Sleep Optimization to Improve Glycemic Control in Adults With Type 1 Diabetes
Phase: NA
Conditions: Type 1 Diabetes
Sponsor: University of Illinois at Chicago

 
 
 
 
 
Sleep Optimization to Improve Glycemic Control in Adults with Type 1 Diabetes 
Study Protocol 
Version 20, 2/7/2025 
 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
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Sleep Optimization to Improve Glycemic Control in Adults with Type 1 Diabetes 
Abstract 
Despite improvements in treatment regimens and technology, less than 20% of adults with type 1 diabetes 
(T1D) achieve glycemic targets. Sleep is increasingly recognized as a potentially modifiable target for 
improving glycemic control. Diabetes distress, poor self-management behaviors, and reduced quality of life 
(QoL) have also been linked to sleep variability and insufficient sleep duration. The American Diabetes 
Association Standards of Medical Care in Diabetes incorporated sleep as an important component of the 
medical evaluation in persons with diabetes. However, no specific recommendation was given as to how to 
improve sleep. A significant gap of knowledge exists regarding the effects of sleep optimization on glycemic 
control in T1D. The purpose of this study is to determine the efficacy of a T1D-specific sleep optimization 
intervention (Sleep-Opt) on the primary outcomes of sleep variability, sleep duration and glycemic control 
(A1C); other glycemic parameters (glycemic variability, time in range), diabetes distress, self-management 
behavior, QoL, and other patient reported outcomes in working-age adults with T1D and habitual increased 
sleep variability or short sleep duration. To achieve these aims, we propose a randomized controlled trial in 
120 working age adults (18 to 65 years) with T1D. Participants will be screened for habitual sleep variability (> 
1 hour/week) or insufficient sleep duration (< 6.5 hours per night). Eligible subjects will be randomized to the 
Sleep-Opt group or healthy living attention control group for twelve weeks. A one-week run-in period is 
planned, with baseline measures of sleep by actigraphy (sleep variability and duration), glycemia (A1C and 
related glycemic measures: glycemic variability and time in range using continuous glucose monitoring), and 
other secondary outcomes: diabetes distress, self-management behaviors, quality of life and additional patient-
reported outcomes. Sleep-Opt is a technology-assisted behavioral sleep intervention that we recently 
developed that leverages the rapidly increasing public interest in sleep tracking by consumers (+500% in 3 
years). Our behavioral intervention employs four elements: a wearable sleep tracker, didactic content, an 
interactive smartphone application, and brief telephone counseling. The attention control group will participate 
in a healthy living information program. At midpoint (Week 6) completion (Week 12) and post-program (Week 
24), baseline measures will be repeated to determine differences between the two groups and sustainability of 
the intervention.  
 
 
 

 
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Specific Aims  
Despite improvements in treatment regimens and technology, less than 20% of adults with type 1 diabetes 
(T1D) achieve glycemic targets.1 Sleep is increasingly recognized as a potentially modifiable target for 
improving glycemic control. Specifically, sleep variability (variability in sleep duration)2 and insufficient sleep 
duration (insufficient total sleep time) may adversely affect glycemic control3 via circadian misalignment,4 
diabetes distress, poor self-management behaviors, and reduced quality of life (QoL).5-9 The American 
Diabetes Association has added sleep as an important component of medical evaluation in persons with 
diabetes.10 However, research is limited as to how to optimize sleep among patients with T1D and whether 
such interventions improve important outcomes including glycemic control, diabetes distress and quality of life. 
The proposed study will determine the efficacy of a T1D-specific sleep optimization intervention (Sleep-
Opt) targeted to reduce sleep variability, insufficient sleep duration and improve glycemic control (A1C; primary 
outcomes); other glucose parameters (glucose variability, time-in-range [TIR]), diabetes distress, self-
management behavior, quality of life and other patient-reported outcomes in working-age adults with T1D and 
habitual increased sleep variability or insufficient sleep duration.  
Our preliminary data demonstrated: (1) Sleep variability in T1D was associated with poor glycemic control, 
increased glycemic variability, and greater distress in cross-sectional studies of 71 working-age adults.2,11 (2) A 
pilot study of a 2-week behavioral sleep optimization in adults without diabetes with habitual short sleep 
duration led to improved fasting insulin resistance; sleep gain significantly correlated with reduction in insulin 
resistance.12 (3) Our team’s pilot study of Sleep-Opt in T1D working-age adults demonstrated reduced sleep 
variability, A1C, glycemic variability, and improvement in self-reported fatigue, mood, sleep and distress.  
The goal of this study is to examine whether the Sleep-Opt intervention, a novel intervention to reduce 
sleep variability and insufficient sleep duration, improves objective sleep and glucose parameters, diabetes 
distress, self-management behavior, QoL, and other patient-reported outcomes. Responding to PA-18-330, 
Investigator Initiated Clinical Trials Targeting Diseases with the Mission of NIDDK, we will randomize 120 
individuals with T1D and habitual increased sleep variability or insufficient sleep duration to a 12-week Sleep-
Opt intervention or healthy living control group equal in time and attention. Our novel sleep optimization is 
based on behavior change principles (self-monitoring, goal setting, motivational enhancement) and employs a 
wearable sleep tracker, didactic content, and brief telephone counseling tailored to T1D.13  
Specific aims: In 120 working-age adults (18-65 years) with T1D: 
Aim 1: Determine the effect of the Sleep-Opt intervention, compared to an attention control group, on 
sleep variability, sleep duration and glycemic control (primary outcomes). Rationale:  Sleep variability and 
insufficient sleep duration in T1D adults was associated with higher A1C, possibly related to circadian 
misalignment and reduced insulin sensitivity.  Hypothesis: Sleep-Opt will result in improved sleep and 
glycemic control. Approach Assess sleep variability, sleep duration, and glycemic control as measured by 
actigraphy and A1C at Week 0 (baseline), Week 6 (mid-treatment), Week 12 (end of treatment) and post 
treatment Week 24, of Sleep-Opt compared to a 12-week healthy living attention control. Secondary outcomes 
include glycemic variability, time in range and other objectively measured sleep parameters.  
Aim 2: Determine if Sleep-Opt will result in improved psychological and behavioral outcomes, including 
diabetes distress, diabetes self-management behavior, QoL, fatigue, mood, subjective sleep quality compared 
to the healthy living attention control group. Rationale: Sleep variability and insufficient sleep duration was 
associated with diabetes distress, poor self-management behavior and reduced QoL. Hypothesis: Sleep-Opt 
will lower diabetes distress, improve self-management behavior and QoL. Approach Assess diabetes distress, 
self-management behavior, QoL and other patient-reported outcomes (fatigue, mood, sleep quality) at Week 0 
(baseline), Week 6 (mid-treatment), Week 12 (end of treatment), post treatment at Week 24 in Sleep-Opt 
compared to a 12-week healthy living attention control. .  
Aim 3: Determine the contribution of changes in sleep variability and sleep duration during the intervention 
to changes in glycemic parameters (A1C, glycemic variability, time in range) Rationale:  Sleep variability was 
associated with poor glycemic control and greater glycemic variability in T1D adults.  Short sleep was 
associated with reduced insulin sensitivity in T1D. Hypothesis: Reduction in variability and improved sleep 
duration will correlate with improvement in glycemic parameters. Approach Analyze the relationship between 
changes in sleep variability, sleep duration, and glycemic parameters (outlined in Aim 1) after a 12-week 
Sleep-Opt and 12-week healthy living attention control at Week 0 (baseline), week 6 (mid-treatment), Week 12 
(end of treatment) and week 24 (post-treatment). 
Our long-term goal is to advance the understanding of the potential for sleep optimization as an important 
component of diabetes care to improve glycemic control and QOL. This goal aligns with and enables the 
NIDDK strategic plan to support research on the prevention, treatment, and cure of T1D and its complications. 

 
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Research Strategy 
A. Significance 
A.1 Importance of sleep to health 
Sleep variability and insufficient sleep duration have negative health consequences in the general 
population. These include: changes in appetite and eating patterns,14-16 obesity,17 insulin resistance,18, 
increased systemic inflammation,19, metabolic syndrome, 20 dysglycemia,21 risk for incident diabetes,16 
depression,22 and a higher prevalence of cardiovascular disease (CVD).23 Sleep times of less than 300 
minutes have been associated with up to four times the mortality risk of those with greater than 300 
minutes.24,25 Each hour of increased sleep variability, as measured by standard deviation (SD) of sleep 
duration, was associated with a 27% higher odds of metabolic syndrome in a multi-ethnic population. 20 Work 
commitments, family and social obligations, and general stress have also been linked with poor sleep.26,27 
Negative health consequences of insufficient and irregular sleep may be amplified for persons with T1D, who 
must also cope with the added burden of managing a chronic condition.28  
 
A.2 Insufficient and irregular sleep affects glycemic control in T1D 
Up to 40% of adults with T1D had insufficient sleep (sleep duration < 6-6.5 h/night) either by self-report or 
objectively assessed.2,29-36 Insufficient sleep is a predictor of poor glycemic control in T1D.29,37 Increased insulin 
resistance likely plays a central role; one night of experimental sleep restriction (4 hours) in 7 patients with T1D 
was associated with decreased peripheral insulin sensitivity, compared to normal sleep duration (7.8 hours).38 
In our recent meta-analysis, adults with T1D who reported sleeping > 6 hours had 0.24% lower A1C levels 
than those sleeping ≤ 6 hours.3  
In addition to insufficient sleep, sleep variability, a potential marker of circadian misalignment, can impact 
glycemic control. Up to 73% of adults with T1D have sleep variability (> 1 hour).2,11 The circadian system plays 
an important role in glucose metabolism, and experimental circadian misalignment results in impaired glucose 
tolerance.4,39 Thus, sleep variability could be detrimental to glycemic control. Supporting this hypothesis, recent 
studies have reported that sleep variability is an independent predictor of glycemic control in T1D. .2,40,41  Sleep 
variability, SD of sleep duration as objectively measured by actigraphy, explained 8.2% to 15.8% of the 
variance in glycemic control.2,40 In our study of 41 working-age adults with T1D, those with SD of sleep 
duration >1 hour had significantly higher A1C than those with SD sleep duration ≤1 hour (median 7.2% vs. 
7.8%, p = 0.008). Sleep variability was also associated with increased daily insulin requirement, suggesting 
more insulin resistance in these individuals.2  These findings were reproducible: another study in 65 
adolescents with T1D also found that greater SD of sleep duration was significantly associated with higher 
A1C,42 and in a study of 191 German T1D adolescents, greater variability of sleep timing between work and 
free days was associated with higher insulin requirements.43 T1D patients lack endogenous insulin secretion; 
varying degrees of insulin resistance could lead to increased glycemic variability (within-day glucose 
fluctuations), a factor reported to be associated with increased microvascular complications and cardiovascular 
events in T1D.44,45 Indeed, our pilot data in 30 T1D adults revealed that greater SD of sleep duration was 
associated with greater glycemic variability as measured by continuous glucose monitoring.11 
These data strongly suggest that sleep variability and insufficient sleep duration affects glycemic control 
and glycemic variability, with the effect size similar to some standard treatments for T1D.46,47 Despite 
recognition from the American Diabetes Association that sleep patterns should be assessed in 
individuals with diabetes,10 no published studies to date have explored the effects of sleep 
optimization (strategies to improve sleep duration and variability) on glycemic control in adults with 
T1D. These data are needed and could have a large clinical impact, given the current state of suboptimal 
glycemic control and increasing incidence of T1D.  
 
