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. Author manuscript; available in PMC: 2024 Mar 1.
Published in final edited form as: Clin Nurs Res. 2023 Feb 14;32(3):560–570. doi: 10.1177/10547738231154133

Cognitive Behavioral Sleep Self-Management Intervention for Young Adults with Type 1 Diabetes (NCT04975230)

Stephanie Griggs 1, Estefania Hernandez 2, Pamela Bolton 3, Kingman P Strohl 4, Margaret Grey 5, Sangeeta R Kashyap 6, Chiang-shan R Li 7, Ronald L Hickman Jr 8
PMCID: PMC10249334  NIHMSID: NIHMS1902353  PMID: 36788436

Abstract

The purpose of this study was to explore perceptions of the first dose of a cognitive-behavioral sleep self-management intervention (CB-sleep) among young adults ages 18–25 years with type 1 diabetes (T1D). We used a qualitative descriptive approach to conduct in-depth semi-structured focused interviews with a purposive sample of 16 young adults with T1D, transitioning from adolescence to early adulthood. Interviews were audio-recorded, transcribed verbatim, and analyzed using qualitative content analysis. Participants described their sleep knowledge (previous, new, and additional), sleep health goals, along with barriers and facilitators of the CB-sleep intervention. Based on these results, we suggest CB-sleep is a useful modality with the potential to support sleep self-management in young adults with T1D during this complex life transition. Furthermore, CB-sleep could be incorporated into an existing diabetes self-management education and support program after pilot testing and determining efficacy to improve sleep and glycemic health.

Introduction

Young adults ages 18–25 years are at a high risk of poor sleep health as they move away from their childhood home to transition to early adulthood and either college or their careers (Ji et al., 2021). One-third (32.2%) of young adults in the United States report poor sleep health (sleep duration < 7 hours with high variability) (Peltzer & Pengpid, 2016; Wheaton et al., 2018). Young adults are at a higher risk for short sleep duration due to the demands of social interactions (e.g., social jetlag), the stress of academic or work roles, connectedness via the web and mobile phones, and shifts in circadian physiology, later chronotype—delayed sleep period, coupled with a need to maintain early waketimes for work or school (Owens, 2014).

Poor sleep health is more prevalent in young adults with type 1 diabetes (T1D) due to the need to manage a complex chronic condition throughout the day and night (Ji et al., 2021; Saeedi et al., 2019). Young adults with T1D have more pronounced sleep extension on weekends, spend less time in slow wave sleep, and report sleep that is less restorative than matched control participants without chronic conditions (Amaral et al., 2014; Barone et al., 2015; Jauch-Chara et al., 2008). Also, higher sleep variability is associated with poorer achievement of glycemic targets in adolescents (Patel et al., 2018) and adults with T1D (Larcher et al., 2016) (authors, 2019, 2020, 2021).

Cognitive-behavioral therapy for insomnia (CBT-I) is a multicomponent psychological intervention comprising strategies to target the behavioral and cognitive underpinnings of insomnia (Agnew et al., 2021). CBT-I is the gold standard treatment for addressing clinically significant insomnia with fewer side effects compared to pharmacologic therapy for sleep (Agnew et al., 2021). A goal of CBT-I is to limit sleep opportunities to increase an individual’s sleep drive to improve homeostatic regulation of sleep (Koffel et al., 2015). However, sleep restriction is the main component of CBT-I. There is a negative direct effect of sleep restriction on glucoregulation and insulin sensitivity based on well controlled lab sleep deprivation studies of adults without chronic conditions and in one study of adults with T1D (Donga et al., 2010; Spiegel et al., 1999; Spiegel et al., 2004; Van Cauter et al., 2007). To illustrate, in an experimental hyper insulinemic euglycemic clamp study of 7 middle-aged adults with T1D (mean age 44, SD 7 years), sleep restriction reduced the glucose disposal rate reflecting decreased peripheral sensitivity (Donga et al., 2010). Therefore, sleep restriction may be detrimental to this high risk population of young adults with T1D who are reliant on exogenous insulin.

