Tailored Approaches to Reduce Distress and Improve Self-Management for Veterans With Diabetes (TARDIS) - Survey & Qualitative Interview
Conditions: Diabetes, Diabetes Distress, Self-Management, Veteran
Sponsor: VA Office of Research and Development

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 1 of 33 
Tailored Approaches to Reduce Distress and Improve Self-Management for 
Veterans with Diabetes (TARDIS) – Survey & Qualitative Interview 
 
NCT04587336 
 
Protocol version approved on 10/29/2020 
 
 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 2 of 33 
PROTOCOL TITLE: Tailored Approaches to Reduce Distress and Improve Self-
Management for Veterans with Diabetes (TARDIS) – Survey & Qualitative Interview 
 
PRINCIPAL INVESTIGATOR: Allison Lewinski, PhD, MPH (PhD non-clinician) 
 
VERSION NUMBER AND DATE: Version 3 – 09/28/2020 
 
SPONSOR/FUNDING SOURCE: Health Services Research & Development, Nursing 
Research Initiative 
 
 
 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 3 of 33 
Purpose 
Veterans with type 2 diabetes (T2D) may become overwhelmed with the self-
management behaviors needed to maintain optimal health. Veterans may experience 
diabetes distress (DD), a concept distinct from depression, due the amount and 
frequency of these behaviors. Diabetes distress negatively influences the Veteran’s 
engagement in self-management and subsequent glycosylated hemoglobin (HbA1c) 
levels. Previous interventions aimed at improving T2D self-management and reducing 
DD do not tailor T2D self-management information to a Veteran’s DD, which may be 
one reason interventions are ineffective at reducing DD.  
The etiology of DD in Veterans may be different than non-Veterans due to the 
cumulative nature of Veteran-related comorbid psychosocial factors (e.g., post-
traumatic stress disorder (PTSD), depression). Conceptually, DD, depression, and 
PTSD are unique and independent factors; however, in each individual, these factors 
may overlap and negatively impact a Veteran’s engagement in self-management. Thus, 
this proposal examines the impact of correlating factors (e.g., sociodemographic, 
psychosocial, and environmental) on DD using surveys and semi-structured interviews. 
The proposed Nursing Research Initiative (NRI) includes several projects designed to 
develop a novel intervention to help Veterans with type 2 diabetes (T2D) engage in self-
management. The purpose of this research study is to understand diabetes distress and 
associated factors in veterans with type 2 diabetes. The current IRB protocol covers 
surveys and semi-structured interviews. This study will be performed entirely by our 
team at the Durham Center of Innovation to Accelerate Discovery and Practice 
Transformation (ADAPT). These data collected will inform the development of an 
intervention that will provide tailored self-management information in conjunction with 
supportive services based upon a Veteran’s DD.  
Background and Significance 
An estimated 20-25% of Veterans are diagnosed with diabetes,1 an incidence three 
times higher than the national rate. Treatment and management of poorly self-managed 
diabetes is costly as medical care costs are 2.3 times higher for individuals with 
diabetes than for those who do not have the disease.2 Regular preventative care is 
essential to prevent comorbidities and maintain optimal health. 
 Individuals with T2D provide 99% of their own care; self-management of T2D is 
person-specific and ever-present.3 Consistent and sustained self-management 
decreases comorbidity development, and positively influences quality of life and 
psychosocial outcomes.4 Inconsistent self-management increases the risk of poor 
health outcomes including microvascular and/or macrovascular problems and 
psychosocial complications.5 Yet rates of self-management remain sub-optimal.  
T2D self-management is omnipresent, inescapable, and burdensome. A Veteran may 
become overwhelmed and/or frustrated because of the amount and frequency of T2D 
self-management behaviors, and these negative feelings may lead to inattention to 
critical self-management behaviors such as eating healthy or blood glucose checks.6,7 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 4 of 33 
Veterans often experience challenges to self-management despite knowing the 
importance of these behaviors.3 Self-management challenges include limited access to 
a healthcare provider, unreliable transportation, limited knowledge of healthy food 
options, beliefs about T2D medications, or a lack of support.6-9 Additionally, diabetes 
distress (DD)10 and other psychosocial factors (i.e., depression, post-traumatic stress 
disorder)11,12 negatively influence an individual’s engagement in self-management and 
health outcomes. Due to the inherent complexity of T2D self-management, we propose 
that tailoring on DD is one way to focus the provision of tailored self-management 
information and connections to supportive services. 
Diabetes distress encompasses the cognitive, physical, and affective experience of 
living with T2D. Diabetes distress occurs when an individual is overwhelmed with T2D, 
related self-management behaviors, and knowledge that T2D is progressive and 
uncurable.10 Diabetes distress is subjective and person-specific; and while DD may 
fluctuate over time, DD may be continually present.10,13-15 Undiagnosed and untreated 
DD, may be one cause of a Veteran’s poor self-management, thus resulting in poor 
health outcomes.  
Diabetes distress is commonly assessed via the Diabetes Distress Scale14 or the 
Problem Areas in Diabetes Scale.16 Research indicates both measures demonstrate the 
construct of DD is distinct from general anxiety, stress, and depression.13,17-19 Both the 
Diabetes Distress Scale and Problem Areas in Diabetes scales assess DD in several 
domains, and each DD domain addresses a separate aspect of the burden of 
T2D.14,20,21 The Diabetes Distress Scale measures DD in four domains (i.e., regimen, 
emotional, interpersonal, healthcare provider),14 and the Problem Areas in Diabetes 
Scale assesses DD using similar domains (i.e., emotional, diabetes management, 
treatment, and social support burden).10,22 The Diabetes Distress Scale and Problem 
Areas in Diabetes Scale have demonstrated content validity in measuring an individual’s 
perception of the resources available, or not available, for self-management.13,19 
Diabetes distress is associated with more T2D complications, increased stress, 
being prescribed insulin by a healthcare provider, poor diet and exercise, inadequate 
support environments, and is more prevalent among women.10,19,23-26 Diabetes distress 
levels correlate with self-management behaviors such as monitoring one’s diet, 
engaging with providers, and monitoring one’s blood glucose.9,15,27-29 High to moderate 
levels of DD are related to poorer glucose regulation15, higher HbA1c20, lower 
medication adherence30, and poorer quality of life.31 Additionally, individuals with DD 
report diabetes-related physical burden, health care system distress, and distress with 
comorbid conditions.9 In this proposal, we will first examine the association between 
DD, the Veteran’s engagement in self-management, and the Veteran’s HbA1c.  
 
Guiding Framework.  
The self-directed learning (SDL) theory provides a useful lens for examining factors 
that influence DD. The self-directed learning theory posits that adults diagnose their 
learning needs, formulate goals, identify necessary resources, select applicable 
strategies, and evaluate their learning needs with or without the help of others.32 Three 
components are key to applying SDL: (1) person—characteristics such as life 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 6 of 33 
diverse populations, the factor structure is stable across sites and populations, the 
measure is parsimonious, and items are clear and readable.8,34,37,38,40 The surveys will 
provide insight into potential facilitators and barriers to DD and T2D self-management 
such as symptom severity, minimal social support, and/or high chronic illness needs. 
 
Measures. We will use surveys to understand DD, and facilitators and barriers to T2D 
self-management, in a sample of Veterans who receive care at Durham.  
Variables and data collection methods for the baseline survey.  
Variable 
Measure 
Source 
HbA1c 
Most recent value in the past 180 days 
EHR/CDW 
Diabetes Distress 
Diabetes Distress Scale. 17-items that use a 6-point Likert 
scale that measures diabetes distress in 4 domains: 
emotional, interpersonal, healthcare provider, 
regimen.14,18,34 Scores indicate low, moderate, or high  
diabetes distress.14 Internal reliabilities of 0.92 - 0.93 
demonstrated.14,18 
PRO 
Correlating Factors 
Sociodemographic 
 Demographics 
Age, sex, gender, race/ethnicity, etc. 
EHR/CDW 
 Comorbidities 
Current medical diagnoses during the past 180 days 
EHR/CDW 
 Medications 
Currently prescribed medications during the past 180 days EHR/CDW 
 Symptom 
severity 
Diabetes Symptom Checklist-R. 37-items to assess 
physical and mental symptoms related to living with 
diabetes in the past month.35 
PRO 
 Chronic Illness 
Needs 
Chronic Illness Resource Survey. 20-items that use a 5-
point Likert scale to assess current level of social support 
for T2D self-management from multiple sources: family 
and friends, neighborhood, community, and health 
policies.36  
PRO 
Psychosocial 
 Depression 
The Patient Health Questionnaire-2 (PHQ-2). A two-item 
measure that uses a Likert scale to assess for 
depression.37 Established reliability and specificity for 
assessing depression in adults with diabetes.37 
PRO 
 PTSD 
Primary Care PTSD Screen for DSM-5. 5-items that 
identify respondents with probable PTSD in primary care 
settings. Scores “yes” to 3 or more questions is 
considered positive for PTSD. Established reliability and 
specificity.38 
PRO 
Environmental 
 Social Needs 
Health Related Social Needs Survey. 10-items to assess 
unmet, health-related social needs in 5 domains: housing 
instability, food insecurity, transportation needs, utility 
needs, and interpersonal safety.39 
PRO 
Motivation 
 
