Evaluation of a Complex Intervention for Young Adults With Diabetes: The Resilient, Empowered, Active Living-Telehealth (REAL-T) Study
Phase: NA
Conditions: Diabetes Mellitus, Type 1
Sponsor: University of Southern California

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Study Title: 
Evaluation of an Intervention for Young Adults With Diabetes: Resilient, Empowered, 
Active Living-Telehealth (REAL-T) 
NCT Number: NCT04023487 
 
Version Date: 06/08/2022 
 
 

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RESEARCH STRATEGY 
Scientific Premise and Overall Impact. Young adults (YAs) with type 1 diabetes (T1D), in particular those 
from low-SES and racial/ethnic minority backgrounds, are widely recognized as one of the most vulnerable and 
difficult-to-reach populations with diabetes. In a previous mid-sized RCT, our study team obtained evidence for 
the efficacy of Resilient, Empowered, Active Living (REAL), a community-based intervention addressing self-
management and psychosocial well-being in YAs with T1D.1, 2 In this study, the REAL intervention improved 
hemoglobin A1c (p=0.01; clinically meaningful change of -0.81% in YAs with T1D) and diabetes-related quality 
of life (DQoL; p=0.04) among low-SES, primarily minority YAs with diabetes (N=81). In a subsequent proof-of-
concept study, we demonstrated that REAL is feasible and acceptable to deliver via telehealth (REAL-T), 
which for several reasons (see below) provides an improved method of intervention delivery for YAs with T1D. 
The currently proposed R01 builds upon our prior work by evaluating, on a larger scale, the efficacy, long-term 
effects, mediating mechanisms, and cost implications of REAL-T. In doing so, we lay the groundwork to shift 
the paradigm of diabetes care through an innovative, scalable intervention which has the potential to improve 
health and well-being among a highly vulnerable population whose needs have not been adequately 
addressed by traditional self-management interventions. 
Summary of Scientific Premise and Significance. Diabetes exacts enormous human and economic costs, 
and the need for effective interventions to combat its negative effects on health and well-being is especially 
acute among YAs and disadvantaged populations.73 Further, with the cost of healthcare escalating, scalability 
and cost efficiency should be primary considerations in the development and adoption of innovative 
interventions. Therefore, this project will fill a critical gap by evaluating a telehealth-adapted self-management 
intervention with strong preliminary evidence demonstrating its efficacy among YAs with T1D, and which may 
potentially be extended to other populations including YAs with T2D and other age groups with T1D or T2D.    
Conceptual Model. The conceptual 
model underlying REAL-T is outlined in 
Figure 1. This model was informed by a 
process evaluation of REAL RCT data 
(including statistical analysis of process 
variables, therapist treatment notes, and 
participant and therapist interviews) as 
well as by a review of existing literature.83, 
96-99 As suggested by the model, we 
expect that improvements in glycemic 
control (A1C, time in range) will be 
mediated primarily through increased 
performance of health behaviors (SMBG, 
insulin adherence), while improvements 
in psychosocial well-being (DQoL, 
depressive symptoms) will primarily be 
mediated by improvements in self-
efficacy. Furthermore, we anticipate that changes in health behaviors will be mediated by changes in habit 
strength and self-efficacy. In addition to examining mediating pathways of intervention effects, we will examine 
whether intervention effects are moderated by key demographic or clinical variables including age, gender, 
ethnicity, SES, treatment regimen (multiple daily injections vs. pump; CGM yes/no), or duration of diagnosis. 
REAL-T Intervention. Overview. The REAL-T intervention applies theoretical principles and therapeutic 
strategies drawn from Lifestyle Redesign,96 habit change theory,83 patient empowerment,98 the transtheoretical 
model,99 and motivational interviewing97 to address concrete self-care challenges encountered by YAs with 
diabetes.3 Lifestyle Redesign is an intervention framework which applies principles of OT to the prevention and 
management of chronic conditions, and has been shown to cost-effectively improve physical and mental health 
outcomes among diverse populations and age groups.72, 100-104 The intervention is client-centered and 
individually tailored, with treatment goals and activities developed collaboratively by the client and therapist. 
REAL-T emphasizes the integration of self-care within sustainable habits and routines through a client-directed 
process of activity analysis and evidence-based strategies to promote habit formation and maintenance, 
including chaining, repetition, creating environmental cues, and linking activities to rewards.83 To meet the 
Figure 1. Conceptual Model of REAL-T Intervention 
↑ Glycemic control 
(A1c; CGM-derived 
measures) 
↑ Self-care behaviors 
(SMBG, insulin 
adherence) 
↑ Self efficacy 
↑ Psychosocial 
well-being (DQoL, 
depressive sx) 
Intervention 
Mediating processes 
Health outcomes 
• Analyzing habits 
and routines 
• Individual 
tailoring 
• Client-directed 
goal setting 
• Motivational 
interviewing 
• Delivery in 
everyday context 
↑ Habit strength 
(SMBG, insulin 
adherence) 
Potential moderators: age, gender, ethnicity, SES, tx regimen, duration of diagnosis 