On the other hand, though interventions on sleep promotion and extension are emerging, it has been demonstrated that extending sleep is feasible in young adults without chronic conditions and adolescents with T1D without evidence of a negative effect on glucoregulation. Extending sleep may therefore be a better solution to improving glucoregulation among adolescents and adults with T1D. In fact, increasing sleep duration by 30 minutes per day led to an improvement in time in range after 3 months in adolescents with T1D (Perfect et al., 2016; Perfect et al., 2018; Perfect et al., 2022). A concern of not using CBT-I may be that sleep efficiency may decrease due to an increased time in bed and potential for exacerbating insomnia symptoms. However, based on evidence from a systematic review and meta-analysis of 14 randomized controlled trials (N = 932) focused on sleep promotion there was a small or nonsignificant effect on sleep duration (35 minutes, 95% CI 8.70, 61.1, p = 0.0009) and efficiency (−0.01, 95% CI −1.03, 1.02, p = 0.99) in adolescents and young adults without chronic conditions (Griggs, Conley, et al., 2020). This meta-analysis illustrated that sleep duration can be increased without a significant effect on sleep efficiency in this specific age group. It is unknown if extending sleep duration over time is sustainable, nor what the long-term impact is on clinical outcomes specifically in young adults with T1D who have unique developmental and social needs.

Only one in eight young adults with T1D achieve glycemic targets (glycated hemoglobin A1C <7.0%), placing a majority at a higher risk for premature micro- and macrovascular complications and a higher symptom burden (Nathan & DCCT EDIC Research Group, 2014). Short and variable sleep magnifies the severity of these risk factors and is associated with endothelial dysfunction (Costacou et al., 2005). Given the association and direct effect observed when shortening sleep or forcing desynchrony on glucoregulation, promoting sleep health through extension with consistency in timing may improve glycemic targets (A1C < 7.0% and >80% time in range) and other clinical outcomes in young adults with T1D.

Young adults with T1D have general as well as T1D specific barriers and facilitators to their sleep health (Griggs, Whittemore, et al., 2020), therefore involving them as key stakeholders during the intervention development process will likely lead to an intervention that is more relevant, attractive, and acceptable (Holzer et al., 2014; Shalowitz et al., 2009). In our previous work we observed associations between better sleep health (timing, efficiency, earlier bedtimes/later waketimes) and achievement of glucose targets as measured by continuous glucose monitors (CGM)– lower glucose variability, more time spent in glucose range, and less time spent in hyper/hypoglycemia (Griggs, Grey, Strohl, et al., 2021; Griggs, Hickman, et al., 2021; Griggs, Strohl, et al., 2021; Griggs, Whittemore, et al., 2020). Thus, the purpose of this study was to explore perceptions of the first dose, a one-hour session, of a cognitive-behavioral sleep self-management intervention (CB-sleep) among young adults ages 18–25 years with type 1 diabetes (T1D).

Methods

Design

We used a qualitative descriptive approach (Sandelowski, 2000, 2010) (Holzer et al., 2014) to engage with and conduct in-depth semi-structured focused interviews with a purposive sample of 16 young adults with T1D (population of interest). We used a series of studies to develop the intervention, including a quantitative descriptive study with another cohort of young adults with T1D (Griggs, Hickman, et al., 2021), a qualitative descriptive study to determine bedtime routines, barriers and facilitators to sleep, and sleep health goals (Griggs, Whittemore, et al., 2020), and a 7-day longitudinal survey study of which this cohort was recruited from (Griggs et al., 2022). The young adults in the current study were purposively selected from a 7-day longitudinal survey study with twice daily sleep diaries. The theoretical assumptions underlying qualitative content analysis are related to methods of communication theory as described by Watzlawick et al. (1967). The axioms of human communication help to explain the researcher-to-participant interaction and facilitate interpretation (Watzlawick et al., 2011). This paper is based on a primary aim to develop the intervention with 10–15 young adults with T1D and data on this sample has not been published previously.