 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 7 of 33 
Cognitive Interviews: Examine the understanding and interpretation of diabetes 
distress and the Diabetes Distress Scale in Veterans with T2D.  
Cognitive interviews examine how individuals construct and select answers as they 
complete a survey.43,44 Cognitive interviews are an essential method to assess and 
improve how measures operate.43,44 During a cognitive interview, individuals are read 
each survey question and asked to ‘think aloud’ and/or are probed regarding what they 
believe the question is asking and describe what information they are using to formulate 
a response.45 The resulting interview data indicates the individuals’ comprehension, 
retrieval process, judgments, and the breadth of information underlying the pre-specified 
response categories.43-45 These data from the cognitive interviews in this pilot study will 
help us to develop an understanding of how the Diabetes Distress Scale operates in 
Veterans with T2D. Obtaining Veteran’s perceptions of DD in the context of the DDS is 
essential in verifying what constitutes DD, identifying barriers to addressing DD, and 
creating potential solutions to DD. The data obtained will guide our decisions about 
retaining, deleting, modifying, or adding to the Diabetes Distress Scale. We will conduct 
cognitive interviews with ~12-15 Veterans with T2D who receive care at VA. 
Qualitative Interviews: Describe self-management challenges and preferred 
learning strategies to inform intervention components and delivery approach for 
Veterans with T2D.  
We purposefully chose the sequential explanatory design to identify to what extent, 
and in what ways, Veteran perceptions of DD describe the survey results and the 
overall study purpose.33 Qualitative data will enable us to develop tailored components 
that: (1) promote the Veteran’s active learning; and (2) address commonly occurring 
learning needs in a sample of Veterans. Due to the potentially normalized experience of 
DD, and our desire to develop tailored resources, other data collection methods are not 
be conducive to eliciting in-depth descriptions of DD.  
Semi-structured interviews will last 45-60 minutes and will be recorded as permitted 
by the Veteran. If the Veteran does not consent to being recorded, the qualitative 
analyst will take detailed notes throughout the interview. We will use a semi-structured 
interview guide to elicit Veterans’ experiences self-managing their T2D. Interviews will 
provide insight into how the Veteran interprets T2D self-management, strategies the 
Veteran uses in self-management, learning needs and preferences, including 
interactions with healthcare providers as part of self-management, and the associated 
 Self-Efficacy 
Diabetes Empowerment Scale-Short Form (DES-SF). 8-
items to assess diabetes related self-efficacy. 
Demonstrated validity and reliability.40,41 
PRO 
Self-Management 
Behaviors 
Summary of Diabetes Self-Care Activities. We will use the 
core 11-items that measure completion of T2D self-care 
behaviors in the past seven days. Validity and reliability 
have been demonstrated.42  
PRO 
Note: EHR/CDW=electronic health record/corporate data warehouse, data will be obtained by 
the RA upon study enrollment prior to the survey completion; PRO=patient reported outcome, 
data will be obtained via survey/interview. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 8 of 33 
challenges with self-management, in their sociodemographic, psychosocial, and 
environmental context. To obtain rich data we will use probes and follow-up questions to 
elicit descriptions of diabetes self-management.46 The qualitative analyst will not be 
informed of the Veteran’s data prior to the interview to ensure objectivity. 
Sources of Materials 
This study uses materials from several different sources. The following human subjects 
related data elements will be collected for this study, with the source(s) of information 
noted:  
Data element 
Source(s) 
Cognitive Interviews 
 
Perceptions of the Diabetes Distress 
Scale 
Audio recordings and field notes from 
cognitive interviews 
 
 
Survey  
 
Type 2 diabetes diagnosis 
DVAHCS medical records/CDW 
HbA1c, medications, comorbidities 
DVAHCS medical records/CDW 
Demographics, computer literacy, 
diabetes distress, post-traumatic stress 
disorder, depression, self-management 
behaviors, social needs, chronic illness 
needs 
Self-reported survey responses 
 
 
Qualitative Interviews 
 
Perceptions of DD, self-management 
strategies and barriers, and intervention 
delivery strategy 
Audio recordings and field notes from 
semi-structured interviews 
 
Only individuals officially assigned to the study team will have access to individually 
identifiable information about human subjects. This will include the principal investigator, 
mentors and consultants, statistician, computer programmer, research assistant, project 
coordinator, qualitative analyst, and interventionists. All of these individuals will have 
completed VA required human subjects training and will be included on a staff listing 
with the DVAHCS IRB. Some data described above (e.g., HbA1c, health conditions 
noted in medical records) will be accessed from information already collected as part of 
usual care. All additional data collected from subjects will be specifically for the 
proposed research project and not a part of clinical care.  
Risk/Benefit Assessment 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 9 of 33 
We do not anticipate any significant medical risks to be associated with participation 
in this study. There are no financial or legal risks associated with this study. The types 
of research risk we anticipate are listed below.  
1. Risk of loss of confidentiality, specifically, loss of data or breach of 
confidentiality.  
2. Survey Burden, specifically, temporary unease related to answering survey 
questions and/or interview questions, particularly related to any questions or 
discussion around diabetes distress, post-traumatic stress disorder, 
depression, social relationships, health related social needs, chronic illness 
needs, or environmental concerns.  
 
We will use the following strategies to minimize risks.  
1. In order to minimize any risks regarding privacy of individuals and 
confidentiality of data, we will take specific measures to protect both paper 
and electronic data. To ensure confidentiality, all records will be coded by the 
participant’s study identification number, not by name. Except when required 
by law, participants will not be identified by name, social security number, 
address, telephone number, or any other direct personal identifier in study 
records disclosed outside of Durham VA Healthcare System. We will only 
collect the data necessary for the study. Survey Data will be collected and will 
be entered directly into the RedCAP survey application for the project. 
Electronic study data will be kept in folders and databases that are stored on 
a secure VA server behind the VA firewall, on secure password-protected 
computers, and access will be restricted to project personnel who are IRB-
certified and whose job functions require access to these data. Data will be 
maintained on this server throughout the study duration and for at least 6 
years after study completion, per records control schedule. When data are 
collected on hard copy such as questionnaires and interview notes, a study ID 
number used. Hard copies of data will be stored separately from the 
identifiable data in a locked file cabinet in a secure research office at the 
DVAHCS. Finally, all project staff will complete educational units required by 
the Durham VAHCS IRB. Server hardware, operating system and database 
service performance will be constantly monitored by OIT technicians. If loss 
or theft of study data occurs, study personnel will follow local guidelines by 
immediately notifying the VA police, the PI, the ISO, and the Privacy Officer.  
2. Potential risks will be minimized by closely monitoring emotions during 
surveys and interviews. Participants will be informed that participation in any 
and all aspects for the project are completely voluntary that they are free to 
refuse to answer any items on the questionnaires or questions from the 
interview that they do not wish to answer. They are also informed that they 
are free to decline participation in any procedure and can withdraw from the 
study at any time. Regarding qualitative data, Veteran participants will be 
informed that all published results will be anonymized, including any mention 
of city/medical center/clinic to protect the identity of participants.  

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 10 of 33 
Selection of Subjects 
Potentially eligible patients will initially be identified via VA CDW/electronic health 
records data extract. (ICD-10 codes: E11.9, E11.8, and other inclusionary criteria 
detailed below, as well as the absence of any exclusionary diagnoses).  
Inclusion and Exclusion Criteria  
Veterans must meet all inclusion 
criteria 
Veterans who meet any one of the exclusion 
criteria will be excluded.  
• Diagnosis of type 2 diabetes 
(ICD-10 codes: E11.9, E11.8) 
• Documentation of HbA1c drawn 
within the past 180 days 
• Able to speak and read English 
• Be able to provide informed 
consent to participate in the 
study.  
 
 
• New diagnosis of T2D within the last 60 
days 
• Hospitalization for mental illness within the 
past 30 days 
• Receiving active chemotherapy and/or 
radiation treatment 
• Diagnosis for Metastatic Cancer 
• Recent hospitalization within the past 60 
days that would influence their diabetes 
medication regimen (e.g., myocardial 
infarction, cerebrovascular accident, 
coronary artery bypass grafting, etc.) 
• Currently receiving Kidney Dialysis 
• Limited hearing or speech difficulties that 
influence the Veteran’s ability to complete 
the survey 
• Dementia, delirium, or other cognition issues 
that influence the Veteran’s ability to provide 
consent and complete the survey. 
 
