing, 14(3), 175-182.
https://doi.org/10.1111/wvn.12223
Centers for Disease Control and Prevention. (2019a). Information for health care professionals.
National Diabetes Prevention Program. https://www.cdc.gov/diabetes/prevention/infohcp.html

TRUCK DRIVERS AND DIABETES

70

Centers for Disease Control and Prevention. (2019b). Type 2 diabetes. Centers for Disease
Control and Prevention. https://www.cdc.gov/diabetes/basics/type2.html
Centers for Disease Control and Prevention. (2020a). National diabetes statistics report 2020.
Atlanta, GA. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statisticsreport.pdf
Centers for Disease Control and Prevention. (2020b). What is diabetes? Centers for Disease
Control and Prevention. https://www.cdc.gov/diabetes/basics/diabetes.html
Day, J., & Hait, A. (2019). Number of truckers at all-time high. https://www.census.gov/
library/stories/2019/06/america-keeps-on-trucking.html
Federal Motor Carrier Safety Administration. (2013). Our mission. https://www.fmcsa.dot.gov/
mission.
Federal Motor Carrier Safety Administration. (2014). National survey of long-haul truck driver
health and injury. https://www.fmcsa.dot.gov/sites/fmcsa.dot.gov/files/docs/
National%20Survey%20of%20Longhaul%20Truck%20Driver%20Health%20and%20Injury
.pdf
Federal Motor Carrier Safety Administration. (n.d.). Medical. https://www.fmcsa.dot.gov/
regulations/medical
Federal Motor Carrier Safety Administration. (2018). About the medical examination report
(MER) form, MSCA-5875. https://www.fmcsa.dot.gov/regulations/medical/medicalexamination-report-form-commercial-driver-medical-certification
Federal Motor Carrier Safety Regulations, 49 C.F.R. §390.5T (2019). https://www.ecfr.gov/cgibin/retrieveECFR?gp=1&ty=HTML&h=L&mc=true&=PART&n=pt49.5.390#se49.5.390_1
5

TRUCK DRIVERS AND DIABETES

71

Fellows, S. E. & Cipriano, G. C. (2019). Variations between point-of-care and laboratory HbA1c
testing in clinical practice. Puerto Rico Health Sciences Journal, 38(3), 189-191.
http://eds.a.ebscohost.com.proxy-bloomu.klnpa.org/eds/pdfviewer/pdfviewer?vid=4&sid=
16f18bf0-8c70-4ad3-be1b-f075344051a4%40sessionmgr102
Gamston, C. E., Kirby, A. N., Hansen, R. A., Redden, D. T., Whitley, H. P., Hanson, C., & Lloyd,
K. B. (2020). Comparison of 3 risk factor-based screening tools for the identification of
prediabetes. Journal of the American Pharmacists Association, 60, 481-484.
https://doi.org/10.1016/j.japh.2019.11.015
Giblin, L. J., Rainchuso, L., & Rothman, A. (2016). Utilizing a diabetes risk test and A1c pointof-care instrument to identify increased risk for diabetes in an educational dental hygiene
setting. The Journal of Dental Hygiene, 90(3), 197-202. https://jdh.adha.org/content/
90/3/197#:~:text=In%20a%20study%20by%20Genco%20et%20al%2C%2011,recommend
%20diabetes%20screenings%20in%20traditional%20dental%20settings.%2017
Giuliano, K. K. & Polanowicz, M. (2008). Interpretation and use of statistics in nursing research.
AACN Advanced Critical Care, 19(2), 211-222. https://doi.org/10.4037/15597768-20082014
Gregg, S.R., Dupont, L., & Burns, M. (2020). A format template- SQUIRE and APA 7th ed.
guidelines. University of Arizona Department of Nursing, DNP program.
Greiner, B., Mercer, H., Raymond, C., Sonstein, L., & Hartwell, M. (2020). A recommendation
for earlier screening of type 2 diabetes mellitus within the US population: A cross-sectional
analysis of NHIS data. Diabetes Research and Clinical Practice, 168, 1-4.
https://doi.org/10.1016/j.diabres.2020.108376