A.3 Sleep is related to diabetes distress, self-management behavior, and quality of life in T1D 
Sleep is increasingly recognized as an important contributor to diabetes distress.8,28 Diabetes distress 
pertains to the emotional burdens and worries associated with the complexities of managing diabetes.48 
Moderate to high distress levels are experienced by up to 54% of those with T1D.48 Concern over blood 
glucose levels (particularly fear of hypoglycemia) is a major source of distress at night that impacts sleep.49 
Individuals with T1D report delaying sleep, waking during the night to monitor blood glucose levels, or 
maintaining blood glucose levels higher to avoid hypoglycemia. These self-management behaviors result in 
reports of less restful sleep and diminished well-being the subsequent day.6 In a study of 267 adults with T1D, 
diabetes distress was found to be significantly higher in those adults who reported poor sleep quality. Those 
with poor sleep quality also experienced greater daytime sleepiness and diabetes regimen burdens.28 Our 

 
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team conducted focus groups with T1D adults to examine contemporary challenges of diabetes self-
management. Qualitative reports indicated that sleep was a major source of distress.6  
Diabetes distress is associated with higher A1C, greater glycemic variability in T1D,7,8,50 and poorer self-
management behaviors (such as dietary/medication compliance,51,52 prevention and management of 
hypoglycemia,53 and physical inactivity).54,55 Thus, diabetes distress can be a barrier to improving glycemic 
control and self-management behavior.56 Interventions using diabetes-self management education alone or in 
combination with psychological components produced reported reductions in diabetes distress and improved 
glycemic control in some8,57-60 but not all studies.8,61 Despite these relationships, no studies to date explored 
the effects of sleep interventions specifically designed to address T1D distress and its effects on glycemic 
control.  
Poor sleep has also been linked directly to self-management behavior. In a cross-sectional study of 45 
adolescents, a significant relationship was found between sleep duration and self-management behavior.62 
Speciﬁcally, a 15- and 20-min increase in sleep was associated with one additional blood glucose check and 
one additional insulin bolus, respectively.62 In addition, sleep variability (SD of sleep duration) was found to be 
a significant predictor of self-management behavior, explaining 6.1% of the variance in the frequency of blood 
glucose monitoring.40 Thus improving sleep variability and duration could potentially improve self-management 
behavior.  
Self-reported sleepiness and/or poor sleep habits have been correlated with reduced QoL, depressed 
mood, and reduced academic performance.63 In addition, time spent in stage N2 (lighter sleep) as assessed by 
polysomnography has been significantly correlated with lower QoL.63 Thus, improving sleep could potentially 
improve self-care behavior and QoL, but this has yet to be explored.  
 
A.4 Why a sleep optimization intervention could improve glucose control in highly variable or short 
sleepers 
The goal of this study is to improve glycemic control (A1C) by reducing sleep variability and improving 
insufficient sleep duration in T1D using a randomized controlled design. The secondary outcomes of glycemic 
variability, time in range (TIR), diabetes distress, diabetes self-
management behavior, QoL and other patient-reported outcomes (e.g. 
fatigue, sleep quality, mood) will also be examined. We conceptualize 
that sleep duration variability > 1 hour across one week or insufficient 
sleep < 6.5 hours directly influence glycemic control, increase diabetes 
distress, and influence self-management behavior. Improving these 
sleep parameters will improve glycemic control, reduce glycemic 
variability and improve TIR, reduce diabetes distress, enhance self-care 
behavior, and improve QoL (Figure 1).  
Despite data to support a causal relationship between sleep variability and insufficient sleep with 
abnormal glucose metabolism, very few studies have explored sleep interventions to improve 
metabolic outcomes. One published study examined the effects of home sleep extension on glucose 
metabolism in healthy volunteers (n = 16) who were chronic short sleepers.64 After six weeks of sleep 
extension (average increase of 44 minutes/day), there was a robust relationship between the increase in sleep 
duration and improvement in fasting insulin sensitivity. In our randomized cross-over sleep extension study of 
21 short-sleeping non-diabetic working-age adults, those who extended their sleep to > 6 h/night for 2 weeks 
had significant improvement in fasting insulin resistance, early insulin response to glucose, and β-cell 
function.12 Another RCT of 42 normal-weight, healthy, short sleepers showed a 21-minute increase in sleep 
duration for the intervention group. This was associated with decreased intake of fat, carbohydrates, and free 
sugars.65  
Perfect et al. conducted a short-term pilot RCT using sleep extension in 79 adolescents with T1D for one 
week.66 The sleep extension intervention included didactic information on topics such as the importance of 
sleep, sleep hygiene principles, control of environmental conditions, management of competing activities and 
stress reduction, as well as use of a sleep log and actigraphy monitoring.66 The preliminary results revealed 
that glucose levels as measured by continuous glucose monitoring in extension participants differed from the 
fixed-sleep-duration group by 17 mg/dl points (p = .003) during the sleep modification week. Sleep extension 
resulted in 11 hours more spent in the glucose target range than those in the fixed-sleep condition.67 This 
emerging evidence supports the feasibility and efficacy of sleep optimization, as well as a possible dose-
response relationship between optimized sleep and changes in metabolic control. Because sleep is linked to 
Figure 1. Model of relationships among 
sleep, glycemic control, distress, self-
management behavior and QoL.  

 
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possible mediators of glycemic control (including diabetes distress, diabetes self-management behavior, and 
QoL), those mediators' influence on glycemic control during sleep optimization need to be examined also.  
 
A.5 Wearable sleep trackers provide a critical opportunity to engage short sleepers 
Over the past few years, the public’s interest in monitoring sleep has increased immensely, providing an 
important opportunity to affect sleep in public health. Marketing research reported a 500% annual growth in the 
wearable fitness tracker market for the past three years.68 In 2016, over 12% of U.S. adults owned a fitness 
monitor,69 and sleep was rated as the most popular feature to track.69 Our intervention uses data from a 
wearable sleep tracker (Fitbit) to personalize feedback and promote interaction with remote coaches. Given 
that individuals often need to forgo other potentially more rewarding activities to extend sleep, sleep-tracking 
devices provide a way to make sleep extension insightful and rewarding. Companies including Fitbit and Apple 
have released features such as bedtime reminders and sleep goals. This suggests that consumers are 
interested in improving sleep, but the impact on sleep and health is unknown.  
 
A.6 Why enhancing adherence to technology-assisted behavioral interventions is key to improvements 
Many technology interventions suffer from high rates of non-adherence.70 
Coached interventions typically show larger effect sizes than unguided 
interventions, likely due to improved adherence.71 The process by which human 
support enhances adherence to behavioral intervention technologies has been 
termed “Supportive Accountability” (Figure 2)72 and draws on broad empirical 
literature, including clinical and organizational psychology73,74 and motivation 
theory.75,76 This model suggests that behavioral intervention technology users are 
more likely to adhere if they are accountable to another person. Accountability is 
defined as knowing that one will have to justify use or non-use to another individual 
at some future time.73 The effects of accountability are enhanced when goal setting 
and progress are known to another person, goals are process- rather than 
outcome-focused, and expectations are defined in advance. The model involves 
qualities of the coach, including legitimacy (the person the user is accountable to 
has some expertise74 and is viewed as trustworthy and benevolent). We designed 
and tested a coaching protocol around these principles77 that demonstrated the 
capacity to enhance adherence in a sleep extension intervention (see C.2.7).  
A.7 The proposed work will test the efficacy of a 12-week T1D-specific sleep 
optimization intervention “Sleep-Opt” on the primary outcomes of sleep 
variability, sleep duration and glycemic control (A1C); and secondary 
outcomes: other glycemic parameters (glycemic variability, TIR), diabetes 
distress, self-management behavior, QoL (and other patient reported 
outcomes) in working-age adults with T1D and habitual short sleep duration 
or increased sleep duration variability under a randomized controlled design. Working-age adults (18-65 
years) often encounter social and family obligations that affect their sleep duration, especially on weekdays, 
and were shown to have less sleep regularity than older individuals.78 To determine sustainability of the 
intervention, follow-up will be performed at the 24th  week. This innovative study will provide insights into 
the causal relationship of sleep with glycemic control and diabetes distress, self-management 
behavior, and QoL in individuals with T1D. Our unique technology-based Sleep-Opt is scalable and 
easily deployed; thus, it could have a wide clinical implication for persons with T1D.  
 