Characteristics of focused-interview study population

The young adults were recruited from an online survey (social media, special group newsletters, peer recommendations, and ResearchMatch) and were: (1) between the ages of 18 to 25 years; (2) diagnosed with T1D for at least 6 months; (3) with no other major health problems (chronic medical/major psychiatric); (4) not currently participating in intervention studies; (5) English speaking; (6) habitually sleeping < 7 hours; and (7) with a self-reported A1C > 7% or CGM derived <80% time in range. In the online survey there were individuals who reported sleeping > 7 hours with A1C < 7% or > 80% time in range. Our aim was to have equal representation of males and females and representation from individuals identifying as gender diverse or racially/ethnically diverse. Those with a previous sleep apnea diagnosis; who were currently pregnant; or working night shift were not eligible to participate. Eligible participants were screened for these criteria through an online screening survey before being sent informed consent and a link to the study survey. We adapted the cognitive-behavioral sleep self-management intervention (CB-sleep) originally developed for adolescents with T1D (Perfect et al., 2016). We We developed the intervention for young adults within this specific age range to capture the unique biological and social needs of individuals as they transition to college or their careers. Young adults with T1D for greater than 6 months were chosen to avoid the confounding effects of the initial adjustment period after diagnosis.

Focused-interview procedure

Following institutional review board approval Case Western Reserve University, participants were screened via a self-report survey, twice-daily sleep diaries for 7 days, and self-reported A1C and/or glucose management indicator (estimated A1C) derived from raw CGM data. Thirty-two young adults were purposively selected from the online survey to provide feedback on the first dose of the intervention and 16 participated (50% response rate). We interviewed everyone who responded to the invitation and scheduled an interview. The Qualitative Content Analysis in Nursing Research Framework as described by Graneheim and Lundman (2004) was used to guide the development of qualitative interview questions as well as the analysis of responses. The flexible interview guide was developed based on quantitative and qualitative data from a previous cohort of 46 young adults with T1D (Griggs, Whittemore, et al., 2020). In this previous cohort from the Northeastern United States, actigraphy derived sleep behavior and CGM-derived glucose data were characterized in this other study. In the second study, exit interviews were conducted to determine perceptions of their sleep routine along with facilitators and barriers to sleep (Griggs, Whittemore, et al., 2020). We used an inductive process for both studies to allow for ongoing modification of the interview guide. The intervention included psychoeducation on T1D and sleep content, review of the participant’s self-reported sleep health data, a sample clinician sleep report generated from research grade actigraphy, recommendations on stimulus control, environmental modification, and basic stress reduction (progressive muscle relaxation and guided imagery exercise). The intervention ended with a recommendation of sleep extension (goal up to 1 hour with a titration of 15 minutes per week). The qualitative data were drawn from responses to the following open-ended questions – Do you think that your sleep could be improved? What did you learn in the presentation? What did you already know about sleep? What did you hope was in the presentation that was not there? Do you have any goals for your health based on the presentation? What, when, where, how often/long/much? /start date? “May I share some ideas with you”? - share 2–3 ideas, “Will any of these ideas or your own ideas work?” How confident or sure do you feel about carrying out your plan on a scale of 0 to 10? Why is it not a (lower number than participant provided)? What are the potential challenges to incorporating the intervention into your everyday life? How could the proposed intervention wok in your everyday life?

Quantitative data were collected primarily through a Research Electronic Data Capture (REDCap) survey. Additional quantitative data ratings on quality (1 to 5) and usefulness (1 to 5) of the intervention were collected in the interview, with higher scores indicating higher quality and usefulness. At the end of the survey, participants were provided electronic gift card incentives, $10 for the baseline survey, an additional $10 for twice daily diaries, and $40 for the interview.