TRUCK DRIVERS AND DIABETES

72

Hansen, M., Fyall, A., Spyriadis, T., Rogers, D., & Brander, B. J. (2019). Motivating stakeholder
collaboration within the aerial adventure industry. International Journal of Tourism
Research, 21(3), 311–322. https://doi-org.proxy-bloomu.klnpa.org/10.1002/jtr.2262
Hege, A., Lemke, M. K., Apostolopoulos, Y., Perko, M., Sönmez, S., & Strack, R. (2017). US
long-haul truck driver work organizationa dnt the association with cardiometabolic disease
risk. Archives of Environmental & Occupational Health, 72(5), 303-310.
https://doi.org/10.1080/19338244.2016.1242468
Hege, A., Lemke, M. K., Apostolopoulos, Y., & Sönmez, S. (2018). Occupational health
disparities among U.S. long-haul truck drivers: The influence of work organization and
sleep on cardiovascular and metabolic disease risk. PLoS ONE 13(11): e0207322.
https://doi.org/10.1371/journal.pone.0207322
Hieshima, K., Sugiyama, S., Yoshida, A., Kurinami, N., Suzuki, T., Ijima, H., Miyamoto, F.,
Kajiwara, K., Jinnouchi, K., Jinnouchi, T., & Jinnouchi, H. (2020). Elevation of the renal
threshold for glucose is associated with insulin resistance and higher glycated hemoglobin
levels. Journal of diabetes investigation, 11(3), 617–625. https://doi.org/10.1111/jdi.13191
Hickman, J., Mabry, E. J., Marburg, L., Guo, F., Huiying, M., Hanowski, R., Whiteman, J., &
Herbert, W. (2020). Commercial driver safety risk factors. https://rosap.ntl.bts.gov/
view/dot/49620
Hickey, J. V., & Brosnan, C. A. (2017). Evaluation of Health Care Quality for DNPs (2nd ed.).
Springer Publishing Company.
Hirst, J. A., McLellan, J. H., Price, C. P., English, E., Feakins, B.G., Stevens, R. J., & Farmer, A.
J. (2016). Performance of point-of-care HbA1c test devices: Implications for use in clinical

TRUCK DRIVERS AND DIABETES

73

practice- a systematic review and meta-analysis. Clinical Chemistry and Laboratory
Medicine, 55(2), 167-180. https://doi.org/10.1515/cclm-2016-0303
John, M. N., Kreider, K. E., Thompson, J. A., & Pereira, K. (2019). Implementation of A1C
point-of-care testing: Serving under-resources adults with type 2 diabetes in a public health
department. Clinical Diabetes: A Publication of the American Diabetes Association, 37(3),
242-249. https://doi.org/10.2337/cd18-0082
Jones, G. (2016). The continuing case for point-of-care testing for HbA1C. MLO: Medical
Laboratory Observer, 48(8), 22-24. http://proxy-bloomu.klnpa.org/ login?url=
http://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116948206&site=ehostlive&scope=site
Kirkman, M. S., Tuncer, D., & Brown, C. E. (2019). Findings from a national diabetes survey:
Highlighting progress and opportunities for diabetes prevention and care. Diabetes
Spectrum, 32(3), 277-283. https://doi.org/10.2337/ds18-0086
Lemke, M. K., Meissen, G. J., & Apostolopoulos, Y. (2016). Overcoming barriers in unhealth
settings: A phenomenological study of health truck drivers. Global Qualitative Nursing
Research, 3, 1-9. https://doi.org/10.11771233339361663703
Lynn, L., Kay, D., Younes, N., Sen, S., & Jain, V. (2018). Accuracy and the proper use of the
point-of-care analysis in hemoglobin A1c measurement. Metabolic Syndrome and Related
Disorders, 16(3), 117-121. https://doi.org/10.1089/met.2017.0098
Mardis, C. (2018). Clinical, operational, and financial perspectives of POC diabetes testing.
MLO: Medical Laboratory Observer, 60(6), 8-14. http://proxy-bloomu.klnpa.org/
\login?url=http://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130313499&
site=ehost-live&scope=site