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needs of clients at varying stages of readiness to change, REAL-T uses communication strategies drawn from 
motivational interviewing to address ambivalence, and tailors treatment activities to match the client’s 
readiness to change. This process supports the development of self-efficacy by matching activities to clients’ 
interests, motivation, and skills, thereby facilitating experiences of mastery in diabetes self-care. 
REAL-T Intervention Process. REAL-T participants will receive approximately 12 hours of REAL-T over 6 
months, delivered by a licensed occupational therapist (OTR/L) with a minimum of 12 continuing education 
hours in diabetes education and 12 hours in motivational interviewing training within the past 2 years, and 20 
hours of training in the REAL-T intervention manual. An endocrinologist and a psychologist will be available for 
as-needed consultation with the OTR/L to address emergent medical and mental health issues that fall outside 
of the OT scope of practice. The first intervention session takes place in-person at a location of the client’s 
choosing (client’s home, local community setting, or the OTFP outpatient clinic). If an in-person meeting is not 
feasible, the initial evaluation will take place via telehealth (videoconferencing). Subsequent sessions will take 
place using a HIPAA-compliant telehealth platform, which clients can access through both a web browser and 
smartphone app. Clients who do not have a computer and who are within driving distance of the study office 
will be loaned a study-purchased laptop for the duration of their intervention period, and clients who have a 
computer without a webcam will be given a webcam to facilitate videoconferencing.  
Intervention Fidelity will be documented through three strategies that were employed successfully in our initial 
RCT. First, therapists will document their adherence to the intervention protocol (e.g., timing and duration of 
sessions, use of manualized intervention content) in all treatment notes. Second, approximately 10% of 
sessions, chosen at random, will be observed and/or recorded, with participants’ permission, by an investigator 
or second intervener trained in the intervention, who will complete a fidelity checklist and provide feedback to 
the treating therapist. Finally, weekly meetings will be held in which the Clinical Director and treating therapists 
will discuss client progress and conduct refresher training. 
Communication with Providers. For several pragmatic reasons, REAL-T will not be integrated within a 
particular clinic or health system, but rather will be offered to any YA meeting eligibility criteria. Thus, we will 
draw on several strategies to facilitate open communication with providers. First, we will request clients’ 
medical records prior to initiating OT services, to gain an accurate overview of their medical histories and 
current diabetes treatment regimens. Second, we will initiate several points of informational outflow with clients’ 
providers. These will include (a) notice of the patient’s enrollment in REAL-T; (b) an initial OT evaluation and 
plan of care; (c) summaries of quarterly lab and psychosocial survey results; (d) notification of significant 
medical events; and (e) a discharge summary upon the patient’s completion of REAL-T. Clients who do not 
have a provider at the time of enrollment will be referred by the research team to one of the study’s partnering 
clinical sites or another provider in their area.  
Overview of Study Design. Our primary aim is to analyze the 
efficacy of the 6-month REAL-T intervention in a large-scale 
two-arm RCT. To achieve this aim, N=210 YAs with T1D will be 
randomized evenly to two conditions: REAL-T or usual care 
control. The primary outcome is A1c, with key secondary 
outcomes pertaining to psychosocial well-being (depressive 
symptoms, diabetes distress, and DQoL). In addition, we will 
examine the effects of REAL-T after 3- and 6-month no-
treatment follow-up periods. Furthermore, we will analyze 
mediating mechanisms of the intervention’s effects on 
outcomes through structural equation modeling, and conduct 
exploratory analyses to evaluate the cost-effectiveness, cost 
impact, and change in QALYs associated with the REAL-T 
intervention relative to usual care. Quarterly data collection 
visits, performed over a 12-month period, will enable 
longitudinal modeling of changes in glycemic control, 
psychosocial well-being, and other primary and secondary 
endpoints. In addition, blinded CGM data collected at baseline, 
6 months (immediately post-intervention), and 12 months (long-
term follow-up) will facilitate a more robust analysis of the 
impact of the REAL-T intervention on glycemic control, through 
Figure 2. Participant Flow Diagram 