We will use the quantitative surveys to describe the prevalence of DD and the 
sociodemographic, psychosocial, and environmental factors of DD and DD domains. 
We will survey up to Veterans (n=250), and balance enrollment by HbA1c (<9 or ≥9) 
and medication (no insulin, insulin), to describe DD. Using the same population of 
enrolled participants we will conduct the cognitive interviews (n=~12-15) to guide our 
decisions about retaining, deleting, modifying, or adding to the DDS as well as 
qualitative interviews with Veterans with T2D (n=~36) to obtain perceptions of DD, self-
management strategies and barriers, and intervention delivery strategy. We will balance 
enrollment by HbA1c, medication, and Diabetes Distress Scale score (low, moderate, 
high). 
Subject Recruitment 
For the purposes of screening, recruiting, and determining eligibility information will 
be obtained through written communication with the prospective participant and/or 
identifiable private information will be obtained by accessing medical records. Potential 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 11 of 33 
participants will be identified utilizing the VA’s Information and Computing Infrastructure 
(VINCI) Corporate Data Warehouse (CDW) and/or local electronic health record (EHR). 
With approval of a HIPAA waiver, we will initially conduct a data pull with real Social 
Security Numbers (SSNs) from VINCI.  In order to identify potential participants, we will 
complete computer-based screening using the VA Corporate Database Warehouse and 
our established inclusion/exclusion criteria. Based on protocol we will select the 
appropriate number of participants to be included in each letter batch and create the 
letters for each batch of potential participants. Multiple batches of letters will be sent 
until we reach our recruitment goal. An IRB approved letter along with study information 
documents will be sent to the potential participant informing them of the study and 
providing a phone contact for study staff.  
Each week eligible participants will be mailed introductory letters that provide basic 
information about the study and let patients know that a study team member will call 
them to ask whether they are interested in participating. The letter will also provide 
patients with a telephone number they may call to leave a message saying they are not 
interested in participating and do not want to receive a call from the study team. 
Between 5-7 days after the letter is sent, the research assistant (RA) will call Veterans 
who do not opt out to assess interest in participation. Veterans who do not opt out will 
be screened for eligibility. Participants contacted will next complete a brief telephone-
based screening questionnaire to assess eligibility criteria that are not captured in 
DVAHCS electronic medical records. We will only ask a limited number of questions 
that are needed to determine study eligibility. The RA will conduct a verbal consent 
process with eligible Veterans. Participants will then be asked if they would like to 
complete the surveys at that time or another date and time. The informed consent 
process will include the option to collect and use the Veteran’s health record data in 
analysis for current study and future studies and re-contact the participant for the 
qualitative interviews. The surveys will be completed by a study research assistant. 
After the Veteran completes the quantitative surveys, we will screen the Veteran for 
eligibility based upon the Veteran’s HbA1c, medication use, and Diabetes Distress 
Scale score. Eligible Veterans will be invited to complete the semi-structured interviews. 
Consent Process 
We will request a waiver of written consent, waiver of consent documentation and 
waiver of HIPAA authorization from the IRB to conduct recruitment activities described 
above.  Every effort will be made to talk via telephone with participants in privacy of their 
own home when completing the consent and questionnaires for data collection. 
Participants will be able to indicate to study staff if they are in a private environment or if 
they would like to be called at another time. This will ensure privacy and protect against 
embarrassment of sensitive survey questions and interview details. 
Prior to beginning recruitment, the PI will meet with key personnel to review the 
protocol, the informed consent process, and the collection of questionnaires. Research 
study staff will demonstrate the essential steps in how to implement the protocol to 
ensure proper technique is communicated to participants. PI will ensure all study staff 
are up-to-date with required VA security and privacy training. PI will also ensure 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 12 of 33 
research study data are no longer accessible to study personnel when they are no 
longer part of the research team. 
Study Procedures 
All participants will participate in the survey, there is no randomization or groups. 
After the consent process, Veterans will complete a battery of survey questions 
administered by the RA.  These surveys will be collected in the study RedCAP survey. 
In anticipation of recruitment challenges, we will use methods: (1) flexible staffing of the 
PC and RA, to recruit and enroll Veterans who have varying work and life commitments; 
(2) clear language in the initial recruitment letter, to indicate the time commitment for the 
survey; (3) using a call schedule developed by the Durham COIN PCs, we will make 
three calls on three different days, at three different times, and leave two messages. We 
will offer Veterans the option of completing the surveys via telephone. However, if the 
Veteran prefers to complete the surveys in person, we will meet with these Veterans in 
conjunction with their scheduled medical appointment at Durham VA. 
Variables/data collected in RedCAP survey  
Variable 
Measure 
Source 
 
HbA1c 
Most recent value in the past 180 days 
EHR/CDW 
 
Diabetes Distress 
Diabetes Distress Scale.  
PRO 
 
Demographics 
Age, sex, gender, race/ethnicity, etc. 
EHR/CDW 
 
Comorbidities 
Current medical diagnoses during the past 
180 days 
EHR/CDW 
 
Medications 
Currently prescribed medications during the 
past 180 days 
EHR/CDW 
 
Symptom severity 
Diabetes Symptom Checklist-R.  
PRO 
 
Chronic Illness Needs 
Chronic Illness Resource Survey.  
PRO 
 
Depression 
The Patient Health Questionnaire-2 (PHQ-
2).  
PRO 
 
PTSD 
Primary Care PTSD Screen for DSM-5.  
PRO 
 
Social Needs 
Health Related Social Needs Survey.  
PRO 
 
Self-Efficacy 
Diabetes Empowerment Scale-Short Form 
(DES-SF).  
PRO 
 
Self-Management Behaviors 
Summary of Diabetes Self-Care Activities.  
PRO 
 
Stigma, shame 
PROMIS Stigma Short Form 
PRO 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 13 of 33 
The RedCAP survey will provide RA information pertaining to the Veteran’s eligibility for 
the qualitative interview, based upon the Veteran’s HbA1c, medication use, and 
Diabetes Distress Scale score.  Those identified will be invited to complete the semi-
structured interview. If patients decline to participate in the cognitive or qualitative 
interview no further contact will be made.  For those who, accept the invitation, the RA 
will, based on patient and interviewer availability, schedule the interview to take place 
within the next 2 weeks. Prior to beginning the either the cognitive or qualitative 
interview, a verbal consent script will be reviewed with the participant and verbal 
willingness to continue participation will be captured at the beginning of the audio 
recording. Recording and transcription of the qualitative interviews will be conducted 
utilizing VA approved software installed and configured by VA OI&T personnel.  Audio 
recordings will be captured using WebEx as the software to record the audio portion of 
the patient interviews. WebEx recordings will be saved directly to the restricted study 
folder on the R drive. We will use the approved version of Audacity software 
(http://trm.oit.va.gov/ToolPage.aspx?tid =5566#) to edit  the audio file in the study folder 
on the HSRD VA project server. 
Adverse Events 
There are no hazards associated with the study protocol; however, there are several 
hazards that could potentially occur to the adult population with type 2 diabetes and 
include sickness or infection, hospital admissions, falls, etc. Dr. Lewinski is a licensed 
registered nurse and will be involved in every aspect of the study. Any concern about 
patient health status will be communicated to Dr. Lewinski by study staff. If Dr. Lewinski 
is on leave or unavailable, Dr. Bosworth and/or Dr. Crowley (a licensed VA-credentialed 
endocrinologist) will serve as the contact for research staff. The PI will work closely with 
the research coordinator throughout the study to identify individual hazards, if any 
occur. The PI will be notified of any hazard that occurs and will determine if the 
participant should be removed from the study. If it is agreed that the subject be 
removed, the PI and research coordinator will explain to the participant why it is in her 
best interest to withdraw from the study. The participants will be assured that nothing 
related to her during the study will affect any medical care in the future.  
Given that we propose contacting a small group of patients for a telephone interview, 
we will not utilize a Data Safety Monitoring Board. All adverse events will be reported 
per Durham VAMC requirements. All Serious, Unanticipated and Related adverse 
events will be reported to IRB within 5 business days of hearing of the event. All other 
adverse events will be reported at continuing review. 
Costs and/or Payments to Subjects 
 
Pain and physical activity 
PROMIS Pain Interference – (Short Form 6a) 
PRO 
Note: EHR/CDW=electronic health record, data will be obtained by the RA upon study 
enrollment prior to the survey completion; PRO=patient reported outcome, data will be 
obtained via survey/interview. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 14 of 33 
There will be no cost to participants for care they receive as a participant in the VA 
research project. To compensate participants for their time, they will receive a direct 
deposit /check by mail from the VA for $30 four to six weeks after survey completion. If 
they are selected to complete the cognitive interviews, they will receive a direct deposit 
/check by mail from the VA for $30 four to six weeks after completion. If they are 
selected to complete a qualitative interview, they will receive an additional $30. The total 
amount participants can receive through the study is $90. If participants withdraw from 
the study early, they will be paid for the survey/interviews they have completed. We will 
use the current established process for providing compensation to patients at this 
facility. 
Data and Safety Monitoring 
Because risks to subjects will not exceed usual care, and all subject contact takes 
place within the context of telephonic survey collection and discrete qualitative 
interviews, no Data Safety Monitoring Board will be utilized. The PI assumes 
responsibility for monitoring all data and safety information collected during the conduct 
of the program evaluation described in this research proposal.   
One aspect of our data and safety monitoring plan will involve reporting to the 
principal investigator and DVAHCS IRB. Study team members who become aware of 
any adverse event related to the study (who can include the research assistant, project 
coordinator, and qualitative analyst) will notify the principal investigator, Dr. Lewinski, 
immediately. Safety monitoring for adverse events (AEs) will be conducted in real time 
by Dr. Lewinski and/or the project coordinator. The following information about AEs will 
be collected: 1) the onset and resolution of the AE, 2) an assessment of the severity or 
intensity (use existing grading scales whenever possible), 3) an assessment of the 
relationship of the event to the study (definitely, probably, possibly or not related), and 
4) action taken (e.g., none, referral to physician). The PI, in consultation with Dr. 
Bosworth (primary mentor) will determine the severity of the event, will assign attribution 
to the event, and will monitor the event until its resolution. Any adverse events will be 
reported to the IRB in accordance with the local Human Research Protection Program’s 
Standards of Practice. Reports of non-serious AEs are required as part of these 
progress reports.  
Study team members will have contact information for Dr. Lewinski for evening / 
weekend hours. (Most study activities will occur during regular business hours, but 
some participants may request to be contacted for a screening interview during evening 
or weekend hours.) If Dr. Lewinski is not available for contact when a study team 
member becomes aware of a study-related AE, Dr. Bosworth (Dr. Lewinski’s primary 
mentor) or Dr. Crowley (Dr. Lewinski’s co-mentor), will be contacted. Once Dr. Lewinski 
(or Dr. Bosworth or Dr. Crowley) is contacted about the AE, she/he will decide about the 
reporting requirements in accordance with the DVAHCS IRB guidelines.  
Dr. Lewinski will meet at least weekly with study personnel to discuss participants’ 
reactions to the survey/interviews and any AEs or unanticipated problems. Dr. Lewinski 
will also meet weekly with Dr. Bosworth (primary mentor) and biweekly with Dr. Crowley 
(co-mentor). Monthly meetings between the investigators and the project coordinator 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 15 of 33 
will allow for ongoing progress reports, including the number of participants currently 
involved in the study, attrition rates, and scheduled data collection from participants, as 
well as notification and review of any AEs. All individuals involved in the study will 
develop and implement methods of verifying entered data and of quality control. 
Future Data Use  
We may want to run secondary data analyses related to the original aims of the grant 
(e.g., mediator/moderator analyses, analyses of baseline data, etc.). It is also possible 
that data collection under this proposal might be used to inform future surveys, 
interviews, and/or intervention development among patients with diabetes. Data would 
only be used from patients who agreed that their data could be stored and used for 
future analysis. A de-identified data set from the variables obtained for the project will 
be created.  A separate protocol, defining the possible new project(s) would be 
submitted to the Durham VA IRB. These future studies would request either a 
waiver/alteration to the ICF/HIPAA requirements or a consent form and updated HIPAA 
Authorization, as appropriate.  
Withdrawal of Participants 
VA Patients who no longer want to participate in the survey or qualitative interviews, 
may inform the staff.  The staff will document that change in status, so they are not 
prompted to contact the patient in the future.   
Data Analysis and Statistical Considerations 
Survey—Detailed study design and sampling plan. We will recruit Veterans (n = 
200) with T2D and who receive care at the Durham VA. We will conduct targeted 
recruitment to ensure equal numbers of well- and poorly-controlled Veterans. We will 
classify the Veterans into well-controlled (defined by HbA1c value < 9 during the past 
180 days) and poorly-controlled (HbA1c value ≥ 9 during the past 180 days); our 
sampling strategy is guided by VA/DoD clinical practice guidelines.47 Using EHR/CDW 
data, we will classify Veterans into no insulin (defined by taking only oral T2D 
medications and/or non-insulin injectable medications during the past 180 days) and 
insulin (defined by taking any insulin during the past 180 days; these Veterans may/may 
not also take oral T2D medication(s)). We are balancing enrollment because Veterans 
with different medication regimens may experience different challenges that influence 
DD.34,48 We will oversample women and younger Veterans to ensure we obtain diverse 
perspectives about DD. We will address sampling challenges as these challenges arise 
in order to ensure we obtain a diverse sample while aiming for the numbers shown in 
the Table.  
 