B. Innovation 
Our proposed study will be innovative in that it will study Sleep-Opt on working-age adults with 
T1D. Innovative features include: (1) Optimizing sleep in T1D. Only one sleep intervention study has been 
conducted in the T1D population in adolescents. No interventions have focused on working-age adults. (2) 
Measuring the effects on glycemic control. Sleep variability and insufficient sleep contribute to poor 
glycemic control and glycemic variability. A sleep optimization program to improve glycemic outcomes 
represents a novel approach to addressing the role of sleep in glycemic control and glycemic variability. Self-
management education focuses primarily on modification of diet, activity, and insulin therapy to promote 
glycemic control. Using sleep hygiene to improve glycemic control adds an important self-management 
strategy. (3) Measuring the effects on diabetes distress, diabetes-self management, and QoL. Poor sleep 
is related to diabetes distress, poor diabetes self-management, and reduced QoL, which potentially contribute 
Figure 2: Supportive 
Accountability 
 

 
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to poor glycemic control. Sleep optimization offers a unique and practical treatment to improve these outcomes 
and glycemic control. No such approach currently exists. (4) Measuring intervention sustainability, as 
determined by a follow-up period. No data exist regarding sleep optimization durability in the T1D 
population. Understanding the long-lasting effects will lead to designing a reinforced intervention with proper 
timing and optimize the effects of Sleep-Opt.  
Sleep-Opt is unique, practical, and scalable. The proposed intervention is based on a theoretical model 
focused on motivating long-term behavior change. It uses technologies desirable to patients and already part 
of their daily lives. Using this combination of technology and well-validated behavior change strategies, we can 
engage patients to learn their behavioral patterns and make lasting changes in their sleep habits. The use of 
technology and automated support could result in a highly scalable and cost-effective intervention.  
 
C. Approach 
C.1 Preliminary Studies 
C.1.1 Sleep variability and Insufficient sleep duration are associated with poorer glycemic control in 
adults with T1D. Dr. Reutrakul, co-I of this study, published a meta-analysis exploring the role of sleep in T1D. 
In a pooled analysis of 533 adults, those reporting sleeping < 6h had a higher A1C level by 0.24% than those 
sleeping ≥ 6 h.3 Dr. Reutrakul’s subsequent study explored the role of sleep variability using 5-day actigraphy 
in 41 adults with T1D.2 Those with higher sleep variability (standard deviation [SD] of sleep duration > 60 
minutes) had significantly higher A1C levels than 
those with lower sleep variability (median 7.8% vs. 
7.2%; Figure 3). In addition, higher sleep variability 
significantly correlated with higher daily insulin 
requirement (r = 0.386, p = 0.01), suggesting higher 
insulin resistance in those with greater degree of 
irregular sleep. Dr. Martyn-Nemeth, PI, studied 30 
adults with T1D and found that variability of sleep 
duration as measured by actigraphy was significantly 
related to glycemic variability (glucose SD) measured 
by continuous glucose monitoring (CGM; r = 0.458, p 
= 0.01).  
 
C.1.2 Sleep optimization in short-sleeping non-diabetic adults resulted in improved glucose 
metabolism. Dr. Reutrakul performed a randomized cross-over study using two-week behavioral sleep 
optimization aimed at extending sleep duration and promoting sleep hygiene, including sleep regularity, in 21 
non-diabetic adults with habitual short sleep (mean 5.3 h/night by actigraphy). Participants who extended their 
sleep duration to > 6 h/night (N = 8, mean 6.6 h/night) had significantly improved fasting insulin resistance 
(HOMA-IR; adjusted mean difference [MD] -0.50, p = 0.013), increased early insulin response to glucose 
(insulinogenic index; MD 0.39, p = 0.001), and improved β-cell function (disposition index; MD 1.07, p = 
0.02).12 This suggested that sleep extension is feasible and has favorable effects on glucose metabolism in 
short-sleeping adults. 
 
C.1.3 Our newly developed sleep optimization intervention developed for adults with short sleep 
duration (Sleep Bunny) demonstrated a clinically significant mean sleep increase of 38 minutes.79  
The intervention included use of a Fitbit, weekly didactic content, 
interactive tools (e.g., reminders), and coach-delivered feedback.13 An 
RCT was conducted with 15 adult participants who were followed for six 
weeks with a protocol similar to what is proposed. All participants 
completed 2 periods of actigraphy. Those in the intervention group wore 
the Fitbit for 85-100% of study days and completed 90% of coaching 
sessions. Results from the intervention (n = 10) and control (n = 5) 
groups showed that the intervention was effective at improving sleep 
duration by a mean of 38 min (Table 1), with improvement in sleep 
efficacy, sleep-related impairment, and sleep disturbance. 
 
 
Table 1. Pilot Sleep Extension trial, 6 weeks 
  
Intervention 
Control 
  
N = 10 
N = 5 
Mean Sleep 
Change 
+0:38:36 
+0:08:4
8 
Time in Bed 
+0:29 
+0:08 
Sleep Efficacy % 
+3.2% 
-0.5% 
PROMIS - Sleep 
Disturbance 
-7 
+4 
PROMIS – 
Sleep-Related 
Impairment  
-1 
+8 
 
 
 
Figure 3: Actigraphy recordings of two T1D patients with different sleep 
patterns and their A1C. Left: patient with consistent sleep duration and 
timing. Right: patient with varying sleep duration and timing. 

 
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C.1.4. T1D Sleep-Opt intervention pilot tested in 8 working adults with T1D, revealed those with high 
sleep variability responded with improved sleep and glucose parameters.  
Using a RCT design, we pilot tested the T1D Sleep-Opt intervention and compared it to a healthy living 
attention control group in 8 adults with objectively measured sleep duration (< 6.5 hours per night) or sleep 
variability (sleep duration SD > 1 hour per week). The intervention was adapted from the Sleep Bunny sleep 
extension intervention developed by members of our team.13 The content was tailored to address both sleep 
duration and sleep variability as well as content specific to T1D identified from our focus groups.80 The 
intervention included use of a Fitbit, weekly didactic content, interactive tools (e.g., reminders), and coach-
delivered feedback.13. Participants completed 3 periods of actigraphy, continuous glucose monitoring (Abbott 
Libre®) self-reported questionnaires and a blood draw for A1C at baseline (week 0) at completion of the 
intervention (week 8) and post-program (week 12). A1C was 
reduced by 0.16% in the Sleep Opt group compared to 0.3% in 
the control group. The Sleep-Opt group had nearly optimal A1C 
levels at baseline (5.4-7.3) and could not have safely reduced 
their glucose levels further. In comparison, the healthy living 
group began with higher A1C levels (ranging 6.7-9.0). Glycemic 
variability (CV%) was reduced by 3% in the Sleep-Opt group 
vs. a 4% increase in the control group. Sleep variability 
improved in both groups. Patient-reported variables 
demonstrated a greater improvement in the sleep vs. the control 
group. Fatigue, sleep quality, mood and distress improved in 
the sleep opt compared to the control group from baseline to post-program. (Table 2). There appeared to be a 
reactive effect in the control group as their objective sleep improved (however subjective sleep and other 
patient-reported outcomes did not). We plan to address this in the future by providing a questionnaire at the 
end of the control intervention to evaluate the presence of any sleep behavior changes. We also plan to 
provide the control group the opportunity to participate in the Sleep-Opt intervention at the end of the study. In 
terms of feasibility, all 8 subjects completed the pilot study and evaluated the program positively. The 
technology employed was easy to use and the sleep content and coaching calls beneficial. All participants 
completed 100% of coaching sessions. This pilot study provides proof-of-concept that the Sleep-Opt 
intervention demonstrated improvement in glycemic, sleep and important patient-reported variables.  
 
C.1.5 Our research team consists of investigators from multiple disciplines with expertise that will 
facilitate successful completion of the project. This application brings together an interdisciplinary team 
with shared (endocrinology, sleep, CGM technology and glycemic variability measures, chronic illness, and 
clinical practice) and complementary (behavioral coaching, technology-assisted behavior change, statistical 
modeling, and Certified Diabetes Educator [CDE®]) expertise. Collectively, the team’s research strengths span 
conceptual, methodological, clinical, and statistical research in self-management and chronic disease. The 
team has a strong history of collaboration.  
 
 
 
C.2 Research plan  
C.2.1 Proposed study overview. Figure 4 and Table 3 illustrate the proposed study. We will  enroll  up to 
300 non-shift-working adults aged 18-65 years with a clinical diagnosis of T1D for at least one year who report 
habitual sleep variability (> 1 hour variation in sleep onset or wake up time/week) or insufficient sleep duration 
< 6.5 h/night during work- or weekdays. After informed consent is obtained, participants will have a baseline 
assessment during a 1-week run-in phase. Subjective sleep and objective sleep assessment by wrist 
actigraphy will be performed to confirm eligibility.  Eligible participants will then be randomized to either a 
Sleep-Opt or healthy living attention control group for 12 weeks. At baseline, diabetes distress, diabetes self-
management behavior, quality of life, self-reported fatigue, sleep quality, mood, anxiety, fear of hypoglycemia, 
daytime sleepiness, and anxiety will be assessed. Glycemic assessments will be performed by A1C, and 1-
week continuous glucose monitoring (CGM). At 6 weeks (mid-program) and 12 weeks (end of intervention 
period), these assessments will be repeated to evaluate the effects of the intervention. At the 24-week follow-
up visit, the same assessments will evaluate the program's sustainability.  
 

 
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C.2.2 Setting: The study will take place at subjects’ home. We will mail study materials and schedule 
videoconferences for orientation to the study procedures and instruct subjects how to use the study devices. 
 
C.2.3 Participants.  We plan to enroll up to 300 subjects. We anticipate a 40% screen failure rate and 
expect to randomize up to 170 for a final sample of 120 subjects with complete data. Inclusion criteria: working-
age adults, 18-65 years with a clinical diagnosis of T1D for at least one year who reported habitual sleep 
variability (1 hour/week or more)  or sleep duration < 6.5 h/night during work- or weekdays (confirmed with 
actigraphy) with a desire to improve sleep and who own a smartphone compatible with Fitbit. Exclusion criteria: 
Insomnia symptoms defined as severe as assessed by the Insomnia Severity Index81 (score ≥ 15), being at 
high risk for obstructive sleep apnea as assessed by and Stop  Questionnaire,82 history of severe 
hypoglycemia (defined as hypoglycemic episodes that result in loss of consciousness within the last 6 months, 
seizures, or requiring emergency room visits or hospitalization), A1C > 10%, rotating shift or night shift work, 
use of sleep medications/aids, significant renal impairment (estimated glomerular filtration rate < 45 
ml/min/1.73 m2), significant medical morbidities (such as congestive heart failure, cirrhosis, chronic obstructive 
pulmonary disease requiring oxygen, active treatment for cancer, restless leg syndrome, depression (PHQ8 
score greater than or equal to 10), history of stroke with neurological deficits), or breast feeding, pregnant, or 
planning pregnancy.  
 