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an examination of changes in glycemic variability, percent time in range, percent time in hypoglycemia, and 
percent time in hyperglycemia. Figure 2 provides an overview of data collection throughout the study period.  
Participants. Eligibility criteria, as outlined in Table 3, were chosen to ensure a medically stable pool of 
participants and maximize the potential of therapeutic benefit. In addition, the intervention is most appropriate 
for those with demonstrated diabetes care challenges and risk of developing complications, as evidenced by 
an elevated A1c level. Because of key differences in the pathophysiology and management of T1D and T2D, 
we have elected to restrict participation to YAs with T1D only. 
To verify eligibility, we need to confirm A1C. Estimated A1C 
will be used for initial enrollment, and verified through A1C 
test results received from our laboratory. Estimated A1C will 
be identified through: (1) recent A1C from participant’s 
medical records (directly from EMR or self-report); (2) GMI 
(estimated A1c) from participant’s CGM software; or (3) 
participant self-reports that A1C has consistently been >7.5%. 
• 
If the A1C value in the participant’s electronic medical 
record (EMR) is 7.5% or higher and within the last two 
weeks, we will proceed with the informed consent 
process and use the EMR A1C as the baseline value. 
• 
If the estimated A1C value (defined above) is 7.5% or 
higher, we will proceed with the informed consent 
process and test their A1C. If the study A1C value is 
less than 7.5%, they will not be eligible to participate in 
the study, but they will be compensated for their time 
with a $50 gift card. If the study A1C value is greater 
than or equal to 7.5%, they will be eligible to 
participate in the study and we will proceed with the 
remainder of the baseline testing session.    
• 
If there is no estimated A1C value available, we will 
provide them with the screening consent and proceed 
with the A1C screening. If we are unable to test A1C 
in person, we will provide the participant with a mail-in kit where they will prick their finger and mail the 
kit to a laboratory. Based on the A1C test results we receive from the laboratory, if the study A1C value 
is 7.5% or higher, we will proceed with the informed consent process and use the study A1C as the 
baseline value. If the study A1C value is less than 7.5%, the participant is not eligible to enroll in the 
study and will not be compensated. 
• 
In the event that in-person A1c testing is not feasible, A1c mailing testing kits will also be made 
available to participants. 
• 
In the event that in-person recruiting and consenting of participants is not feasible, we will conduct 
recruitment activities virtually through phone calls or videoconferencing, and will send consent forms 
electronically to participants using DocuSign or a REDCap eConsent form to review and complete.  
Recruitment, Enrollment, and Retention are outlined in brief below. During a 24-month recruitment period, 
we will enroll an average of 10 participants per month to attain our targeted sample size of 210. We will utilize 
four recruitment strategies: social media advertising, in-person invitations at partnering clinics, mass mailings 
to patients who are treated at our partnering clinics as well as outreach via convenience sampling and referrals 
from community clinicians, and Keck data request. A website will complement recruitment efforts by providing 
study information to prospective participants and allowing them to share their contact information for follow-up 
by study personnel. Table 4 illustrates our site-specific recruitment estimates for REAL-T, as derived from 
available information from participating clinics and preliminary data from the REAL RCT. Overall, our estimates 
indicate that we will have access to a pool of 1256 YAs with T1D who meet study eligibility criteria. Based on 
our past recruitment rates, greater than one-half of YAs in this pool are likely to enroll, which would enable us 
to easily meet the study recruitment goal of 210 individuals.  If for some reason we are unable to achieve this 
expectation, we will have access to a variety of additional clinical sites, and/or can make increased use of 
social media outreach, as a means of obtaining the stipulated sample size.  
Table 3. Study Eligibility Criteria 
Inclusion 
Exclusion  
• T1D for ≥12 months  
• A1c ≥7.5% at time of study enrollment 
• Age 18-30 yrs. at time of study 
enrollment 
• English or Spanish speaking 
• Resides in a state where our OT 
clinicians are licensed where the 
participant has access to a local 
healthcare provider in the event of 
emergency and can complete data 
collection in person (when permitted), 
or via mailings (when required during 
period of social distancing or due to 
distance from study site), or via 
contactless data collection in the 
community 
• Participant has access to a laptop or 
desktop computer, either their own or 
loaned by the study (if geographically 
feasible and permitted given COVID-19 
social distancing distractions) 
• Willing to participate in 6-month 
intervention 
• Currently 
pregnant or 
planning to 
become 
pregnant 
within the 
next 12 
months 
• Previously 
received 
REAL 
intervention 
• Cognitive 
impairment 
or severe 
disability 
limiting life 
expectancy 