 
 
Table. Sampling plan for surveys.  
HbA1c 
Medication 
 
No Insulin Insulin Total 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 16 of 33 
Sample Size Considerations. Although our 
sample size of 200 Veterans with completed 
surveys is driven by practical considerations of 
feasibility, we will have a sufficient sample size 
to test our specified hypotheses regarding differences in DD. With our sampling plan, 
we assume an equal distribution of 100 patients within each HbA1c level and 
with/without insulin use. Given a sample size of 200 Veterans, we will be able to 
estimate 95% confidence intervals for a mean difference in the Diabetes Distress Scale 
with precision equivalent to an effect size of 0.28. Additionally, with a type-I error of 5%, 
we will have 80% power to detect mean differences in DD of effect size magnitude 0.4 
between groups. Note that our conclusions from these analyses will be based on 
defining a clinically meaningful difference, such as a decrease in DD, and relationships 
via estimates and confidence intervals, rather than by p-values. 
Analysis Plan. Standard descriptive statistics (e.g., mean, standard deviation, median, 
IQR, proportions, etc.) will be generated to characterize the Diabetes Distress Scale as 
well as the associated factors (e.g., psychosocial, sociodemographic) in the sample. In 
particular, we will examine the distribution of variables for sparsely populated categories 
or for deviations from normality. Descriptive statistics will be generated separately by 
HbA1c level and by insulin use. Rather than p-values, standardized differences will 
characterize differences between these sample subgroups.49 The primary goal of the 
surveys is to examine differences in Diabetes Distress Scale score by HbA1c level 
(HbA1c < 9 and HbA1c ≥ 9) and by medication regimen (no insulin, insulin). We 
hypothesize that mean DD will be higher in: (1) Veterans with HbA1c > 9 vs < 9; and (2) 
Veterans who use insulin vs those who do not. These hypotheses will be evaluated via 
separate multiple regression models with continuous Diabetes Distress Scale score as 
the outcome variable, and with the primary predictor being either an indicator for HbA1c 
> 9 or an indicator for insulin use. A limited number of additional predictors will be 
included in the adjusted model, chosen a priori by the guiding framework and by 
evidence of imbalance as indicated by the standardized differences. As exploratory 
analyses, we will also examine the interaction of HbA1c level and insulin use upon 
Diabetes Distress Scale score. Descriptive statistics will be generated by these four 
subgroups, and statistical inference will be conducted via a two-way ANOVA model 
including the main effects and interaction. All analyses will be performed using SAS 
software (SAS Institute Inc., Cary, NC). These analyses will enable us to determine 
which population of Veterans we should target for a diabetes self-management 
intervention. For instance, if we identify that DD is highest in Veterans with HbA1c ≥ 9, 
who do not use insulin, who are younger, and are male, we will target those Veterans 
for a self-management intervention. Knowledge of the factors associated with DD will 
provide insight into potential modifiable and unmodifiable barriers to T2D self-
management. 
 
Cognitive interviews—detailed study design and sampling plan. Audio 
recordings will be transcribed by the DVAHCS staff. When transcribed, names and 
other identifying information will be redacted. Transcripts will be saved in the secured 
VA project drive (v06.med.va.gov\DUR\HSRD\TARDIS_Aim1-2). We will use a 
convenience sampling plan to recruit ~12-15 Veterans with T2D who receive care at the 
< 9 
50 
50 
100 
≥ 9 
50 
50 
100 
Total 
100 
100 
200 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 17 of 33 
Durham VA and who are eligible to complete the surveys described above. The 
preferred method of data collection will be via telephone. Interviewing Veterans via the 
telephone may decrease barriers to participation (e.g., access, time, transportation).  
Cognitive interviews will enable us to establish Veterans’ comprehension, retrieval 
process, judgments, and the breadth of information underlying the pre-specified 
response categories in the DDS (i.e., not a problem, slight problem, moderate problem, 
somewhat serious problem, serious problem, very serious problem).43,45,50 Cognitive 
interviews will be conducted by a qualitative analyst, will last 45-60 minutes, and will be 
recorded as permitted by the Veteran. If the Veteran does not consent to being 
recorded, the qualitative analyst will take detailed notes throughout the interview. We 
will use a cognitive interview guide to elicit Veterans’ understanding and interpretation of 
the DDS.  Items in the DDS are not ordered by dimension as items for each of the four 
dimensions are randomly dispersed throughout the survey. The qualitative analyst will 
read each item aloud to the Veteran and ask them to state their understanding of the 
meaning of the item and any response to the item. As the focus will be on the 
interpretation of the items rather than how the Veteran would respond to the items, we 
will not ask the Veteran to complete the DDS during the interview.  
We will use verbal probes as a way to elicit Veteran’s understanding and 
interpretation of DD and the DDS.43,45,50 The use of concurrent (e.g., to assess real-time 
thoughts and feelings about DD and the DDS) and retrospective (e.g., to assess 
thoughts of the DDS at interview completion) probes will enable us to obtain specific 
and global feedback on DD and the DDS.45 During the interview, we will encourage the 
Veteran to paraphrase the item, state their opinions of the wording, and any judgements 
about the item using probes such as: “What do you mean by that?”, “I noticed you 
hesitated. Tell me more about that.”, “How did you arrive at that answer?”, and “What do 
you like best/least about this item/survey?”43,45,50 
Analysis Plan. We will begin analysis after the first three interviews and preliminary 
analysis will guide future data collection.51 The analysis process will be guided by the 
analysis steps for cognitive interviews detailed in Knafl et al.44 Each interview will be 
transcribed and identifying information will be removed. Limited demographic 
information (e.g., gender, medication) will be noted within the transcript to facilitate 
consideration of the Veteran’s responses. We will use thematic analysis with the 
software NVivo for this study.52  
A coding team consisting of Dr. Lewinski and the qualitative analyst will code these 
data. They will meet weekly during interviewing, coding, and analysis to identify 
emerging areas, refine the interview guide, and address emergent questions. The 
coding team will independently read all transcripts to become familiar with these data; 
the coding unit for each a priori code will be a response to an item. The coding team will 
independently create examples from the transcripts for the a priori codes, and any 
emergent codes, for 20% of the transcripts, and then meet to discuss codes and coding 
units. We will compare examples and discuss findings until agreement is reached on 
coding definitions and application of the definitions to these data. The coding team will 
repeat the described process until we have total agreement on the first level coding; 
following discussions, the coding team will re-code transcripts using any new codes. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 18 of 33 
The coding team will identify and create higher level codes based upon created and 
agreed upon codes after the completion of first level coding and discussion of 
preliminary findings with the co-mentors. Second level coding will mirror the first level 
coding approach in that the coding team will independently code 20% of the transcripts 
and meet to discuss emerging patterns. To ensure reliability and validity of the coding 
process and the creation of these descriptive summaries, the coding team will meet 
weekly to compare coding and resolve disagreements. All findings will be discussed and 
reviewed with other study team members to ensure reliability and validity.  
Emerging codes. A Veteran may indicate that the DDS does not capture an aspect 
salient to DD or living with T2D. Should this occur, the QA will: (1) use probes to obtain 
further data about this aspect from the Veteran during the interview; (2) discuss these 
emerging topics with Drs. Lewinski and King; and (3) amend the interview guide to 
query subsequent Veterans about the emerging topic(s).  
A priori codes. We will use the coding scheme described in Knafl et al.44 to capture 
challenges to describing DD in the context of the DDS. These codes include: (1) limited 
applicability, responses which indicate the item is not appropriate for Veterans with T2D; 
(2) unclear reference, responses that indicate lack of clarity for the item; (3) unclear 
perspective, responses that indicate lack of clarity about perspective to answer the 
question; and (4) wording/tone, responses that indicate the wording of the question is 
confusing or negative. Additionally, we will use codes to denote: the need for more 
questions, ease of recall of information needed tor response, and ease of fitting 
personal experiences to the measure/response options.  
Analysis Step 1: Data Overview. First, we will analyze responses for each survey 
item in aggregate. For each survey item, we will identify responses that are similar, 
different, or if the item was deemed not relevant. When we identify differences or if the 
item was deemed not relevant, we will examine the responses to determine if these 
differences are related to certain characteristics of the Veteran (e.g., gender, 
medication). 
Analysis Step 2: Summarizing Responses. After noting general differences, we will 
write a descriptive summary of each unique response to each survey item. These 
summaries will enhance our ability to compare and contrast responses to each survey 
item. We will look for items in which there are multiple interpretations or 
misunderstandings, as these indicate a threat to the reliability of the DDS. After we write 
a descriptive summary for all responses for each item, we will use the matrix function in 
NVivo to identify higher level summaries. For each survey item, we will examine 
summaries within identified themes.   
Sampling plan for qualitative interviews.  