C.2.4 Recruitment. Subjects will be recruited through the UIC medical center/university, diabetes clinics in  
the Chicago metropolitan area through diabetes websites and organizations, using flyers, e-announcements, 
recruitment letters, listservs and Research Match (www.researchmatch.org), participants from previous studies 
who have provided consent to be contacted for future studies (Protocols 2014-0477,  2018-0382, and 2022-
1130), and the Division of Endocrinology at Cook County Health. At UIC, we (study staff) will identify potentially 
eligible subjects through the electronic medical record (EMR) system and send recruitment letters and emails. 
The UIC endocrinology clinic serves approximately 4,000 patients with diabetes per year that reflect a racially, 
ethnically, and socioeconomically diverse urban population. We have used these same recruitment methods in 
our previous studies with approximately 20% of study participants recruited through the UIC medical center, 
while 80% have been recruited through the other sources identified above. We have also drafted a 
Smartphrase for authorized research staff and providers at both clinics to send to potentially eligible patients 
via the MyChart Patient Portal in Epic Powerchart EMR. In addition, we will recruit via social media websites 
such as Facebook and Instagram by posting our flyer to relevant peer support groups for people with Type 1 
diabetes and via Facebook Ads. We will work with national Type 1 Diabetes organizations such as the T1D 
Exchange to share our study via their social media accounts and events. Specific ads will be submitted for 
approval prior to publishing on social media. Our study will be listed on the UIH Research registry (UIHRR) and 
The New Normal (TNN) websites.  People who are interested in contacting our research staff can email our 
shared mailbox sleepopt@uic.edu or visit our listing on the UIHRR.  
 
C.2.5 Sample size estimation. We conservatively estimated the minimal detectable difference between 
treatment arms to be 0.4 to 0.6 standard deviations for our target sample size of 60 per group (after attrition) at 
the 12-week post-treatment measurement based on a 2-groups pre-post design, α = 0.02, 2-sided, and 80% 
power. This represents a modest but clinically important change. Table 4 shows projected estimates for 
attention controls, based on our pilot data with this population, and the treatment group, based on the minimum 
detectable difference we could detect as statistically significant given our design, target sample size, and 
correlations between time points of 0.5 to 0.8.83 Standard deviations from pilot data for A1C (1.07%), sleep 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
9
variability (30 minutes), and sleep duration (49 minutes), and the correlation between measurements (r = 0.56 
to 0.85) were used for sample size determination.  
 
Analyses adjusting for the pre-treatment glucose level and other 
key covariates (e.g., BMI) will increase power. Aim 2 assesses the 
main effects of secondary outcomes and, similar to the primary aim, 
will allow us to detect effect sizes of 0.3 to 0.5 or greater as 
statistically significant. Adjustment for multiple testing.  Our 
primary aim uses an adjusted alpha level for multiple primary 
outcomes.  For secondary aims, exploratory, and hypothesis 
generating analyses we will publish unadjusted p-values and apply 
the Benjamini-Hochberg approach to control the family-wise error rate.84 Aim 3 pertains to correlations 
between change measures of sleep and glucose control parameters, for which we will be able to detect 
correlations of the magnitude of .25 or greater as statistically significant.85  
 
C.2.6 Procedures –  
C.2.6.1 Screening (Week 0). Study staff will conduct initial screening of potential subjects for 
inclusion/exclusion criteria by telephone. This screening will include age, diagnosis, brief health history, usual 
sleep patterns and use of sleep aids. Prospective participants will then be provided a link to sign electronic 
consent form and complete screening questionnaires in REDCap. The questionnaires will assess for 
exclusionary criterion: insomnia (Insomnia Severity Index), sleep apnea (Stop ) and depressive mood (Patient 
Health Questionnaire (PHQ8). Those who score 15 or greater on the Insomnia Index, at high risk for sleep 
apnea on the Stop  or 10 or greater on the PHQ8 will be excluded. Those with a PHQ8 score of 10 or greater 
(indicative of a depressive mood) will be offered a list of mental health resources (See Mood Score Script, Ver 
1, 3-20-20 and Health Resources, Ver 1, 3-20-20). Anyone who is ineligible but expresses problems with sleep 
will also be sent a list of Sleep Resources if they are interested (See SleepResources Ver1 8-18-21). 
Those who meet study criteria by initial screening will be scheduled for a video conference appointment for 
the start of the 1-week run-in period (Week 0) to obtain baseline measures, confirm eligibility (A1C, urine 
pregnancy [for females] and actigraphy for sleep), and apply a CGM device and give instructions on their care. 
Prior to the video conference appointment, study staff will mail study materials (pregnancy test strips, 
measurement tape, CGM (FreeStyle Libre Pro CGM and Reader, or Dexcom), Actiwatch, paper sleep log, A1C 
kit, postage-paid package to return supplies,) to subjects’ home.  
During the first video conference we will meet for approximately one hour to review the study procedures 
and instruct a subject how to use the study materials (pregnancy test strips, measurement tape, CGM 
(FreeStyle Libre Pro CGM and Reader or Dexcom), Actiwatch, A1C kit, paper sleep log, postage-paid package 
to return supplies). If applicable, (1) a subject (women) will test urine for pregnancy; (2) measure their waist 
circumference using a measurement tape; and (3) provide eGFR and most recent height and weight from 
health provider. Then subjects will wear (1) a continuous glucose monitor (CGM) and (2) Actiwatch (3) fill out 
paper sleep log for one week. The CGM is placed under the skin on their arm using sterile technique. Subjects 
will be provided instructions on how to care for the CGM. They will also be given an Actiwatch to track their 
sleep for one week. After one week of wearing the CGM and Actiwatch, subject will remove the CGM and 
Actiwatch and return them in the mail using the postage-paid package. In addition, subjects will use fingerstick 
blood sampling to obtain measures of A1C. Following the 1-week run-in period and a confirmation of sleep 
variability (> 1 hr) or mean sleep duration (< 6.5 h/night) during work- or weekdays, A1C results, and other 
inclusion criteria, subjects will be randomly assigned to the Sleep-Opt intervention or Healthy Living attention 
control group. We will use permuted blocks of 4, arranged in random order and stratified by sex, A1C and age. 
The use of permuted blocks preserves balance in randomization over the course of the study (i.e., at any point 
in time, there will be approximately equal numbers of patients randomized to the intervention or attention 
control group). The randomization model will be developed by the study statistician (Alana Steffen) and 
executed through the Research Data Capture (REDCap) data management system. Investigators overseeing 
intervention and control groups and project director are unblinded; for all other investigators and key personnel, 
randomization allocation will be concealed through blinded coding within REDCap. During the study period, 
subjects will continue to receive their usual diabetes care with their health care provider and care for their 
diabetes as they normally would.  
 
 
Table 4. Minimum detectable treatment effects 
  
Control 
Treatment 
A1C (%) 
7.8 
7.4 
Sleep variability 
(minutes) 
75 
57 
Sleep duration 
(minutes) 
355 
380 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
10
 
C.2.7 Sleep-Opt Intervention. The goal of intervention will be to decrease sleep variability by at least 30 
minutes and/or increase sleep time by at least 30 minutes. The intervention will take place over 12 weeks and 
be conducted remotely by phone call/video conference (Zoom) at participants’ preference. Participants who are 
randomized to Sleep-Opt will receive the following four components: 1) a wearable sleep tracker; 2) a 
smartphone application with interactive feedback and tools; 3) didactic content via email lessons, reminders, 
and notifications; and 4) brief telephone coaching. The components are described below.  
C.2.7.1 Wearable sleep tracker: Participants assigned to the sleep intervention will receive a Fitbit 
wearable sleep tracker to allow them to track their sleep and share results with the coach. Data support that 
consumer sleep trackers provide an estimation of sleep but are less precise than validated actigraphy 
devices.86,87 Therefore, sleep sufficiency will be measured with actigraphy, which is validated but does not 
currently provide real-time feedback to the wearer.88 Fitbit data will be used in coaching sessions and for 
providing weekly reports. The interventionist will have access to participants’ sleep tracker data through a 
dashboard using the Fitabase platform. We considered whether it was necessary to provide Fitbits and rather 
simply rely on coaching for behavior change. We determined that the current popularity of wearable sleep 
trackers would likely help maintain interest in the intervention, as well as provide more easily actionable data 
for both the participant and the coach.  Additionally, if coaching content can be standardized, future directions 
of this intervention may include automated messaging, which would pair well with wearable sleep trackers. 
C.2.7.2 Smartphone application: We will use a commercial sleep tracking application to provide participants 
feedback on their sleep behaviors. Participants will download the Fitbit smartphone application on their 
smartphone and participate in a brief training in the intervention orientation session. Participants will be trained 
to review and edit their Fitbit sleep log each day, thus increasing the validity of the data. Although the Fitbit 
application has developed the ability to enter sleep goals, these features will not be set on participants’ 
applications. In addition, participants will be able to use other features (e.g. step goals) but not trained or 
instructed on the use of these application features as part of the intervention.  
C.2.7.3 Intervention content (Figure 4): Participants will receive 
automated content including didactic lessons for 8 of the 12 weeks with gap 
weeks included for participants to work on behavior change beginning at 
Week 5 (see schedule Table 5), individualized progress reports (weekly), and 
bedtime reminder text messages. The intervention content was developed by 
psychologists with advanced training in sleep and behavior change (Drs. 
Baron and Duffecy) and has been piloted in initial user testing. The 8 didactic 
lessons (estimated duration 8-10 min) of written and video didactic content 
will be delivered via email using REDCap and can be viewed on smartphone, 
desktop, or tablet. Content from the lessons will be reinforced in the 
telephone coaching sessions. Participants will receive an automatically 
generated report each week detailing their days of device usage, average 
bedtime, wake time, sleep duration, and an encouraging statement 
linked to the weekly didactic content. Participants will also receive a 
bedtime reminder via text message via their smartphone 30 min 
before their scheduled bedtime. They can disable the text reminder for 
Weeks 7-12.  
 