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Strategies to Enhance Retention. To maximize retention, consistent with the successful strategies used in our 
previous research and as reflected by research on retention of YAs in longitudinal studies,109 we will: (a) 
employ consistent study staff with flexible working hours and strong interpersonal skills; (b) maintain regular 
points of contact throughout the intervention and follow-up period (using websites, social media presence, 
phone numbers, and email addresses); (c) collect multiple forms of contact information for each participant and 
update this information at each data collection point; (d) provide participants with choices as appropriate (e.g., 
completing surveys on paper or electronically); (e) track participants’ whereabouts through the Postal Service 
(Forwarding Address requests), web search engines, and medical record numbers; (f) offer adequate stipends 
to show our appreciation for participants’ time and effort in completing study activities; and (g) convey our 
respect and gratitude for participants at each point of contact. 
Randomization. Participants will be computer randomized using random block sizes. The statistician will 
securely maintain the randomization list on her network drive and upload to REDCap to automatically assign 
participants to a treatment condition after they complete baseline testing. 
Intervention. The REAL-T intervention will be delivered as outlined above. Participants in both the REAL-T 
intervention and usual care control condition will continue to have access to routine diabetes care from the 
provider of their choosing, and their care will not be disrupted in any way due to their study participation.  
Measures and Data Collection. A trained research assistant, blind to condition assignment, will administer 
the assessments outlined in Table 5. All self-report measures have been validated in this age group.  All 
diabetes-related measures are appropriate for use in T1D. We will assess A1c with a CLIA-waived point-of-
care device (DCA Vantage) which collects capillary blood using a standard finger-prick procedure. If traditional 
in-person testing for A1c is not feasible, we will offer one of the following sample collection methods: (1) an in-
person appointment on campus or in the community using socially-distanced methods such as contactless 
transfer of supplies and demonstration of sample collection from a safe distance; (b) contactless drop off and 
pick up of supplies in the community, or (c) mailing participants provide participants with a mail-in A1c kit. We 
will collect CGM data using Abbot’s Freestyle Libre Pro blinded CGM, which will be placed by the research 
team at the assessment session and worn by participants on their upper arm for 14 days. If the research team 
is unable to apply the CGM in-person through traditional means, we will offer one of the following application 
methods: (1) an in-person appointment on campus or in the community using socially-distanced methods such 
as contactless transfer of supplies and demonstration of self-application from a safe distance; (b) contactless 
drop off and pick up of supplies in the community, or (c) mailing participants the CGM application kit with 
instructions along with a prepaid postage box to be mailed back. After completing the CGM data collection 
period, participants will remove the device and mail it to the study site in a postage-paid envelope for data 
download by the research team. The Libre Pro CGM was chosen because it requires no calibration, 
manipulation, or restriction of activities on the part of the participant, and does not interfere with a patient’s use 
of a personal CGM or closed loop CGM-pump system. As noted in Figure 2, we will conduct the full 
assessment battery at baseline, 6 months (immediately after the intervention), and 12 months (long-term 
follow-up), and a slightly abbreviated assessment battery at 3 months (midway through the intervention), and 9 
months. The 3-month and 9-month assessments serve several critical functions. These assessments will be 
conducted in-person through the REDCap mobile app on the study’s iPads, via surveys through REDCap’s 
secured electronic system, or mailed out to the participants’ homes for completion and mailed back in prepaid 
postage envelope. If participants prefer, they can also complete surveys over the phone with a trained 
research assistant. First, collecting data on key outcomes midway through the intervention will contribute to our 
understanding of how changes unfold over time, and in relation to each other, during the intervention, 
facilitating future refinements to intervention dose and timing. Second, the resulting information will permit 
longitudinal modeling of changes in A1c and other key variables at quarterly intervals over the full study period. 
Finally, quarterly assessments will provide additional contact with participants in the usual care condition, 
increasing retention. As the standard of care for diabetes dictates quarterly assessment of A1c, self-care (e.g., 
meter downloads), and psychosocial well-being (e.g., depression screening), this measurement strategy 
reflects typical clinical care.110 Monthly surveys sent through a secured electronic system will be conducted to 
gather healthcare utilization data, which will facilitate more accurate recall of healthcare utilization, to 
supplement data from medical chart reviews. 