Data analysis

Demographic and quantitative data were analyzed using Statistical Package for Social Sciences (SPSS) version 28 for Mac (SPSS for Mac, IBM, Corp., Armonk, NY, USA). Qualitative content was analyzed and coded for themes using qualitative content analysis (Graneheim & Lundman, 2004; Preiser et al., 2021). We used a constant comparison method for content analysis using Nvivo 12 for Mac (Jackson & Bazeley, 2019). Qualitative responses were coded using an in vivo approach (Thomas, 2006). Data were collected until data redundancy or thematic saturation was confirmed by two independent coders (Thomas, 2006).

Our qualitative approach was conducted in five stages. First, independent coders read each transcript to enhance familiarity with the data and to assess the document as a whole. Second, meaning units were identified and condensed by each coder using directed content analysis (Hsieh & Shannon, 2005). Meaning units centered around new knowledge, previous knowledge, additional knowledge, sleep health goals, and facilitators and barriers of behavior change and of incorporating the intervention. Third, condensed meanings of participant responses were created to help synthesize and collapse emerging themes in the data. Two authors used a conceptual coding method to develop the final coding structure by synthesizing and collapsing the preliminary 168 in vivo codes until consensus was reached (Saldaña, 2016). The final coding structure included 110 mutually exclusive codes. Fourth, two independent coders who were blinded from demographic data coded the transcripts with a strong level of agreement (Cohen’s kappa = 0.824). Codes were collapsed into categories, and categories were collapsed into themes and subthemes. Conceptual mapping and memos were used to help refine themes and subthemes.

Finally, once themes were finalized participant profiles were used to connect themes with responses (Whittemore et al., 2001). Validity in qualitative research criteria (Whittemore et al., 2001) and four main strategies of trustworthiness (credibility, transferability, dependability, and confirmability) were used throughout the systematic process (Lincoln, 1995; Lincoln & Guba, 1985; Nowell et al., 2017). Field notes were taken to reveal any biases, and audio recordings were transcribed verbatim in addition to performing an inter-rater reliability analysis for dependability and reliability. A thick description of methods was reported to a second researcher with an effort made to recruit participants from varied backgrounds to facilitate transferability and external validity. Audit trails, memos, and conceptual mapping were used to ensure objectivity and confirmability (Lincoln, 1995; Lincoln & Guba, 1985; Nowell et al., 2017).

Results

Study population

We present the participant (N = 16) demographic and clinical characteristics in Tables 1 and 2. Young adults with T1D in the current study had a mean age of 20.5 ± 2.1 years, mean BMI of 23.9 ± 4.1 kg/m2, 81.3% were Non-Hispanic White (6.3% Asian and 12.5 % Black), 69% reported female sex assigned at birth, and 12% identified as non-binary. The mean T1D duration was 9.9 ± 5.0 years, A1C was 7.3 ± 1.3% and glucose management indicator (GMI) was 7.7 ± 0.67%. GMI was previously known as “estimated A1C – eA1C” and is derived from raw CGM data.

Table 1.

Demographic Characteristics (N = 16)

Demographic Characteristics
Mean SD
Age (years) 20.5 2.1
BMI kg/m2 23.2 4.0
N %
Sex (% male) 5 18.8
Gender
  Woman 8 50
  Man 5 31.3
  Non-binary 3 11.9
Race/ethnicity
  Non-Hispanic White 13 81.3
  Non-Hispanic Black 2 12.5
  Asian 1 6.3
Able to meet monthly expenses (% yes) 14 87.5
College student (% yes) 9 56.3
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Table 2.

Clinical and Sleep characteristics (N = 16)

Type 1 Diabetes Profile Mean SD
T1D duration (years) 9.9 5.0
A1C (%) 7.3 1.3
GMI (%) 7.7 0.7
N %
Insulin Pump (% yes) 9 56.3
CGM brand
  Dexcom G6, 4 10 62.5
  Medtronic Guardian, 2 2 12.5
  Medtronic Enlite, 1 1 6.3
  Freestyle Libre. 6 2 12.5
  N/A, does not use CGM, 8 2 12.5
Sleep Profile Mean SD
Total sleep time (hours) 7.3 1.3
Sleep efficiency (%) 86.2 13.4
Global PSQI 5.7 3.5
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Note: The Systéme International d’Unités (SI units) conversion for 7% A1C is 53 mmol/mol. GMI glucose management indicator from continuous glucose monitor.