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 19 of 33 
 Analysis Step 3: Identifying 
Problems & Modifying the DDS. 
We will examine the summaries 
created in Step 2 to identify where, 
if any, problem exists for each 
survey item. We will generate an 
overall interpretation of DD in the 
context of the DDS, describe the identified problems with specific survey items, and 
then decide the appropriate action (e.g., retain, delete, modify, add) for the survey item 
and/or DDS.  
Semi-structured Interviews—Detailed study design and sampling plan. Audio 
recordings will be transcribed by the DVAHCS staff. When transcribed, names and 
other identifying information will be redacted. Transcripts will be saved in the secured 
VA project drive (:.v06.med.va.gov\Dur\HSRD\TARDIS_Aim1-2). We will use a 
combination of maximal variation and extreme case sampling strategies,53 to interview 
~36 Veterans, or until saturation is reached. The combination of these sampling 
strategies will ensure that we obtain a diverse range of responses. We will balance 
enrollment on HbA1c level (< 9 or ≥ 9) and medication (no insulin, insulin); we will also 
balance enrollment on the Veteran’s Diabetes Distress Scale score14 (low, moderate,  
high). We will use the Diabetes Distress Scale score due to its established and 
confirmed four domains of DD.14 We are balancing enrollment on Diabetes Distress 
Scale score to ensure we obtain descriptions of each level of DD (low, moderate, high). 
Our actions will help us qualitatively describe what, and to what extent, differences exist 
between DD levels. A maximal variation sampling plan is ideal in order to obtain diverse 
perspectives (e.g., HbA1c, medication, Diabetes Distress Scale score) on T2D self-
management.33,53 Including well- and poorly controlled Veterans with low distress will 
provide insight into these Veteran’s self-management strategies and behaviors. When 
we begin to see saturation, we will begin to oversample Veterans based upon certain 
characteristics (e.g., race/ethnicity, gender, age). Extreme case sampling will enable us 
to purposively obtain diverse perspectives in the presentation of DD; we will work during 
data collection to determine when to use extreme case sampling. When appropriate, we 
will use NVivo, a qualitative data analysis software program that facilitates management 
of data analysis across transcripts. 
The preferred method of data collection will be via telephone. Interviewing Veterans 
via the telephone may decrease barriers to participation (e.g., access, time). Semi-
structured interviews will be completed by a Qualitative Analyst. We will develop an 
operating manual for the interviews, review interviewing techniques that encourage 
openness, and review active listening techniques.46,54 Prior to the first interview, we will 
practice interviewing to ensure the qualitative analyst is familiar with the interview guide 
and purpose of the interviews. We will observe the qualitative analyst for the first 3 
interviews to ensure she is using the interview guide correctly, and then we will listen to 
recordings of every fourth interview to ensure that the qualitative analyst is following the 
interview guide. 
HbA1c 
DDS Score18 
 
Low 
Mod. 
High 
Total 
 
No 
Ins. 
Ins. 
No 
Ins. 
Ins. 
No 
Ins. 
Ins. 
 
< 9 
3 
3 
3 
3 
3 
3 
18 
≥ 9 
3 
3 
3 
3 
3 
3 
18 
Total 
12 
12 
12 
36 
Note: No Ins. = No insulin; Ins. = Insulin.  

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 20 of 33 
Analysis Plan. We will begin analysis after the first three interviews and preliminary 
analysis will guide future data collection.51 The analysis process will be iterative, as 
initial analysis will inform interview guide questions.51 A coding team consisting of Dr. 
Lewinski and Ms. Shaprio will code these data. They will meet weekly during 
interviewing, coding, and analysis to identify emerging areas, refine the interview guide, 
and address emergent questions. All transcripts will be de-identified and will not contain 
information about the HbA1c level, medication, or DD score to ensure objectivity during 
coding and analysis. Throughout coding and analysis, Dr. Lewinski will work closely with 
Drs. Tanabe and Barcinas, and findings will be discussed with other mentors to ensure 
reliability and validity of the interpretation. The coding team will use content analysis55 
with the software ATLAS.ti. The analysis plan will occur in two steps. First, the 
transcribed text from the interviews will be coded and analyzed for aggregate themes 
specific to DD and T2D self-management. Then, using matrices, we will identify 
similarities and differences between levels of DD and participant characteristics  
 Analysis Plan: Step 1. First, we will upload the transcripts into ATLAS.ti and then 
begin coding. First level coding will be guided by a priori codes based upon how 
Veterans describe DD and the variables in the guiding framework (Figure 1). Emergent 
codes about DD, T2D self-management, and learning will be identified in relation to 
observations of self-management unique to Veterans. Codes will be examined in the 
context of DD and self-management of T2D to focus the analysis.33 The coding team 
will independently read all transcripts to become familiar with these data, and then 
identify appropriate coding units for each a priori code prior to beginning coding. The 
coding team will independently create examples from the transcripts for the a priori 
codes, and any emergent codes, for 20% of the transcripts, and then meet to discuss 
codes and coding units. We will compare examples and discuss findings until 
agreement is reached on coding definitions and application of the definitions to these 
data. The coding team will repeat the described process until we have total agreement 
on the first level coding; following discussions, the coding team will re-code transcripts 
using any new codes. The coding team will identify and create higher level codes based 
upon created and agreed upon codes after the completion of first level coding and 
discussion of preliminary findings with the co-mentors. Second level coding will mirror 
the first level coding approach in that the coding team will independently code 20% of 
the transcripts and meet to discuss emerging patterns. We anticipate that a 
multidimensional structure will be present in these data, thus no limit will be placed upon 
codes to which a coding unit can be assigned. 
Analysis Plan: Step 2. We will begin Step 2 once we identify the overall themes for 
the entire data corpus. We will use the co-occurrence feature in ATLAS.ti and explore 
the findings in matrices to identify patterns of similarities and differences. To enable the 
analysis in Step 2, we will first label each transcript in ATLAS.ti with the Veteran’s 
HbA1c level, medication, and Diabetes Distress Scale score. Dr. Lewinski has 
previously used the co-occurrence feature to identify points of similarities and 
differences.56 
Validity and reliability. Validity and reliability of findings and the iterative generation of 
codes will be ensured by working closely with Ms. Shaprio and the research team.33 The 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 21 of 33 
coding team will: provide rich descriptions of all codes and themes; discuss coding until 
consensus is reached for each code and theme; triangulate data from quantitative and 
qualitative sources; and present any discrepant information.33 The codebook will 
describe the codes and emerging themes and serve as an audit trail. Regular meetings 
with the study team will ensure validity and reliability, higher level code development, 
and discussion of emerging findings.  
Primary Outcomes.  
• Surveys. The primary goal of the baseline survey is to examine differences in 
Diabetes Distress Scale score by HbA1c level (HbA1c < 9 and HbA1c ≥ 9) and 
by medication regimen (no insulin, insulin). 
• Cognitive interviews. Individual interviews will enable us to identify how Veterans 
describe DD and interpret items in the DDS. 
• Semi-structured interviews. Interviews will provide insight into how the Veteran 
interprets T2D self-management, strategies the Veteran uses in self-
management, learning needs and preferences, including interactions with 
healthcare providers as part of self-management, and the associated challenges 
with self-management, in their sociodemographic, psychosocial, and 
environmental context. 
Privacy, Confidentiality, and Information Security 
1. Lists of Data Reviewed and/or Collected for Screening/Recruitment and 
Conduction of Study:  
The Personal Health Information that will be obtained, used, and/or shared for this study 
includes 
Identifier(s) 
Source(s) of Health Information 
 Names 
 Medical history & physical exam 
information Describe: Blood Pressure, 
HbA1c, lipid labs, RX for lipids, HTN, 
DM  
 All geographic subdivisions smaller than 
a State, including street address, city, 
county, precinct, and zip code. Describe: 
street address, City, Zip, County 
 Photographs, videotapes, 
audiotapes, or digital or other images 
 All elements of dates (except year) for 
dates directly related to an individual, 
including birth date, admission date, 
discharge date, visit or treatment dates, 
etc.; and all ages over 89, Describe: birth 
date, dx dates, admission date, discharge 
date, visit or treatment dates, RX dates, lab 
dates, survey dates 
 Biologic specimens (e.g., blood, 
tissue, urine, saliva). Describe: 
 Telephone numbers 
 Progress notes 
 Fax numbers 
 Diagnostic / Laboratory test results 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 22 of 33 
Identifier(s) 
Source(s) of Health Information 
 Electronic mail addresses 
 Operative reports 
 Social Security Numbers 
 Imaging (x-ray, CT, MRI, etc.) 
 Medical record numbers 
 Discharge summaries 
 Health plan beneficiary numbers 
 Survey / Questionnaire responses 
 Account numbers 
 Billing records 
 Certificate and/or license numbers  
 HIV testing or infection records 
 Vehicle identifiers and serial numbers, 
including license plate numbers  
 Sickle cell anemia information 
 Device identifiers and serial numbers 
 Alcoholism or alcohol use 
information 
 Web Universal Resource Locators 
(URLs) 
 Drug abuse information 
 Internet Protocol (IP) address numbers 
 Mental health (not psychotherapy) 
notes 
 Biometric identifiers, including finger & 
voice prints 
 Psychological test results 
 Full-face photographic images and any 
comparable images 
 Genetic testing 
 Any other unique identifying number, 
characteristic, or code, describe:  
Anonymous/Randomly assigned study ID#  
*Note: This is not the unique code assigned to 
otherwise de-identified health information for re-
identification purposes. 
 Other, describe:        
 