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Data Management and Analysis. The study’s analytic plan is outlined in brief in this section; for further 
details, please refer to the “Statistical Design and Power” attachment in the PHS Human Subjects and Clinical 
Trials Information section. Data Management: Data will be uploaded and stored in a REDCap database 
management system.111 REDCap is a secure, web-based application designed to support data capture for 
research studies, providing an intuitive interface for validated data entry. It also includes audit trails for tracking 
data manipulation and export procedures; automated export procedures for seamless data downloads to 
common statistical packages; and procedures for importing data from external sources. The REDCap website 
is password-protected and restricted to authorized users (See Data and Safety Monitoring Plan). Research 
assistants will enter and reconcile all data, with further data quality checks performed by the biostatistician. 
Prior to conducting hypothesis tests, baseline participant characteristics will be described using frequency 
distributions, histograms, and summary statistics. Sparse data may be recoded to dichotomous or ordinal data. 
Power and Sample Size: The 
study is powered for an 
intention-to-treat analysis of 
our primary outcome, change 
in A1c over the intervention 
period (including 3 and 6-
month data). Other analyses 
are designated as secondary, 
to control the type 1 error rate in identifying statistically significant changes in the primary outcome. Power 
analyses were conducted using proc glmpower in SAS using a 2-sided alpha of 0.05. As described 
comprehensively in the “Statistical Design and Power” attachment, we calculated power for a range of  
Finally, power for analyses of mediators and secondary outcomes using structural equation modeling (SEM) is 
excellent for effect sizes of at least .26 for the direct effects of intervention on the mediator (path a) and of the 
Table 5. Demographic, Clinical, and Psychosocial Assessments 
General 
Construct 
Variable 
Instrument 
Description 
Time Point 
0 3 6 9 12 
Demographic/ 
background 
Study-specific survey; medical 
record reviews 
Age; gender; race/ethnicity; education; employment; country of 
origin; duration of diagnosis; treatment regimen; comorbidities 
X 
 
 
 
 
Treatment satisfaction 
Patient Satisfaction Survey 
14 items; adapted from physical therapy satisfaction survey; 
α=0.99.112  
 