Table 5. Intervention Didactic Content and Coaching Schedule 
Week 
Content – Sleep-Opt 
Content – 
Healthy Living 
Coaching 
1 
Intro Basics of Sleep:  
• 
Why do we sleep? 
• 
How much sleep is recommended? 
• 
Consequences of poor sleep with diabetes 
Introduction to 
health living; 
dental health 
20 min 
engagement 
session 
2 
How to Beat Bedtime Procrastination:  
• 
What is bedtime procrastination? 
• 
Take the bedtime procrastination quiz 
• 
How to avoid it 
Handwashing 
5-10 min call 
3 
Sleep and Type I Diabetes 
• 
Nocturnal hypoglycemia 
• 
Nighttime arousals due to diabetes management 
• 
Eating/exercise patterns 
Preventing 
infection 
5-10 min call  
4 
Dealing with Weekends and Challenges 
• 
The circadian system 
• 
What happens on weekends affects your week 
• 
What to do when life happens 
Body alignment 
and stretching 
5-10 min call 
5 
Gap week for skill building 
 
 
Figure 4. Didactic lessons will be sent 
weekly to intervention participants via 
il

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
11
6 
Stress and Sleep 
• 
What is stress 
• 
How does stress affect sleep 
• 
How to manage stress 
Lung and heart 
health 
 
5-10 min call  
7  
Gap week for skill building 
 
 
8 
The Sleep Environment 
• 
Comfort, light and noise 
• 
Electronic devices, partners 
Health risks of 
smoking 
5-10 min call 
9 
Gap week for skill building 
 
 
10 
Effects of Sleep 
• 
Benefits of a good night’s sleep 
• 
What happens if you didn’t sleep 
Vaccination 
5-10 min call  
11 
Gap week for skill building 
 
 
12 
Maintaining your Gains 
• 
Keeping sight of your goals 
• 
Taking note of how you feel  
• 
Getting back on track if needed 
Cancer screening 
Wrap-up 
session (10 
min – week 
12) 
 
C.2.7.4 Coaching: All participants will be assigned to an interventionist who will be a sleep coach to monitor 
their progress during the study and provide telephone coaching sessions related to their sleep-related goals. 
Coaches will be recruited, trained, and supervised by Dr. Duffecy. The coaching protocol developed by Dr. 
Duffecy is based on the principles of Supportive Accountability.89 The coaches will establish legitimacy by 
their knowledge of sleep and basic counseling principles. They will establish goals with the participants based 
on the participants’ values and beliefs, including the sleep-related goals and usage goals (e.g., number of days 
wearing the sleep tracker). Performance monitoring will be completed through an online dashboard visible to 
the coaches (Fitabase®). The dashboard will contain data from sleep diaries and the wearable sleep tracker. 
The first coaching session will be a 20-min engagement session, which includes introductions, rationale for the 
program, clarifying roles of the coach, and the participants’ goals for the program. Coaches (directed by Dr. 
Duffecy) will provide feedback to the participant based on wearable sleep tracker data. For subsequent 
coaching sessions, the coach and participant will also have weekly brief (5-10 min) follow-up support calls to 
troubleshoot any problems with the application or wearable sleep tracker, review progress, problem solve 
barriers to progress, and set goals. Between sessions, the coaches will be available, mostly via email to 
troubleshoot any problems with the application or wearable sleep tracker. All coaching sessions, text and email 
communication will be recorded, and a selection of sessions will be coded for intervention fidelity. The use of 
coaching has been demonstrated to improve adherence to technology-based interventions.90,91 Dr. Duffecy has 
extensive experience in the use of coaching to improve adherence to technology-based interventions.77,90,92,93  
 
C.2.8 Healthy Living Attention Control. The research will be conducted remotely by phone call/video 
conference (Zoom) at participants’ preference.  The design of the 12-week control group is intended to control 
for the coach contact in the intervention group, so that we can test the intervention-related components (e.g. 
motivational enhancement, goal setting, feedback, and sleep education). Dr. Laurie Quinn will train and 
oversee the coaches for the attention control group. Participants assigned to the health education control 
group will be provided eight scheduled emails with health education content (e.g. cooking, handwashing, 
stretching exercises; 8-10 minutes in length) with content written at the 4th grade level or below. Participants 
will receive eight brief (5- 10 min) telephone contact from the coach (see schedule Table 5) to determine if they 
received the information and if they had any questions about the materials. The schedule will mirror that of the 
intervention group. .Coaches will not provide counseling or goal setting but may clarify terms or concepts. 
Participants in the healthy living control group will be instructed not to change their sleep behavior. They will be 
eligible to receive the sleep intervention at the end of the study. After the final follow-up session, they will 
receive a Fitbit wearable fitness tracker.   
 
C.2.9 Measures. Self-report questionnaires will be used to obtain demographic, literacy, health 
information, menopausal status, food intake, psychological, behavioral and self-management characteristics 
(Table 6).  
C.2.9a Primary outcome and additional glycemic measures: Glycemic assessment. 1.) Glycemic 
control will be assessed using hemoglobin A1C (A1C; Home Access Health Corp/Everlywell). A1C is a gold 
standard marker of glycemic control in T1D reflecting average glucose levels in the previous 90 days. 2) 
Glycemic variability using CGM (blinded) will be conducted using FreeStyle Libre Pro glucose sensor or 
Dexcom (FDA-approved). The sensor tracks glucose concentrations in the interstitial fluid over 24 h using a 
thin flexible filament (< 0.4 mm thick) inserted into the upper arm (5 mm depth). The system captures glucose 
levels every 1 minute and records the data every 15 minutes. Variables we will derive from the CGM are mean 
glucose level, standard deviation (SD), coefficient of variation (CV), percentage of time spent in range (70-180 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
12
mg/dL), percentage of time < 70 mg/dl, and percentage of time spent ≥ 180 mg/dl.94,95 Interstitial glucose 
measurements with FreeStyle Libre and Dexcom were found to be accurate compared with capillary blood 
glucose reference values with a mean absolute relative difference (MARD) of 12% and 9% respectively 
compared to the gold standard YSI measure of blood glucose.96 Accuracy of 10% MARD have been approved 
for self-adjustment of insulin doses in clinical practice.97 Glycemic measures will be obtained for one week at 
baseline and Weeks 6, 12, and 24. In our pilot study we experienced a 10% sensor failure rate (less than 15% 
failure rate reported by the manufacturer and may be due ot the team’s expertise in placing these devices). 
Most sensor failure was due to dislodgement. If dislodgement occurs, we will overnight mail a new sensor to 
the study participant. The dislodged sensor retains the recorded data. Participants will be instructed to return 
the sensor. We anticipate a 24-hour disruption in data collection with dislodgement.  
C.2.9b Wrist actigraphy to measure behavioral sleep Participants will wear an Actiwatch Spectrum Plus 
(Respironics, USA) on their non-dominant wrist for one week during baseline assessment, and Weeks 6,12 
and 24. Data will be collected in 30-sec epochs. Actiwatch records activity, light exposure, and wear time. 
Subjects will be asked to keep a daily sleep log and press an event marker on the Actiwatch at bedtime and 
wake-up time. Data will be downloaded and reviewed with each participant to clarify inconsistencies when the 
Actiwatch is returned. Rest intervals will be set using reported try-to-fall-asleep times and wake-up times on 
daily sleep logs or event markers if these times are missing. Using the Immobile Minutes algorithm in the 
Actiware 6 software, we will derive the following variables: sleep onset, sleep offset, sleep duration, sleep 
efficiency (a measure of sleep quality), mid-sleep time (time point between sleep onset and wake time), and 
standard deviation (SD) of sleep duration, an indicator of sleep variability which we previously showed to be 
related to glucose metabolism.2  
C.2.9.c Secondary outcomes: Diabetes distress, self-management behavior, quality of life. Diabetes 
distress will be measured with the Type 1 Diabetes Distress Scale.98 This 28-item, 6-point Likert scale 
measures 7 subscales (powerlessness, management distress, hypoglycemia distress, negative social 
perceptions, eating distress, physician distress, and friend/family distress) as well as provides an overall total 
distress scale score. Self-management behavior will be measured with the Diabetes Self-Management 
Questionnaire-Revised (DSMQ-R).99 This 27-item, 4-point Likert scale measures aspects of self-management 
behavior and has questions that are specific to those using rapid-acting insulin. Quality of life will be measured 
with the Diabetes Quality of Life Scale (DQOL), a 46-item, 5-point Likert scale that measures 4 subscales 
(satisfaction, impact, social/vocational worry and diabetes-related worry).100 The scales chosen have strong 
psychometric properties and have been validated in T1D populations.  
C.2.9.d Important patient-related variables. Self-efficacy, anxiety, fear of hypoglycemia, mood, fatigue, 
sleep quality, and daytime sleepiness will be measured with validated instruments: Self-Efficacy for Diabetes 
Scale,101 General Anxiety Disorder – 7-item (GAD-7),102 Hypoglycemia Fear Scale II,103 Center for 
Epidemiological Studies Depression Scale (CES-D),104 PROMIS Fatigue Scale,105 Pittsburgh Sleep Quality 
Index,106 and Epworth Sleepiness Scale.107 Food intake and timing will be measured by 24-hour dietary recall. 
Because menopausal status can affect sleep, we will use the STRAW+10 (Stages of Reproductive Aging 
Workshop+10) criteria for staging menopause for women aged 40 and over.108 Physical activity will be 
obtained by activity counts from actigraphy recordings.  
 
Table 6 Measures 
 
 
Variables 
Measures 
Frequency 
Demographic, health, and 
literacy information, 
caffeine and sleep aid use 
Demographic, health questionnaire, menopausal status, Clark Scale, Health Literacy 
Screener (Newest Vital Sign),109 Menopausal status (STRAW+10 in women≥40 
years108, caffeine, sleep aid use 
Week 0 
Subjective sleep indices 
Sleep quality (Pittsburgh Sleep Quality Index),106 Epworth Sleepiness Scale107  
Week 0 
Objective sleep Indices 
Sleep duration, sleep efficiency, sleep variability, sleep and wake timing (Respironics 
Actiwatch Spectrum Plus®). Confirmed with sleep diary (bedtime, disruptions, wake 
time). 
Weeks 0, 6,12, 
24 
Primary measures: 
Glycemic Indices 
    Glycemic control  
    Glycemic variability 
 
A1C 
CGM (Abbott Libre®; or Dexcom®): glucose variability: coefficient of variation (CV%), 
time spent in range94,95 
 
Weeks 0, 6,12, 
24 
 
Secondary measures:  
   Diabetes distress 
   Diabetes  
   Self-Management 
   Quality of life 
 
T1D Diabetes Distress Scale98 
 
Self-Management Questionnaire-R99 
Diabetes Quality of Life Scale (DQOL)100 
Weeks 0, 6,12, 
24 
Important Patient-Related 
Variables 
Self-Efficacy for Diabetes Scale,101 General Anxiety Disorder – 7-item (GAD-7).102 
Hypoglycemia Fear Scale II,103 Center for Epidemiological Studies Depression Scale 
Weeks 0, 6,12, 
24 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
13
(CES-D),104 PROMIS Fatigue Scale,105 24-hour dietary recall, activity counts from 
actigraphy recordings. 
Participant Engagement 
Number of sessions attended, length of coaching sessions, lessons viewed, Fitbit 
usage  
Weeks 1-12 
 
C.2.10 Data Analysis 
Statistical Analysis 
We will conduct descriptive analyses of measured variables, evaluate associations among variables, and 
changes over time. This review of the data will allow us to identify covariates (e.g. age groups, treatment 
regimen) that may require statistical adjustment in the final models. We will conduct exploratory analyses with 
baseline cross-sectional data using our de-identified data which may include emerging approaches to capture 
and analyze sleep and glucose metrics in this rapidly evolving area of research.   
 