2. Data and/or Specimen Acquisition:   
Data for this study will be collected through (check all that apply): 
  Prospective data and/or specimen collection obtained from participants.  Provide 
description of processes:  We will collect information from patients using telephonic 
survey collection methods. 
  Retrospective data collection and/or specimens obtained from medical chart 
review/data access.  Describe how data will be obtained (e.g., fileman, CDW, etc.):   
We will use the current VA CDW/VINCI resources to identify the necessary participants 
with the requested inclusion/exclusion criteria. Using the real SSNs from that subset, we 
will retrieve current mailing addresses, telephone number, as well as the specific 
medical record data (using approved VA data bases) via the VHA Corporate Data 
Warehouse (CDW) dataset via the VINCI or another secure platform.   
  Retrospective data collection and/or specimens obtained from an IRB-approved 
data and/or specimen repository.  Indicate the repository source including name, VA 
location, and IRB number:       . 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 23 of 33 
Note:  for data and/or specimens obtained from a VA approved data repository, a Data 
Use Agreement (DUA) must be executed prior to obtaining data and/or specimens.  See 
VHA Handbook 1200.12 for further information. 
3. Level of Data:   
The following level(s) of data will be acquired/maintained for this study (check all that 
apply): 
  Identifiable—Data contains direct identifiers.   
  Coded—Data linked to a specific by a code rather than a direct identifier for re-
identification purposes. Only someone possessing the key to the code can link the data 
to a particular participant.    
  De-Identified (all 18 HIPAA identifiers removed  
  Verified Statistically  
 
OR 
  Verified by Absence or Removal of 18 HIPAA identifiers   
  Limited Data Set 
  Other: Describe:        
 
4. Location of Data and/or Specimens, and Data Retention Plan:   
A. Data and/or Specimen Location:  All survey data will be collected and stored on VA 
secure server for the Study RedCap survey located on VINCI.   
Data will be stored electronically in \\ v06.med. va.gov \Dur\HSRD\TARDIS_Aim1-2.  
Data that will be stored electronically include electronic versions of recruitment letters, 
scanned copies of completed surveys hard copy surveys, database with study cohort 
criteria.   
Paper records of data include study team meeting minutes, hard copy of telephone 
screening documents, and surveys, etc., will be stored at Legacy tower, suite 600, 
cubical 632H and office 605.  
  Data will be also be placed at the VA Informatics and Computing Interface (VINCI; 
http://vaww.vinci.med.va.gov/vincicentral/VINCIWorkspace.aspx). The VA Informatics 
and Computing Infrastructure is a partnership between the VA Office of Information 
Technology and the Veterans’ Health Administration Office of Research and 
Development.  Researchers and operations staff can use VINCI to access data and 
statistical analysis tools in a virtual working environment through a certified VHA 
network computer using the VA Intranet or Virtual Private Network (VPN).  
B. Data Retention Plan 
 Research records will be maintained and destroyed according to the National 
Archives and Records Administration, Records Schedule Number:  DAA-0015-2015-

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 24 of 33 
0004.  Records destruction, when authorized, will be accomplished using the then 
current requirements for the secure disposal of paper and electronic records.  Currently, 
destruction of research records (see DAA-0015-2015-0004, section 7.6 “Research 
Investigator Files” for materials included in research records) is scheduled for 6 years 
after the cut-off (the cut-off is the completion of the research project) and may be 
retained longer if required by other federal agencies.  Records will not be destroyed 
without pre-notification to the facility records manager.    
  Other data retention plan, describe:        
5. Data Access and Data Recipients:  
Only members of our DVAHCS research team will have access to identifiers and coded 
data.  All VA research personnel who have access to VHA records are instructed, in 
accordance with VA policy, on the requirements of Federal privacy and information laws 
and regulations, VA regulations and policies, and VHA policy. All study personnel who 
are VA employees working within the VA system have fulfilled all required HIPAA and 
other VA security and privacy policy training requirements and have agreed to follow 
guidelines pertaining to the protection of patient data. All research staff sign VA Rules of 
Behavior, and all study staff are up-to-date with VHA Privacy Policy Training and the VA 
Office of Cyber and Information Security Awareness Training Course. The data security 
and privacy procedures summarized in that course include logging off or locking the 
computer when walking away from it; no sharing of access codes, verify codes or 
passwords; not allowing anyone else to use the computer under one’s password; and 
disposing of sensitive information using VA-approved methods (e.g., shredder bins). 
Access to study data will be removed for all study personnel when they are no longer 
part of the research team.  
 
6. Data and/or Specimen Transportation and/or Transmission for all data and/or 
specimens involved in the study:   
 
I. 
  Data and/or specimens will not be transported or transmitted outside of 
Durham VAMC environment. 
II. 
  Data and/or specimens will be transported BETWEEN sites that are under 
the auspices of the Durham VA Medical Center.   
III. 
   Data and/or specimens will be transmitted to other VA sites using the 
following method(s): 
A. Data 
  Data are de-identified and thus will be sent via unencrypted e-mail or 
unencrypted disk (encryption is optional). 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 25 of 33 
  Data are coded or contain identifiers and thus will be sent < 
  Other, describe:        
B. Specimens 
  Specimens are de-identified and thus will be sent via standard carrier 
(tracking is optional).  
  Specimens are coded or contain identifiers and thus will be sent via VA-
authorized carrier with tracking.   
  Other, describe:        
IV. 
  Data and/or specimens will be transported to non-VA/VHA sites (e.g., 
academic affiliates, laboratories, etc.) using the following method(s): 
A. Data 
  Data are de-identified and thus will be sent via unencrypted e-mail or 
unencrypted CD. 
  Data are coded or contain identifiers and thus will be sent via using VA—
approved carrier with tracking.   
  Data are coded or identified and will be sent via the Safe Access File 
Exchange (SAFE) at https://safe.amrdec.army.mil/safe/.  SAFE is a secure 
method of exchanging files <2GB to and from individuals with a valid .gov, 
.mil, .com, or .edu email address. <insert information including collaborator 
name.> 
  Data are coded or identified and will be uploaded to sponsor website 
using electronic case report form (eCRF). 
 Other, describe:       
B. Specimens 
  Specimens are de-identified and thus will be sent via standard carrier 
(tracking is optional) or will be hand-delivered by research study personnel.  
Specify method of delivery:        
  Specimens are coded and thus will be sent via VA-approved carrier with 
tracking or will be hand-delivered by research study personnel.  Specify 
method of delivery:        
In accordance with the HIPAA and the Privacy Act, for any coded or identifiable data or 
specimens released from the Durham VAMC (with the exception of Limited Data Sets), 
an Accounting of Disclosure (AOD) will be maintained (e.g., in a database or 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 26 of 33 
spreadsheet) that includes the participant’s name, date of the disclosure, description of 
the nature of the Individually Identifiable Information (III) disclosed, purpose of each 
disclosure, and the name and address of the person/agency to whom the disclosure 
was made. 
C.      
 Local DVAMC memorandum “Authorization to Use, Process, Store, or 
Transmit VA Sensitive Information Outside VA Owned or Managed Facilities” has 
been pre-filled out for each study team member who may transport the data 
and/or specimens off-site.  This (these) forms are included with the IRB 
materials.   
D. 
  Containers (e.g., briefcase, bin) are labeled with the following notice (label 
placed on the outside of container) in accordance with VHA Directive 6609: 
NOTICE!!! 
Access to these records is limited to: AUTHORIZED PERSONS ONLY. 
Information may not be disclosed from this file unless permitted by all applicable 
legal authorities, which may include the Privacy Act; 38 U.S.C. §§ 5701, 5705, 
7332; the Health Insurance Portability and Accountability Act; and regulations 
implementing those provisions, at 38 C.F.R. §§ 1.460 – 1.599 and 45 C.F.R. 
Parts 160 and 164. Anyone who discloses information in violation of the above 
provisions may subject to civil and criminal penalties. 
 