TRUCK DRIVERS AND DIABETES

74

Nathan, D. M., Griffin, A., Perez, F. M., Basque, E., Do, L., & Steiner, B., (2019). Accuracy of a
point-of-care hemaglobin A1C assay. Journal of Diabetes Science and Technology, 13(6),
1149-1153. https://doi.org/10.1177/1932296819836101
Okorie, O., These, M. S., Murtaugh, M. A., Sheng, X., Handy, R., & Hegmann, K. (2019).
Relationships between poor health and calroies from fat among commercial truck drivers.
Journal of Occupational and Environmental Medicine, 61(11), 944-948.
https://doi.org/10.1097/JOM.0000000000001706
Olson, R., Wipfli, B., Thompson, S. V., Elliot, D. L., Anger, K., Bodner, T., Hammer, L. B., &
Perrin, N. A. (2016). Weight control intervention for truck drivers: The SHIFT randomized
controlled trial, United States. American Journal of Public Health, 106(9), 1698-1706.
https://doi.org/10.2105/AJPH.2016.303262
Qualification of Drivers and Longer Combination Vehicle Driver Instructors, Subpart E- Physical
Qualifications and Examinations, 49 C.F.R. §391.41-46 (2019). https://www.ecfr.gov/cgibin/retrieveECFR?gp=&SID=&mc=true&r=PART&n=pt49.5.391#se49.5.391_146
Ravi, R., Abraham, G., Ravi, R., Mathews, M., & Partasarathy, R. (2020). Early detection of
renal disease among truck drivers through organized screening. Journal of Family Medicine
Primary Care, 30(9), 4880-4884. https://doi.org/10.4103/j.fmpc.jfmpc_903_20
Riva, M. M., Cantamessa, F., Borleri, D., & Mosconi, G. (2018). Occupational health and safety
of road haulage company employees. Medicina Del Lavoro, 109(3), 180-189.
https://doi.org/10.23749/mdl.v109i3.6827
Robbins, R. B., These, M. S., Ott, U., Wood, E. M., Effiong, A., Murtaugh, M., Kapellusch, J.,
Cheng, M., & Hegmann, K. (2020). Metabolic syndrome in commercial truck drivers:
Prevalence, associated factors, and comparison with the general population. Journal of

TRUCK DRIVERS AND DIABETES

75

Occupational and Environmental Medicine, 62(7), 453-459. https://doi.org/10.1097/
JOM.0000000000001863
Sobolesky, P. M., Smith, B. E., Saenger, A. K., Schulz, K., Apple, F. D., Scott, M. G., Wu, A. H.,
Little, R. R., & Fitzgerald, R. L. (2018). Multicenter assessment of a hemoglobin A1c pointof-care device for diagnosis of diabetes mellitus. Clinical Biochemistry, 61, 18-22.
https://doi.org/10.1016/j.clinbiochem.2018.09.007
Spiess, K. E., Sansosti, L. E., & Meyr, A. J. (2017). Diabetes driving studies- Part 2: A
comparison of brake response time between drivers with diabetes with and without lower
extremity sensorimotor neuropathy. Journal of Foot & Ankle Surgery, 56(3), 573-576.
https://doi.org/10.1053/j.jfas.2017.01.043
Storey, H. L., van Pelt, M. H., Bun, S., Daily, F., Neogi, T., Thompson, M., McGuire, H., &
Weigl, B. H. (2018). Diagnostic accuracy of self-administered urine glucose test strips as a
diabetes screening tool in a low-resource setting in Cambodia. BMJ Open, 8(3), e019924.
https://doi.org/10.1136/bmjopen-2017-019924
Sylvia, M. & Terhaar, M. (2018). Clinical analytics and data management for the DNP (2nd ed.).
Springer Publishing Company.
Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: A
high-risk state for developing diabetes. Lancet, 379(9833), 2279-2290.
https://doi.org/ 10.1016/S0140-6736(12)60283-9
Thiese, M. S., Moffitt, G., Hanowski, R. J., Kales, S. N., Porter, R. J., Hartenbaum, N., &
Hegmann, K. T. (2021). What medical conditions limit or medically disqualify truck drivers:
A large cross sectional study. Journal of Occupational and Environmental Medicine, 63(2),
139-146. https://doi.org/10.1097/JOM.0000000000002101

TRUCK DRIVERS AND DIABETES

76

Toro-Crespo, M., Sánchez-Mora, C., Fernández-Riejos, P., Maesa-Márquez, J. M., & GonzálezRodríguez, C. (2017). Evaluation of 3 hemaglobin A1c point of care instruments. Point of
care testing for HbA1c: Evaluation of Cobas b101, B-analyst and Afinion™. Clinical
Laboratory, 63(7), 1107-1112.
U.S Preventative Services Task Force [USPSTF]. (2021). Prediabetes and type 2 diabetes:
Screening. https://uspreventiveservicestaskforce.org/uspstf/recommendation/screening-forprediabetes-and-type-2-diabetes
Whitley, H. P., Hanson, C., & Parton, J. M. (2017). Systematic Diabetes Screening Using Pointof-Care HbA1c Testing Facilitates Identification of Prediabetes. Annals of Family
Medicine, 15(2), 162–164. https://doi-org.proxy-bloomu.klnpa.org/10.1370/afm.2035
Zaccagnini, M. & Pechacek, J. M. (2021). The Doctor of Nursing practice essentials: A new
model for advanced practice nursing (4th ed.). Jones & Bartlett Learning.