Our overall statistical approach will be a mixed-effect model for repeated measures (MMRM), 
recommended for primary analysis of clinical trials with continuous endpoints.110 Conceptually, MMRM is a 
more advanced version of repeated-measures ANOVA that can account for the correlation among multiple 
measurements over time, yet there is no assumption that participants are measured at every time point. 
Therefore, these models are very flexible at handling missing data. Participants who have a missing 
observation are not excluded; thus, this produces the intention to treat (ITT) full information maximum 
likelihood (FIML) model. While missing data will be minimized through careful procedures, some missing data 
are inevitable with longitudinal studies. We will handle missing data using the FIML approach that is 
appropriate for data missing at random.111 We will use inclusive models with auxiliary variables related to 
missingness among covariates collected at baseline, if needed, to support the missing at random 
assumption.112 Multiple imputation will be considered if excessive data are missing among predictor variables, 
e.g., change in sleep parameters for Aim 3.112 Sensitivity analyses such as pattern mixture models will be 
employed if data are suspected to be missing not at random.113  
 
Aim 1: Determine the effect of Sleep-Opt, compared to a healthy living attention control group, on the 
primary outcomes of sleep variability, sleep duration and glycemic control (A1C). 
We will conduct MMRM using change from baseline for our primary outcome regressed onto categorical 
fixed effects for treatment arm, time, their interaction, and the initial baseline measure of the outcome. We will 
use an unstructured covariance structure to model within-person errors. If convergence problems occur, we will 
select the best fitting model from among several options, including random coefficients with residual 
covariance patterns such as autoregressive or exchangeable structure.110,114 In addition to A1C, we will 
estimate separate models for parameters from continuous glucose monitoring (glycemic variability, TIR). Sex 
will be included as a covariate and tested for moderation of the treatment effect. We will also control for BMI, 
A1C (for other glucose measures), and method of insulin delivery. If treatment arms are found to differ in the 
distribution of baseline characteristics despite randomization we will conduct sensitivity analyses including 
these variables as covariates. The primary endpoint will be change differences between groups at 12 weeks, 
based on least square means using a two-sided test with α = .05. We will also assess differences in change 
from baseline to the 24-week endpoint to assess sustainability of effect.  
Aim 2: Determine if Sleep-Opt will result in improved psychological and behavioral secondary outcomes, 
including diabetes distress, diabetes self-management behavior, and quality of life (QoL).  Secondary 
outcomes will be analyzed with the same approach used in Aim 1. 
Aim 3: Determine the contribution of changes in sleep variability and sleep duration during the intervention 
to changes in glycemic parameters (A1C, glycemic variability, TIR).  
Sleep-Opt is designed to reduce sleep variability and extend sleep and duration, and we expect these 
changes to mediate change in glycemic control. In the context of the Aim 1 models, we will add time-varying 
sleep parameters, taking into account the average levels per person and the variation at each time point, to 
understand contributions of between- and within-person differences. We will also examine additional covariates 
predicting sleep parameters, because level and change in sleep parameters (while influenced by randomized 
treatment arm) are not experimentally controlled.115 In addition, we will test moderation of within-person 
mechanisms by sex, distress, method of insulin delivery, and A1C level using interaction terms. Successful 
completion of this aim will inform how aspects of sleep are related to the various aspects of glycemic control in 
general; which glycemic control parameters show reactivity to within-person fluctuations in sleep; and which 
personal characteristics may be more associated with this reactivity. This will explicate key mechanisms of 
change and suggest who may benefit most from sleep optimization. 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
14
 
As an additional statistical analysis we will explore the association of actigraphy-derived light and activity with 
sleep, glucose, and participant characteristics as ambient light may function as a covariate.  
 
Statistical Analysis Addendum  
Prior to data analysis the following are revisions were made to the analysis plan. 
Multiplicity.  The original proposal used alpha=.05 for 3 outcomes in the primary aim.  We will use this 
threshold for all primary and secondary outcomes and provide a post hoc description of familywise error (e.g., 
families: sleep, CGM, psychosocial outcomes) using Holm’s procedure.116 
 
Missing data approach.  We will conduct our models for aims 1-3 (focal models) using an inclusive 
strategy incorporating extraneous auxiliary variables to support a conditionally missing at random assumption.  
We will examine missing data for all variables in our focal models.  For variables with substantial missingness 
(5% or more) we will create a group indicator for missingness (yes, no)  and examine effect sizes for 
extraneous baseline variables, considering those with effect sizes of >|.20| as being related to missingness.  
We will also consider extraneous baseline variables with partial correlations >|.30| with the variables with 
substantial missing.   We will prioritize variables meeting both criteria, then those meeting only the partial 
correlation criteria.  Variables only related to missingness will not improve the model and will not be considered 
further.117  Auxiliary variables will be included as additional outcomes in a way that does not affect 
interpretation of focal variables.  If missing not at random is suspected, we will conduct pattern mixture or 
selection model approaches as sensitivity analyses.  All models will be tested using BLIMP 3 software using 
Markov chain Monte Carlo estimation; we will report Bayesian median point estimates and 95% credible 
intervals for treatment effect estimates for previously stated models (i.e., MMRM) for aims 1 and 2 and 
mediation (indirect) effects from path models for aim 3 (see below).   
 
Intention to treat and per protocol.  Modeling for aims are based on the intention to treat principle 
retaining all individuals in their randomly assigned treatment arm.  We will conduct per protocol models if more 
than 5% of participants did not adhere to the protocol, defined as completing at least 80% of sessions.  In 
these models, we will assess for correlates of engagement and include them as control variables in models 
excluding the participants who did not complete the protocol. 
 
Models for aims 1 and 2 will provide valuable insight into the potential moderators and timing of change in 
participants.  Aim 3 modeling is revised to use a path analysis approach to test separate parallel multiple 
mediator models for glycemic outcomes (e.g., A1C, time in range).  The indirect effects will be tested with 
Bayesian 95% credible intervals.  Correlates of sleep improvement will be used in these models as control 
variables and explored as moderators of the mediated effect. 
 
C.2.11 Subject Retention Strategy. We anticipate a 17% attrition rate. We expect to randomize  up to 170 
subjects to obtain complete data on 120 subjects. This estimate is based on our previous work. All efforts will 
be made to retain participants and reduce burden of participation. Assessment visits and coaching calls will be 
flexibly scheduled according to participant needs. Parking and/or transportation costs will be reimbursed. 
Participants will be allowed to mail back actigraph and CGM devices to reduce office visits. Participants will 
receive $100 for participation, incrementally paid across visits. Participants will also be allowed to keep the 
Fitbit device as an additional incentive for program completion. Those in the attention control group will receive 
a Fitbit at the end of the study. Parking will be provided for anyone who chooses to complete their data 
collection visits in-person.  
 
C.2.12 Treatment Fidelity Strategy All coaching sessions (Sleep-Opt and Healthy Living attention control) 
will be recorded for training and fidelity monitoring (audio or videorecording). All coaches will be trained until 
they reach a criterion of adherence to the treatment protocol before being assigned study participants. Each 
quarter, Dr. Duffecy will review approximately 10% of conducted sessions quarterly and code them for 
adherence using previously developed rating scales.90 Items include aspects of the session content (e.g., 
eliciting questions or problems about the intervention, setting goals), structure of the session (e.g., sets an 
agenda), and therapeutic process (e.g., uses open-ended questions, appropriate session pacing). Control 
sessions will be reviewed for the presence of sleep-related content or health advice. If any sleep or health 
advice (other than direction to the source website) is provided, this will trigger re-training. Drs. Duffecy and 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
15
Quinn will provide bi-weekly supervision to the coaches for their respective groups, providing back data 
regarding treatment fidelity as well as troubleshooting any clinical issues. A manual of operations will be 
developed, and staff will be trained on study procedures. All interactions with participants will be scripted when 
possible. Protocol violations will be reviewed by the staff and reported to the IRB. All interactions by staff with 
participants will be logged, and emails will be archived.  
 