We organized the findings into 5 categories: new and previous knowledge, additional knowledge requested, sleep health goals, barriers of the intervention, and facilitators of the intervention/behavior change (Table 3).

Table 3.

Community Engaged Approach Categories, Themes, and Subthemes

Categories Themes Subthemes
Knowledge (new and previous) Sleep physiology and behavior Time in bed not time asleep, sleep stages, sleep routines
Brain and body response Body temperature effects, changeable body temperature
Sleep environment Room temperature, light, noise, relaxing atmosphere
Restorative health benefits Repairs heart and muscle, reduces motor vehicle accidents
Lifestyle choices Foods with melatonin, alcohol impact on sleep
Additional knowledge Diabetes sleep self-management Diabetes nocturnal complications, devices
Diabetes related fear and stress Fear of hypoglycemia (nocturnal), food effect on glucose
Disparities and background of sleep Racial, cultural, gender, socioeconomic status, residential considerations
Sleep health goals Quantity Sleep longer, avoiding naps (longer nocturnal sleep)
Quality Less tired, higher sleep quality
Hygiene Relaxing sleep atmosphere, avoiding blue light
Timing Consistent bed and waketimes on weekday and weekend, sleep hours, routine
Barriers to proposed intervention Later bedtime preference/earlier waketime Early waketime for work or school
Routine: inconsistent, busy, unexpected Varied schedule (school or work), assignments/tests
External factors Noise, light, device nighttime settings
Facilitators of proposed intervention/behavior change Lifestyle timing Meals, exercise, time management
Bed/wake time Earlier bedtime, set waketimes, waking up naturally
Diabetes self-management Diabetes already on a schedule
Daytime function Less daytime fatigue, napping
Increase self-awareness Perceptions, interactions, benchmarks for achievement
Usefulness and Quality Ratings
Usefulness rating
(out of 5) 		4.5 ± 0.71 80% ≥ 4
Quality rating
(out of 5) 		4.6 ± 0.52 100% ≥ 4
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N = 16, 62.5% female, 20.5 ± 2.1 years, 7.4 ± 1.2 % A1C, 23.9 ± 4.1 kg/m2

New and previous knowledge

Participants described gaining new knowledge and having previous knowledge about sleep physiology and behavior, brain and body response, sleep environment, restorative health benefits, and lifestyle choices. Regarding sleep physiology and behavior, participants knew the approximate hours they should be sleeping or the specific hours: “That I should be getting between seven and nine hours of sleep” (Participant 8, age 18). However, some expressed a lack of knowledge about the differences between time in bed and time asleep, as Participant 1 (age 24 years) stated “I did not know … that your time in bed is not equal to your time asleep”.

Participants reported knowing the general benefits that sleep has on the body (brain and body response) and “that it involves all five senses” (Participant 8, age 18). Less was known about the effect on the brain “Well, I did know that it may affect your attention and how you perceive things and … how you react to things so, not just within the moment or just within the moment of waking up but also how you react throughout the day” (Participant 3, age 21).

Others mentioned learning new ways to either create a better sleep environment or how to adjust their current sleep environment through modifying room temperature, light and noise exposure, using five senses to adjust one’s preferences, along with alternatives to technology use (e.g., reading, journaling, or art). Participants often expressed awareness of the difficulty in maintaining a restful environment as they transitioned into a new sleep environment in college: “I’m in college now but I have a twin sister and we shared a room for a long time and so it would always be like “can you turn that light off?” So, I knew that the amount of light in the room was important” (Participant 5, age 19).