V. 
  We will communicate with veterans enrolled as participants in this research 
study through MyHealtheVet.    
 
7. Risk Mitigation Strategies:   
Participant’s identifying information (name and last 4 of social security number) will 
be linked to a study code to be used for all data sets. There will be one key of the last 
4SS#/Name and study code, on the secure computer server. The VA server will only be 
accessible to study personnel with rights to the folder. These data will be entered into 
the database on the secure VA server. Any paper form will be kept in a secure, locked 
file cabinet in a locked research office in the ADAPT HSRD at Legacy Tower, suite 600, 
office 602 or cubical 632H. They will be identified only by a study code. All data will be 
retained in accordance to the Records Control Schedule, the Records Management 
Officer will be contacted for the current policy, prior to any destruction. 
  Data are fully de-identified (stripped of HIPAA 18 and study ID/code) before being 
shared outside of Durham VAMC. 
  Specimens are fully de-identified (stripped of HIPAA 18 and study ID/code before 
being shared outside of Durham VAMC. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 27 of 33 
  Data or specimens are coded and the code is not related to, or derived from, 
information about the individual and that code is not otherwise capable of being 
translated as to the identify the individual. Only someone possessing the key to code 
can link the data to a particular participant.    
  Other, specify:        
8. Suspected Loss of VA Information: 
Should any incident such as theft or loss of data, unauthorized access of sensitive data 
or non-compliance with security controls occur it will be immediately reported according 
to VA policy. All incidents regarding information security/privacy incidents will be 
reported to the ISO and PO within 1 hour of acknowledgement of issue and done so 
using the VHADUR Research Events Report e-mail group 
(VHADURResearchEventReport@va.gov). 
9. Reporting of Results:   
  Reporting of results, such as in scientific papers and presentations, will never 
identify individual subjects.  Data will be presented in aggregate and individual-level 
data will not be published. 
  Other results reporting plan, describe:       
10. Future Use of Data: 
  Data will be retained for future use.  This is described elsewhere in the protocol and 
is noted in the HIPAA authorization. 
 
  Future Use of data is optional (i.e., not required by the research subject). 
  Future Use of data is required for participation in the study. 
  No future use of data is currently planned. 
11. Use of Mail Merge Technology 
  Mail merge programs will be used to generate letters and/or address labels for 
mailings to potential or already enrolled research subjects.  The study team is aware 
that to reduce risk of mail merge related privacy incidents, use of mail merge programs 
requires a 25% accuracy check to verify that (potential) research subject name and 
mailing address are properly “matched”.  If discrepancies are found, a 100% accuracy 
check is required before letters may be mailed.   
12. Use of Non-Standard Software 
 I do NOT intend to use any new specialized software (i.e. Software that’s not already 
approved OR installed) in this study. 
 
 I intend to use specialized software that has not already been installed and it has 
been approved for use by the VA Technical Reference Model (TRM) Group.  

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 28 of 33 
(Note: All new software must be approved by TRM before it can be installed on VA 
systems.) 
 
 I intend to use previously installed software on my VA computer.  
 
13. Use of Cloud Computing Services 
 Cloud computing services will NOT be used in this study. 
 
 Cloud computing services WILL be used in this study as described below and have 
been approved nationally by the VA Chief Information Officer (CIO).  (Note: ONLY cloud 
computing services that have been approved nationally may be used.) 
 
References 
Provide references, if applicable.   
 
 
1. 
Veterans Health Administration. VA Research on Diabetes. In: Atlanta, GA. : U.S. 
Department of Veterans Affairs, Veterans Health Administration, Office of 
Research and Development; 2016: 
https://www.research.va.gov/pubs/docs/va_factsheets/Diabetes.pdf. Accessed 
1/3/2018. 
2. 
American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. 
Diabetes Care. 2018. 
3. 
Funnell M, Anderson RM. Working toward the next generation of diabetes self-
management education. Am J Prev Med. 2002;22(4 Suppl):3-5. 
4. 
Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM. Self-management 
education for adults with type 2 diabetes: a meta-analysis of the effect on 
glycemic control. Diabetes Care. 2002;25(7):1159-1171. 
5. 
The Diabetes Control and Complications Trial Research Group. The Effect of 
Intensive Treatment of Diabetes on the Development and Progression of Long-
Term Complications in Insulin-Dependent Diabetes Mellitus. N Engl J Med. 
1993;329(14):977-986. 
6. 
Ahola AJ, Groop PH. Barriers to self-management of diabetes. Diabet Med. 
2013;30(4):413-420. 
7. 
Laranjo L, Neves AL, Costa A, Ribeiro RT, Couto L, Sa AB. Facilitators, barriers 
and expectations in the self-management of type 2 diabetes--a qualitative study 
from Portugal. The European journal of general practice. 2015;21(2):103-110. 
8. 
Gariepy G, Smith KJ, Schmitz N. Diabetes distress and neighborhood 
characteristics in people with type 2 diabetes. J Psychosom Res. 
2013;75(2):147-152. 
9. 
Tanenbaum ML, Kane NS, Kenowitz J, Gonzalez JS. Diabetes distress from the 
patient's perspective: Qualitative themes and treatment regimen differences 
among adults with type 2 diabetes. J Diabetes Complications. 2016;30(6):1060-
1068. 
10. 
Fisher L, Hessler D, Glasgow RE, et al. REDEEM: a pragmatic trial to reduce 
diabetes distress. Diabetes Care. 2013;36(9):2551-2558. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 29 of 33 
11. 
Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of 
comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 
2001;24(6):1069-1078. 
12. 
Aronson B, Palombi L, Walls M. Rates and consequences of posttraumatic 
distress among American Indian adults with type 2 diabetes. J Behav Med. 
2016;39(4):694-703. 
13. 
Aikens JE, Zivin K, Trivedi R, Piette JD. Diabetes self-management support 
using mHealth and enhanced informal caregiving. J Diabetes Complications. 
2014;28(2):171-176. 
14. 
Fisher L, Hessler DM, Polonsky WH, Mullan J. When is diabetes distress 
clinically meaningful?: establishing cut points for the Diabetes Distress Scale. 
Diabetes Care. 2012;35(2):259-264. 
15. 
Adriaanse MC, Pouwer F, Dekker JM, et al. Diabetes-related symptom distress in 
association with glucose metabolism and comorbidity: the Hoorn Study. Diabetes 
Care. 2008;31(12):2268-2270. 
16. 
Welch GW, Jacobson AM, Polonsky WH. The Problem Areas in Diabetes Scale. 
An evaluation of its clinical utility. Diabetes Care. 1997;20(5):760-766. 
17. 
Schmitt A, Reimer A, Kulzer B, Haak T, Ehrmann D, Hermanns N. How to assess 
diabetes distress: comparison of the Problem Areas in Diabetes Scale (PAID) 
and the Diabetes Distress Scale (DDS). Diabet Med. 2016;33(6):835-843. 
18. 
Fisher L, Glasgow RE, Strycker LA. The relationship between diabetes distress 
and clinical depression with glycemic control among patients with type 2 
diabetes. Diabetes Care. 2010;33(5):1034-1036. 
19. 
Baek RN, Tanenbaum ML, Gonzalez JS. Diabetes burden and diabetes distress: 
the buffering effect of social support. Ann Behav Med. 2014;48(2):145-155. 
20. 
Berry E, Lockhart S, Davies M, Lindsay JR, Dempster M. Diabetes distress: 
understanding the hidden struggles of living with diabetes & exploring 
intervention strategies. Postgrad Med J. 2015;91(1075):278-283. 
21. 
Dennick K, Sturt J, Speight J. What is diabetes distress and how can we 
measure it? A narrative review and conceptual model. J Diabetes Complications. 
2017;31(5):898-911. 
22. 
Chesla CA, Chun KM, Kwan CM, et al. Testing the efficacy of culturally adapted 
coping skills training for Chinese American immigrants with type 2 diabetes using 
community-based participatory research. Res Nurs Health. 2013;36(4):359-372. 
23. 
Carolan M, Holman J, Ferrari M. Experiences of diabetes self‐management: A 
focus group study among australians with type 2 diabetes. J Clin Nurs. 
2015;24(7-8):1011-1023. 
24. 
Fisher L, Mullan JT, Skaff MM, Glasgow RE, Arean P, Hessler D. Predicting 
diabetes distress in patients with Type 2 diabetes: a longitudinal study. Diabet 
Med. 2009;26(6):622-627. 
25. 
Faber-Wildeboer AT, van Os-Medendorp H, Kooy A, Sol BGM. Prevalence and 
risk factors of depression and diabetes-related emotional distress in patients with 
type 2 diabetes: A cross-sectional study. Journal of Nursing Education & 
Practice. 2013;3(6):61-69. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 30 of 33 
26. 
Lipscombe C, Smith KJ, Gariepy G, Schmitz N. Gender differences in the 
association between lifestyle behaviors and diabetes distress in a community 
sample of adults with type 2 diabetes. Journal of diabetes. 2016;8(2):269-278. 
27. 
Perrin NE, Davies MJ, Robertson N, Snoek FJ, Khunti K. The prevalence of 
diabetes-specific emotional distress in people with Type 2 diabetes: a systematic 
review and meta-analysis. Diabet Med. 2017. 
28. 
Sturt J, Dennick K, Hessler D, Hunter BM, Oliver J, Fisher L. Effective 
interventions for reducing diabetes distress: systematic review and meta-
analysis. International Diabetes Nursing. 2015;12(2):40-55. 
29. 
Dennick K, Sturt J, Hessler D, et al. High rates of elevated diabetes distress in 
research populations: A systematic review and meta-analysis. International 
Diabetes Nursing. 2015;12(3):93-107. 
30. 
Chew B-H, Vos RC, Pouwer F, Rutten GEHM. The associations between 
diabetes distress and self-efficacy, medication adherence, self-care activities and 
disease control depend on the way diabetes distress is measured: Comparing 
the DDS-17, DDS-2 and the PAID-5. Diabetes Res Clin Pract. 2018;142:74-84. 
31. 
Carper MM, Traeger L, Gonzalez JS, Wexler DJ, Psaros C, Safren SA. The 
differential associations of depression and diabetes distress with quality of life 
domains in type 2 diabetes. J Behav Med. 2014;37(3):501-510. 
32. 
Knowles MS. The modern practice of adult education (revised and updated). 
New York: Cambridge. 1980. 
33. 
Creswell J, Plano Clark V. Designing & Conducting Mixed Methods Research. 2 
ed. Thousand Oaks, CA: Sage; 2011. 
34. 
Polonsky WH, Fisher L, Earles J, et al. Assessing psychosocial distress in 
diabetes: development of the diabetes distress scale. Diabetes Care. 
2005;28(3):626-631. 
35. 
Naegeli AN, Stump TE, Hayes RP. A psychometric evaluation of the Diabetes 
Symptom Checklist-Revised (DSC-R) cognitive distress, fatigue, hyperglycemia, 
and hypoglycemia subscales in patients with type 1 and type 2 diabetes. 
Diabetes Metab Syndr Obes. 2010;3:27-30. 
36. 
Glasgow RE, Toobert DJ, Barrera M, Jr., Strycker LA. The Chronic Illness 
Resources Survey: cross-validation and sensitivity to intervention. Health Educ 
Res. 2005;20(4):402-409. 
37. 
Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity 
of a two-item depression screener. Med Care. 2003;41(11):1284-1292. 
38. 
Prins A, Bovin MJ, Smolenski DJ, et al. The Primary Care PTSD Screen for 
DSM-5 (PC-PTSD-5): Development & Eval. Within a Veteran Primary Care 
Sample. J Gen Intern Med. 2016;31(10):1206-1211. 
39. 
Billioux A, Verlander K, Anthony S, Alley D. Standardized Screening for Health-
Related Social Needs in Clinical Settings. 2017. 
40. 
Anderson RM, Fitzgerald JT, Gruppen LD, Funnell MM, Oh MS. The diabetes 
empowerment scale-short form (DES-SF). Diabetes Care. 2003;26(5):1641-
1642. 
41. 
Kerfoot BP, Gagnon DR, McMahon GT, Orlander JD, Kurgansky KE, Conlin PR. 
A Team-Based Online Game Improves Blood Glucose Control in Veterans With 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 31 of 33 
Type 2 Diabetes: A Randomized Controlled Trial. Diabetes Care. 
2017;40(9):1218-1225. 
42. 
Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care 
activities measure: results from 7 studies and a revised scale. Diabetes Care. 
2000;23(7):943-950. 
43. 
Willis GB, Artino AR, Jr. What Do Our Respondents Think We're Asking? Using 
Cognitive Interviewing to Improve Medical Education Surveys. J Grad Med Educ. 
2013;5(3):353-356. 
44. 
Knafl K, Deatrick J, Gallo A, et al. The analysis and interpretation of cognitive 
interviews for instrument development. Res Nurs Health. 2007;30(2):224-234. 
45. 
Drennan J. Cognitive interviewing: verbal data in the design and pretesting of 
questionnaires. J Adv Nurs. 2003;42(1):57-63. 
46. 
Drabble L, Trocki KF, Salcedo B, Walker PC, Korcha RA. Conducting qualitative 
interviews by telephone: Lessons learned from a study of alcohol use among 
sexual minority and heterosexual women. Qualitative social work : QSW : 
research and practice. 2016;15(1):118-133. 
47. 
Department of Veterans Affairs, Department of Defense. VA/DoD Clinical 
Practice Guidelines for The Management of Type 2 Diabetes Mellitus in Primary 
Care: Pocket Card. In. Vol 5.02017. 
48. 
West C, McDowell J. The distress experienced by people with type 2 diabetes. Br 
J Community Nurs. 2002;7(12):606-613. 
49. 
Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects 
of Confounding in Observational Studies. Multivariate behavioral research. 
2011;46(3):399-424. 
50. 
Willis GB. Cognitive interviewing: A tool for improving questionnaire design. Sage 
Publications; 2004. 
51. 
Sandelowski M. Qualitative analysis: what it is and how to begin. Res Nurs 
Health. 1995;18(4):371-375. 
52. 
Guest G, MacQueen KM, Namey EE. Applied thematic analysis. Los Angeles : 
Sage Publications, c2012.; 2012. 
53. 
Onwuegbuzie AJ, Collins KM. A typology of mixed methods sampling designs in 
social science research. The qualitative report. 2007;12(2):281-316. 
54. 
Musselwhite K, Cuff L, McGregor L, King KM. The telephone interview is an 
effective method of data collection in clinical nursing research: A discussion 
paper. Int J Nurs Stud. 2007;44(6):1064-1070. 
55. 
Krippendorff K. Content Analysis An Intro. to its Methodology. Thousand Oaks, 
Calif.: Sage; 2004. 
56. 
Lewinski AA, Anderson RA, Vorderstrasse AA, Fisher EB, Pan W, Johnson CM. 
Type 2 Diabetes Education and Support in a Virtual Environment: A Secondary 
Analysis of Synchronously Exchanged Social Interaction and Support. J Med 
Internet Res. 2018;20(2):e61. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 32 of 33 
Appendix A:  Expedited Review of Research 
 