 
C.2.13 Study Timeline (See Table 11).  
Table 11. Study Timeline 
Year 1 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Cumulative 
Number of 
active 
Subjects 
Study set-up 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
 
 
 
 
24 
*Sleep-Optimize subjects 
 
 
 
 
 
 
2 
2 
2 
2 
2 
2 
*Control subjects 
 
 
 
 
 
 
2 
2 
2 
2 
2 
2 
Total active subjects 
 
 
 
 
 
 
4 
8 
12 
16 
20 
24 
Total subjects YEAR 1 
 
 
 
 
 
 
4 
8 
12 
16 
20 
24 
Year 2 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
 
 
 
 
72 
*Sleep-Optimize subjects 
2 
2 
2 
2 
2 
2 
2 
1 
1 
2 
2 
2 
*Control subjects 
2 
2 
2 
2 
2 
2 
2 
1 
1 
2 
2 
2 
Total active subjects 
24 
24 
24 
24 
24 
24 
24 
22 
22 
24 
24 
24 
Total subjects YEAR 2 
28 
32 
36 
40 
44 
48 
52 
56 
60 
64 
68 
72 
Year 3 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
 
 
 
 
116 
*Sleep-Optimize subjects 
2 
2 
2 
2 
2 
2 
2 
1 
1 
2 
2 
2 
*Control subjects 
2 
2 
2 
2 
2 
2 
2 
1 
1 
2 
2 
2 
Total active subjects 
24 
24 
24 
24 
24 
24 
24 
22 
22 
24 
24 
24 
Total subjects YEAR 3 
76 
80 
84 
88 
92 
96 
100 
102 
104 
108 
112 
116 
Year 4 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
 
 
 
 
144 
*Sleep-Optimize subjects 
2 
2 
2 
2 
2 
2 
2 
 
 
 
 
 
*Control subjects 
2 
2 
2 
2 
2 
2 
2 
 
 
 
 
 
Total active subjects 
24 
24 
24 
24 
24 
24 
24 
20 
16 
12 
8 
4 
Total subjects YEAR 4 
120 
124 
128 
132 
136 
140 
144 
 
 
 
 
 
Year 5 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
 
 
 
Data cleaning 
X 
X 
X 
 
 
 
 
 
 
 
 
 
Data analysis 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
Manuscript preparation 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
Final study reports 
 
 
 
 
 
 
 
 
 
X 
X 
X 
Total active subjects 
*Newly enrolled subjects 
 
 
 
 
 
 
 
 
 
 
 
 
 
C.2.14 Potential Study Problems and Alternate Approaches. 1) Changes in diabetes treatment regimen 
is a potential influencer in glycemic control during the study period. The participants in the study will receive 
diabetes treatment per the usual care by their physicians. We will collect the modalities of treatment, changes 
in treatment, and totally daily insulin doses as covariates when analyzing the primary outcomes. 2) Design 
consideration regarding the length of the intervention and A1C measurement: The intervention was designed 
for a12-week period to achieve the best possible outcomes based on previous behavioral interventions and 
feedback from pilot study participants.59 3) The intervention requires smartphones; this could limit the 
accessibility to non-smartphone users. However, the number of smartphone users has grown significantly in 
the last decade. It was estimated that 77% of US adults owned a smartphone in 2016, up from 35% in 2011, 
making the smartphone one of the most quickly adopted consumer technologies.118 This number is expected to 
grow; thus, smartphone accessibility will become less of a limitation in the near future. 4) Obstructive sleep 
apnea is screened by questionnaire and not the gold standard polysomnography in this study. It is possible 
that subjects with sleep apnea will be included in the study. However, this will not pose harm to the subjects, 
and sleep apnea and sleep duration independently contribute to glycemic control.3,119 5.) In our pilot study we 
experienced a 10% sensor failure rate. (This is less than that reported by the manufacturer and may be due to 
the research team’s expertise in placing these devices). Most sensor failure was due to dislodgement by the 
participant. If dislodgement occurs, we will overnight mail a new sensor to the study participant. The dislodged 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
16
sensor will retain the recorded data. Participants will be instructed to return the sensor. We anticipate a 24 hour 
disruption in data collection with dislodgement.  
 
C.2.15 Future Directions. Once the intervention is determined beneficial, future directions include 
deploying sleep optimization to other age groups in T1D, as well as other population with dysglycemia, such as 
those with type 2 diabetes and gestational diabetes. In the future, using automated prompts with or without 
human interaction could increase the scalability of this approach. Sleep optimization could be incorporated as 
a component of standard medical care of T1D.  
 
3.1  Protection of Human Subjects 
 
1. Risks to Human Subjects 
 
a. Human Subjects Involvement, Characteristics, and Design 
Study Design. The purpose of this study is to determine the efficacy of a T1D-specific sleep optimization 
intervention (Sleep-Opt) on the primary outcomes of glycemic control (A1C), sleep variability and sleep 
duration; and secondary outcomes: other glycemic parameters (glycemic variability, time in range); diabetes 
distress, self-management behavior, quality of life (QoL) and other patient reported outcomes in working-age 
adults with T1D and habitual high sleep variability or short sleep duration. The human subjects involved in this 
research will be working-age adults, 18-65 years with type 1 diabetes (T1D) who report habitual sleep 
variability (sleep duration SD > 1 hour per week) or sleep duration <6.5h/night during the work days with a 
desire to improve sleep. To achieve these aims, we will conduct a randomized controlled trial comparing a 
sleep intervention to a healthy living attention control group.  
 
Study population. We plan to enroll up to 300 subjects. We anticipate a 40% screen failure rate and expect to 
randomize up to 170 for a final sample of 120 subjects with complete data. Inclusion criteria include 18-65 
years of age, with a clinical diagnosis of T1D for at least one year, report habitual sleep variability (1 hour/week 
or more) or sleep duration <6.5h/night during the work days or week days (confirmed with actigraphy), with a 
desire to sleep longer, and who own a smartphone compatible with Fitbit. Exclusion criteria include insomnia 
symptoms defined as severe insomnia symptoms as assessed by the Insomnia Severity Index (score ≥ 15), 
being at high risk for obstructive sleep apnea as assessed by Stop  Questionnaire,82 a history of severe 
hypoglycemia (defined as hypoglycemic episodes that result in loss of consciousness within the last 6 months, 
seizures, or requiring help from others), A1C > 10%, rotating shift or night shift work, use of sleep 
medications/aids, significant renal impairment (estimated glomerular filtration rate < 45 ml/min/1.73 m2), 
significant medical morbidities such as congestive heart failure, cirrhosis, chronic obstructive pulmonary 
disease requiring oxygen, active treatment for cancer, depression (PHQ8 > 10), history of stroke with 
neurological deficits, breastfeeding, pregnant or planning pregnancy.  
Convenience sampling will be used to recruit subjects throughout the Chicago metropolitan area. We expect 
that approximately 20% of participants will be recruited from the University of Illinois Medical Center and the 
remaining from the Chicago metropolitan area. This number is based on our previous study recruitment 
outcomes. Participants will be randomized to the intervention group or an attention control group to minimize 
bias. Randomization will be computer-generated in permuted blocks of 4, arranged in random order and 
stratified by sex. The randomization model will be developed by the study statistician (Dr. Steffen) and will be 
executed through the REDCap data management system. 
 
Site/Setting. Subjects’ home is the study site. All study materials will be mailed to subjects’ home and subjects 
and study staffs will meet via video conference for orientation to the study procedures and instruct subjects 
how to use the study materials. During the study period, subjects will continue to receive their usual diabetes 
care with their health care provider. Subjects will be encouraged to discuss any questions regarding blood 
glucose management with their diabetes health care provider.  
 
b. Study Procedures, Materials, and Potential Risks 
Study Procedures 
The sleep optimization intervention employs a wearable sleep tracker (Fitbit), 8 weekly online lessons (over a 
12-week period), an interactive smartphone application, and 8 brief telephone counseling sessions tailored to 
T1D. The intervention will address three specific aspects: sleep variability, sleep duration, and TID-related 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
17
sleep issues such as nocturnal hypoglycemia. Those randomized to the healthy living attention control group 
will receive 8 weekly online healthy living information lessons (over a 12-week period), and 8 brief phone calls 
from a health education coach (equal in time and attention to the intervention group). Participants in both 
groups will be evaluated for sleep parameters, primary outcomes of glycemic control, sleep variability and 
sleep duration, and secondary outcomes (glycemic variability, time in range, diabetes distress, self-
management behavior, QoL and other patient reported outcomes) at baseline (Week 0), Week 6 (mid-
treatment), Week 12 (end of treatment), and Week 24 to test the sustainability of the intervention. 
 
 
 
Sources of Materials 
Data will be collected from study subjects only for research purposes as follows: (1) self-reported data from 
questionnaires and the most recent estimated glomerular filtration rate (eGFR), height, and weight from health 
provider; (2) a fingerstick blood for A1C and urine for pregnancy; (3) continuous glucose monitoring (CGM) 
from a glucose sensor placed under the skin; (4) wrist actigraphy; (5) Intervention data from the Fitbit and other 
technologies, as well as coaching calls.  
1. Self-reported data will include information collected: (a) during recruitment to establish study 
eligibility and (b) demographic (age, sex, race, ethnicity), health (diabetes duration, comorbidities, 
menopausal status, use of sleep aids and caffeine, eGFR, height, weight), key study variables 
(diabetes distress, self-management behavior, quality of life); and related patient-reported 
outcomes (anxiety, mood, fatigue, subjective sleep, daytime sleepiness, self-efficacy, hypoglycemic 
fear, 24 hour dietary recall) collected from questionnaires.  
2. Specimens: A1C determined from a fingerstick blood  by using standardized procedures. 
Pregnancy (to confirm eligibility) will be determined from a urine sample using standard procedures. 
All specimen collection will be conducted by subjects instructed by a trained member of the study 
personnel using standards for blood borne pathogens.  
3. Continuous interstitial glucose levels to calculate glycemic variability and time in range will be 
obtained from a CGM sensor placed under the skin using standard procedures by trained study 
personnel. The sensor transmits the glucose levels to a receiver (the size of a quarter) that the 
subject wears for one week.  
4. Sleep parameters obtained through actigraphy (size of a wristwatch worn on non-dominant wrist) 
will include sleep duration, sleep efficiency, and sleep timing. Standard deviations of these 
parameters will be calculated and used to reflect sleep variability.  
5. Program evaluation obtained from questionnaires and interview.  
6. Data collected via the intervention, including frequency and length of use of technologies and 
coaching data 
7. Data to evaluate program outcomes will include (1) recruitment (number recruited, screened, 
eligible, consented) and (2) retention (attendance and completion rate records) maintained by study 
personnel.  
8. CCTS UIC-Circle data on UI Health patients that may be eligible to participate in our study. This 
data will be used to mail invitations to participate (See Section C.2.4). The data elements provided 
by the CCTS will include full name, age, gender, race, ethnicity, address, phone number, and email 
address if available. Once received from the CCTS the data will be stored securely in REDCap and 
a Box-Health folder. 
 
Potential Risks 
The potential risks to subjects include:  
1. Bleeding, irritation, discomfort, or infection at the fingerstick site and CGM insertion sites. The risk for 
these problems is very minor and is minimized by following standardized procedures using trained 
study personnel.  
2. Loss of confidentiality. Standard procedures will be used to avoid breaches in confidentiality. We expect 
this risk to be low.  
3. Completing self-report measures on diabetes distress and mood may cause subjects some emotional 
distress.  
4. Risks associated with the intervention may include irritation due to wearing the Fitbit or actigraphy 
devices.  