 
X 
 
 
Glycemic 
control 
A1c 
Axis-Shield Afinion point-of-
care assay 
CLIA-waived; meets NGSP certification criteria; correlation with 
laboratory A1c measure=0.991.113 
X X X X X 
% TIR, % 
hyper, % hypo 
Abbot Freestyle Libre Pro 
CGM 
14-day factory-calibrated blinded CGM; MARD of 11.4% 
compared to capillary blood glucose reference values114, 115 
X 
 
X 
 
X 
Psycho-
social 
well-being 
Diabetes 
quality-of-life 
Audit of Diabetes-Dependent 
Quality of Life (ADD-QoL) 
19 items; α=0.85; assesses impact of diabetes on social, 
physical, and emotional functioning.118 
X 
 
X 
 
X 
Diabetes 
distress 
Diabetes Distress Scale (DDS) 28 items; 7 subscales. Total scale α=0.91, subscale range 
α=0.76-.89;119 r=0.56 correlation with CES-D120 
X 
 
X 
 
X 
Health-related 
quality-of-life 
MOS SF-12v2 
12 items; evaluates global physical and mental well-being; 
subscale α in patients with diabetes=0.83-0.85; r=0.40-0.43 
concurrent validity with self-reported physical/mental health121 
X 
 
X 
 
X 
Self-care 
behaviors 
Medication 
adherence 
• Medication adherence self-
rating 
• 3 items; quantitative self-rating of adherence over 30 days; 
0.55 correlation with MEMS122 
X X X X X 
SMBG 
• 14-day meter data 
• Number of BG checks completed over 14 days. 
X X X X X 
Global self-
management 
Diabetes Self-Management 
Questionnaire (DSMQ) 
16 items, 5 subscales; subscale α range=0.72-0.83;  r=-0.53 
correlation with A1c in adults with T1D124 
X X X X X 
Habit strength 
Self-Report Behavioral 
Automaticity Index (SRBAI) 
8 items, 4 for each target behavior (SMBG and taking 
insulin/medication); assesses habit strength; correlation to 
corresponding behaviors ranges from 0.42-0.86.125 
X X X X X 
Self-efficacy 
Diabetes Empowerment 
Scale-Short Form (DES-SF)  
8 items; α=0.84; self-efficacy for performing diabetes self-
management126 
X X X X X 
Healthcare utilization 
Study-specific survey; medical 
record reviews 
Incidence of diabetes-related outpatient medical visits, ED 
visits, urgent care visits, hospitalizations 
 
Monthly 
Table 6.  Power, given 210 participants, for testing intervention effects on A1c over 6 
months (Aim 1), 9 and 12 months (Aim 2), and mediators/secondary outcomes (Aim 3).   
A1c 0-6 months (Aim 1) 
A1c 0-9 or 0-12 months (Aim 2) SEM/mediation analysis (Aim 3) 
% ∆ 
Effect 
size 
6-month attrition % ∆ 
Effect 
size 
Annual attrition 
Effect size 
(path a/path b) 
6-month attrition 
7.4% 
10% 
14.3% 
20% 
7.4% 
10% 
0.81 
0.55 
>.99 
>.99 
0.81 
0.55 
>.99 
>.99 
.26/.36 
.95 
.95 
0.59 
0.40 
.93 
.92 
0.59 
0.40 
.97 
.96 
.36/.26 
.92 
.92 
0.50 
0.34 
.82 
.81 
0.50 
0.34 
.89 
.86 
.26/.26 
.89 
.88 