The categories of research that may be reviewed by the IRB through the expedited 
review procedure include research activities that present no more than minimal risk to 
human subjects AND involve procedures listed in one or more of the specific categories 
listed below. 
 
The expedited review procedure is not to be used when identification of the subjects or 
their responses would reasonably place them at risk of criminal or civil liability; be 
damaging to the subjects’ financial standing, employability, insurability, or reputation; or 
be stigmatizing, unless reasonable and appropriate protections are implemented so that 
risks related to invasion of privacy and breach of confidentiality are no greater than 
minimal.   The IRB must apply the standard requirements for informed consent (or its 
waiver, alteration, or exception) to all studies that undergo expedited review. 
 
EXPEDITE CATEGORIES  
1-Drugs and Devices:  One of the following must be met: 
 (1) The research is on drugs for which an IND application is not required.   
 (2) The research is on medical devices for which an investigational device exemption (IDE) 
application is not required; or the medical device is cleared or approved for marketing, and the 
medical device is being used in accordance with its cleared or approved labeling. 
 
2-Blood Samples:  Collected by finger / heel / ear stick or venipuncture: 
 (1) From healthy, nonpregnant adults who weigh at least 110 pounds.  For these subjects, the 
amounts drawn may not exceed 550 milliliters (ml) in an 8-week period, and collection may not 
occur more frequently than two times per week; or 
 (2) From other adults and children, considering the age, weight, and health of the subjects, the 
collection procedure, the amount of blood to be collected, and the frequency with which it will 
be collected. For these subjects, the amount drawn may not exceed the lesser of 50 ml or 3 ml 
per kilogram (kg) in an 8-week period, and collection may not occur more frequently than two 
times per week.   
 
3-Noninvasive Collection of Biological Specimens:  Collected prospectively for research 
purposes by noninvasive means: 
(1) Hair and nail clippings in a non-disfiguring manner.  
(2) Deciduous teeth at time of exfoliation or if routine patient care indicates a need for 
extraction.  
(3) Permanent teeth if routine patient care indicates a need for extraction. 
(4) Excreta and external secretions (including sweat). 
(5) Uncannulated saliva collected either in an unstimulated fashion or stimulated by chewing 
gumbase or wax or by applying a dilute citric solution to the tongue. 
(6) Placenta removed at delivery. 
(7) Amniotic fluid obtained at the time of rupture of the membrane prior to, or during, labor. 
(8) Supra- and subgingival dental plaque and calculus, provided the collection procedure is not 
more invasive than routine prophylactic scaling of the teeth and the process is accomplished in 
accordance with accepted prophylactic techniques.  
(9) Mucosal and skin cells collected by buccal scraping or swab, skin swab, or mouth 
washings. 
(10) Sputum collected after saline mist nebulization. 

Protocol Number:3       Version Date:09/28/2020          DVAHCS Protocol Template—V. 02/15/19    Page 33 of 33 
EXPEDITE CATEGORIES  
4-Noninvasive Collection of Data:  Data must be collected through noninvasive procedures 
(not involving general anesthesia or sedation) routinely employed in clinical practice, excluding 
procedures involving x-rays or microwaves.   
(1) Physical sensors that are applied either to the surface of the body or at a distance and do 
not involve input of significant amounts of energy into the subject or an invasion of the 
subject's privacy.  
(2) Weighing the subject. 
(3) Testing sensory acuity.  
(4) Magnetic resonance imaging (MRI).  
(5) Electrocardiography, electroencephalography, thermography, detection of naturally 
occurring radioactivity, electroretinography, ultrasound, diagnostic infrared imaging, Doppler 
blood flow, and echocardiography. 
(6) Moderate exercise, muscular strength testing, body composition assessment, and flexibility 
testing, where appropriate, given the age, weight, and health of the individual. 
 
5-Collected Material: Research involves: 
(1) Materials (data, documents, records, or specimens) that have been collected for any 
purpose, including previous research; or 
(2) Materials (data, documents, records, or specimens) that will be collected solely for 
nonresearch purposes (such as medical treatment or diagnosis).  
 
6-Collection of Data From Voice, Video, or Photographs:  Research involves collection of 
data from voice, video, or photographs. 
 
7-Group Characteristics, Surveys, Interviews, and Quality Assurance:  Research must be 
on individual or group characteristics or behavior (including, but not limited to:  research on 
perception, cognition, motivation, identity, language, communication, cultural beliefs or 
practices, and social behavior), or will employ survey, interview, oral history, focus group, 
program evaluation, human factors evaluation, or quality assurance methodologies.  NOTE:  
Some research in this category may be exempt from the VA regulations for the protection of 
human subjects (38 CFR 16.101(b)(2) and (b)(3)).  This listing refers only to research that is 
not exempt.