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
18
5. Loss of privacy. Private locations will be used to collect data at all times.  
 
No other risks are currently identified. 
 
2. Adequacy of Protection Against Risks 
 
a. Informed Consent 
Subjects (18-65 years) will be recruited from a Chicago metropolitan area university medical center, diabetes 
clinics, Research Match, local diabetes websites, and organizations, using flyers, e-announcements, and 
recruitment letters and emails. Potentially eligible interested subjects will be screened by trained study 
personnel for inclusion and exclusion criteria. Written informed consent will be obtained via online (REDCap) 
prior to performing any research procedures. The informed consent process will begin when potential subjects 
are contacted. The researcher will explain the study purpose, procedures, benefits, risks, confidentiality, and 
research subject’s rights. After all questions have been answered and the subject verbally agrees to 
participate, written consent will be obtained at the via online (REDCap). A copy of the signed consent will be 
provided to the subject either mail and/or email.  
A waiver of informed consent will be requested for the purpose of identifying eligible patients for recruitment 
from the University of Illinois Health System data base.  
 
b. Protections Against Risk 
Methods to minimize risk include: 
1. Minimization of bleeding, irritation, discomfort, or infection at the fingerstick site (e.g. A1C) and 
insertion site (for CGM). The risk for these problems is very minor and is minimized by following 
standardized procedures. CGM placement and fingerstick will be done using sterile technique and 
standardized procedures. These will be performed by subjects instructed by trained personnel. 
Subjects will be instructed to observe their CGM insertion site daily and contact the research 
personnel if there are signs of bleeding, irritation, redness, or pain.  
2. Loss of confidentiality. Strict procedures will be put into place to minimize the risk of breach of 
confidentiality. All study staff will be trained on methods of maintaining subject confidentiality. 
Subjects will be assigned a unique code number. A master list that links the subject identity to the 
data will be kept by the principal investigator (PI) and stored in a locked office separately from the 
data. Data storage: All data will be stored and analyzed by code number. This consists of coded 
questionnaires, A1C results, actigraphy, and CGM recordings. The coded data will be entered into a 
password-protected computer with a secure server for analysis. Hard copy (paper copy) data (blood 
draw results) will be stored in a locked office. Questionnaire data will be collected using Research 
Electronic Data Capture (REDCap). No identifiers (except subject ID) will be included. Only 
members of the research team will be able to access these data. Procedures during data 
collection: Privacy will be provided during recruitment by screening potential subjects in a private 
setting. The screening data that contain personal identifiers will be kept separate from the coded 
study data and stored in a locked office. All data collection and study procedures will take place in a 
private location. All data collected via the intervention (e.g., internet intervention materials, data 
entry, etc.) will be transmitted using Transport Layer Security (TLS) encryption to prevent 
eavesdropping and tampering with information while it is in the transmission pipeline. To prevent 
unauthorized access to internet sites, they will be password-protected.  
3. Because people with T1D are always at risk for hypoglycemia, study staff will instruct study 
participants to have fast-acting carbohydrate available should a they develop hypoglycemia during 
a meeting session (either intervention or attention control group).  
4. Completing self-report measures on distress and mood may cause subjects some emotional 
distress. The risk is minimal. In our previous studies with the same population, no subjects reported 
emotional distress with these measures. However, study staff will be aware of the possibility and 
address any concerns that are voiced.  
5. Should a subject require medical or other professional intervention due to an adverse event or 
illness, treatment may be obtained through the UIC Medical Center, the subject’s regular doctor, or 
the treatment center or clinic of their choice. Subjects will be provided contact information for the PI 
should they want to talk to her about their illness or injury.  

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
19
6. 
To prevent coercive contact from the application and coach, all email outreach is pre-scripted, and user 
contact information is never revealed. Outreach emails will state that the coach has noticed they have not worn 
the Fitbit in a few days and ask if they would like troubleshooting help or to return to the program. It will not 
include any coercive language or pressure. Email communication will be limited to pre-scripted content 
reminders and participant outreach.  
7. 
Participants will be informed that they may discontinue the intervention at any time. If an adverse event 
is experienced, participants will be instructed to contact Dr. Duffecy or Dr. Quinn. Phone numbers will be 
provided through which one of the investigators can be reached 7 days/week, 24 hours/day. 
 
c. Vulnerable Subjects 
Vulnerable subjects will not be included in the study (fetuses, neonates, pregnant women, children, prisoners, 
institutionalized individuals or others who may be considered vulnerable populations). 
 
3. Potential Benefits of the Proposed Research to Human Subjects and Others 
 
There may be no direct benefits to participating in the study. However, subjects in the intervention group may 
increase sleep duration and experience increased glucose control. The potential in future is that this study may 
advance the knowledge of sleep interventions to extend sleep duration and improve glucose control. The 
possible risks are no greater than those experienced in normal day-to-day life.  
 
3.3 Data Safety Monitoring Plan  
This study will involve working adults (aged 18 to 65 years) with type 1 diabetes (T1D) who experience high 
sleep variability or insufficient sleep duration. We plan to enroll  up to 300 subjects for a final sample size of 
120. Subjects will be enrolled and will complete a 1-week run-in period to collect baseline data, actigraphy to 
measure sleep, and continuous glucose monitoring (CGM) to calculate baseline glycemic variability. Following 
the run-in period, eligible subjects will be assigned to one of two groups: attention control group = 12 weeks or 
intervention group = 12 weeks.  
The risk associated with this study is minimal for the interventions and data collection methods employed in the 
study. The potential for a serious adverse event is very low. Drs. Martyn-Nemeth, Reutrakul, and Quinn are 
experts in the clinical care of people with diabetes. Dr. Steffen has extensive experience with advanced 
statistics. Dr. Martyn-Nemeth will assure that participants are fully informed and consent freely, and Drs. 
Martyn-Nemeth and Steffen will monitor the safety of the study as described below.  
 
Monitoring Plan 
The PI, Dr. Martyn-Nemeth, will be the primary monitor and a Data Safety Monitoring Committee will share the 
responsibility of monitoring the data.  
• 
A Data Safety Monitoring Committee (DSMC) will be appointed to provide oversight and monitoring of 
the data on an annual basis. The DSMC will consist of: (1) Mary Kapella, PhD, RN, Associate Professor 
Emeritus and past Director of Sleep Center, Department of Biobehavioral Health Science, College of 
Nursing; (2). Ulf Bronas, PhD, Associate Professor, Biobehavioral Health Science, College of Nursing;  
(3) Dr. Eileen Collins, PhD, Dean, College of Nursing,  (4) Terry Unterman, MD, Professor, Division of 
Endocrinology, Department of Medicine at UIC, and (5) Dr. Chang Park, PhD, statistician, Department 
of Population Health Nursing Science, College of Nursing. Drs. Martyn-Nemeth and Steffen will meet 
with the DSMC annually and will present both a written and verbal report. The report will include a 
summary of cumulative recruitment, randomization, cumulative retention and attrition rate, study group 
demographics, adverse events, and data completeness and quality. A written report of the meeting will 
be compiled that summarizes the review of data and outcomes, as well as any recommendations with 
respect to modification of the protocol.  
• 
Process for Adverse Events. Throughout the study, the PI and Co-Is will monitor the participants for 
adverse events (AEs). Dr. Martyn-Nemeth and study staff will review AEs individually in real time and 
consult with co-investigators as needed. All AEs or unanticipated problems will be reported to the UIC 
IRB and/or study sponsor as required based on the respective policies and procedures.  
• 
Procedures for monitoring study safety. The facility has protocols in place for responding to emergency 
events and serious adverse events. These protocols will be followed for the study. All study personnel 
involved in conducting the research, including recruitment and screening for eligibility, data 
management, and provision of interventions, will receive training that will address: (1) overview of the 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
20
study objectives and procedures, (2) background and training on collecting data free from bias, (3) 
protection of human subjects and confidentiality, bloodborne pathogen training and infection control 
procedures (4) instructing study participants in performing fingersticks and placing CGM sensors using 
infection control procedures, and (5) data and intervention monitoring.  
• 
Procedures for maintaining study compliance. The PI will assure that informed consent is obtained prior 
to performing any research activities, all subjects meet eligibility criteria, and the study is conducted 
according to the IRB-approved research plan. Compliance of regulatory documents and study data 
accuracy and completeness will be maintained through an internal quality assurance process. Quality 
control will include annual data verification and protocol compliance checks, as well as checks for 
missing data by study personnel. Protocol adherence will be monitored by the PI by auditing 50% of the 
cases for compliance with IRB requirements, informed consent requirements, verification of source 
documents, and compliance with the study protocol. To ensure reliability of data entry, a random 
sample of data entries will be reviewed and compared with the raw data, and the results will be 
recorded. An acceptable error rate will be < 0.3%. Results of the audit will be documented. Research 
meetings will be scheduled to assure the quality of the conduct of the research and promote 
communication among study team members and good data management activities.  
 
 
Treatment Fidelity Monitoring 
All coaching sessions (Sleep-Opt and Healthy Living attention control) will be recorded for training and fidelity 
monitoring. All coaches will be trained until they reach a criterion of adherence to the treatment protocol before 
being assigned study participants. Biannually , Dr. Duffecy will review approximately 10% of conducted 
sessions quarterly and code them for adherence using previously developed rating scales.90 Items include 
aspects of the session content (e.g., eliciting questions or problems about the intervention, setting goals), 
structure of the session (e.g., sets an agenda), and therapeutic process (e.g., uses open-ended questions, 
appropriate session pacing). Control sessions will be reviewed for the presence of sleep-related content or 
health advice. If any sleep or health advice (other than direction to the source website) is provided, this will 
trigger re-training. Drs. Duffecy and Quinn will provide bi-weekly supervision to the coaches for their respective 
groups, providing back data regarding treatment fidelity as well as troubleshooting any clinical issues. A 
manual of operations will be developed, and staff will be trained on study procedures. All interactions with 
participants will be scripted when possible. Protocol violations will be reviewed by the staff and reported to the 
IRB. All interactions by staff with participants will be logged, and emails will be archived. 
 
 
 
 
 

 
T1D Sleep Optimization,  Ver 20, 2/7/2025 
 
21
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