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mediator on the outcome (path b), which are reasonable to anticipate given effect sizes derived from previous 
literature (see “Statistical Design and Power” for additional details).  
Aim 1: Evaluate the efficacy of REAL-T in improving glycemic control and psychosocial well-being. Our primary 
analysis will evaluate efficacy for changes in A1c on an intention-to-treat basis, assessing between-group 
differences using mixed effects regression models. Nonparametric or other robust statistical methods will be 
used to evaluate the effect of deviations from normality including outliers and truncated data (e.g. eligibility 
criteria of >7.5% A1c) on results. An advantage to mixed effects regression models is that they incorporate all 
available data even when data are incomplete, providing robust effect estimates and high statistical power, as 
well as easy comparisons of the treatment groups. We will also conduct sensitivity analyses to evaluate 
whether outliers or missing data are driving the observed effects, and moderation analyses to examine whether 
baseline clinical or demographic characteristics (e.g., age, gender, ethnicity, SES, treatment regimen, duration 
of diagnosis) modify any observed effects of the intervention. Secondary analyses: We will evaluate the impact 
of the intervention on psychosocial well-being and CGM-derived measures of blood glucose control. Analysis 
of secondary outcomes will assess between-group differences in signed change scores on measures of % time 
in range (the proportion of all time measured by the CGM when blood glucose is between 70 and 180 mg/dl), 
% time in hypoglycemia (the proportion of time blood glucose is below 70 mg/dl), % time in hyperglycemia (the 
proportion of time blood glucose is above 180 mg/dl), depressive symptoms, DQoL, and diabetes distress. 
Aim 2: Assess the post-intervention durability (3- and 6-months post-intervention) of REAL-T’s effects on 
glycemic control and psychosocial well-being. Data analysis will be similar to that outlined in Aim 1, using 
mixed effects regression models, and will examine changes from baseline to 9 and to 12 months (i.e., 3 and 6 
months post-intervention). In addition, we will explore the pattern of changes in A1c and other outcomes 
across the 4 study subintervals, stratified by treatment group. We will fit linear trends to the subintervals and 
test if model fit is improved significantly compared to models using fewer linear subintervals. Although analyses 
can be completed incorporating data from all time points in one model, the potential for varying effects over 
time has led us to frame our primary endpoints in terms of changes over specific shorter periods; exploratory 
analyses will consider possible longer-term patterns of change. 
Aim 3: Examine mediating mechanisms of REAL-T’s observed effects through structural equation modeling. In 
Aim 3, we propose to test mediation of: (a) intervention effects by self-care behaviors on A1c and psychosocial 
well-being (Aim 3, Hypothesis 1); (b) self-care behaviors as well as psychosocial well-being by self-efficacy 
(Hypothesis 2); and (c) self-care behaviors by habit strength (Hypothesis 3). These potential mechanisms will 
be assessed via structural equation models which include both direct and indirect effects of intervention. The 
indirect effects will be estimated as the multiplicative effect of direct paths between intervention and the 
mediator and between the mediator and the outcomes.  
Cost Analyses: We will evaluate the cost-effectiveness, cost impact, and change in quality-adjusted life years 
(QALYs) associated with REAL-T relative to usual care. We will calculate incremental cost-effectiveness ratios 
(ICERs) divided by the average difference in QALYs; if preliminary analyses demonstrate that REAL-T is both 
cost-saving and net health beneficial (which would render the ICERs moot), we will instead calculate the 
projected total net health benefits for REAL-T relative to usual care. ICERs will be calculated according to 
generally recognized best practices, using two different timeframes and scopes. The first will be a simple Cost 
Utility Analysis (CUA) over a 12-month timeframe, using cost and QALY inputs derived directly from observed 
data, adopting a “Payer” perspective (direct medical costs only). This CUA estimate will likely represent a 
conservative lower bound, given the short follow-up period, as the greatest intervention costs are incurred up-
front, while some medical cost savings may take longer to realize. The second CUA will employ modeling to 
project longer-term costs and health benefits, using parameter estimates derived from the literature, from both 
a “Payer” perspective and a “Social Planner” perspective (direct and indirect medical costs). The mathematical 
approach will employ Markov modeling, but may also incorporate microsimulation, decision trees, or Monte 
Carlo methods. Non-parametric bootstrapping methods will be used to bound the ICER estimates by deriving 
confidence intervals around the cost-effectiveness ratios for the 12-month timeframe CUA. For the longer-term 
CUA, we will conduct univariate and multivariate sensitivity analyses to further bound the ICER estimates.