While each participant expressed the belief that sleep supported good health, 22% participants were unaware of the restorative properties sleep has on the human body and overall health. Participants reported learning how good quality and quantity of sleep promotes brain, heart, muscle, eye, and joint health. Others described not knowing about the associated risks insufficient sleep has on higher weight or obesity and motor-vehicle accidents.

Several participants described learning how lifestyle choices, such as those related to technology use, nutrition, and exercise, affected their sleep. For example, some participants mentioned that melatonin supplementation often aids in sleep; however, many were unaware that melatonin can also be found in foods such as cherries, goji berries, pineapple. Alcohol use is known to induce sleep; however, participants were unaware of the effects alcohol and caffeine had on sleep quality and efficiency: “I knew that there were different things that affected sleep like caffeine, but I didn’t know so much about alcohol but I’m 19, I don’t drink, but that was still interesting to learn for future use” (Participant 5, age 19).

Additional knowledge

Additional knowledge requested centered on three central subthemes diabetes self-management, diabetes-related fear and stress, and disparities and background of sleep. Regarding diabetes self-management several participants noted that a degree of flexibility and sacrifice is needed among individuals who need to manage their blood sugar levels throughout the night. A majority of the participants shared knowledge about the importance of maintaining a healthy diet, following a regular exercise and sleep schedule, and having open conversations with their primary care provider about the best insulin treatment plan to accommodate their lifestyle and manage their condition. However, it was expressed that less is known about navigating nightly responsibilities after sleep has been initiated: “The unplanned interruptions but also the planned interruptions…. where you have to sacrifice sleep over your other health.” (Participant 4, age 21).

Nighttime awakenings were described to occur as a result of waking up often to urinate, drink water, or check if their glucose monitor was still actively connected to their cellphone throughout the night. The known possibility of nighttime hyperglyemia and hypoglycemia among young adults was a chronic stressor negatively impacting sleep quality: “I can go to sleep now but I need to wake up in an hour and a half, so I can check my blood glucose” (Participant 4, age 21). While sleep environments and habits may be in place and working, they do not mitigate the nighttime responsibilities required to stabilize blood sugar levels overnight.

An additional focus on the racial-ethnic and socio-economic disparities in the management and outcome of sleep and T1D was requested. Specifically, racial-ethnic and social-economic disparities were noted in the management and outcome of sleep in the management of T1D. As stated by a participant in the study: “An African American who is low income and lives on a low-income side of town with a very big family…. some people have gunshots in their sleep environment whereas some people have water noises, so I think those are big things to look into” (Participant 3, age 21). Participants further expressed that individual experience and definitions of “good quality sleep” varied; specifically, that not all individuals had the financial nor social capital to apply self-care recommendations as they may not align with their day-to-day lives or current living arrangements.

Sleep health goals

All participants were able to generate sleep health goals following the presentation. Young adults wanted to increase or improve their sleep quantity, quality, timing, and hygiene habits. Several participants described adjusting the timing and type of exercise activity (e.g., moderate versus intense exercise) closer to bedtime: “(It’s) better going straight to sleep without having exercised and then getting up in the morning and doing so” (Participant 1, age 24). Others set goals to avoid daytime naps and technology use before bed to promote longer nocturnal sleep and daytime function. Behavioral goals of sleep hygiene included reducing drinking levels prior to bed and maintaining a regular sleep-wake routine throughout the week – including weekends: “I would definitely like to try and make my routine first going to sleep and waking up a little bit more regular like Monday through Friday and Saturday and Sunday” (Participant 4, age 21).

Barriers to the intervention

Young adults described the juxtaposition of later bedtimes and earlier waketimes to accommodate school or work, inconsistency in routines, and other external factors (e.g., family death, relocation) competing with a healthy sleep routine. Several participants stated that they could only adjust their social and physical space to a degree: “even though I’m young I have so much going on, so it feels like there’s always something happening” (Participant 3, age 21).

Late night assignments and early morning classes or work shifts were described to reinforce shorter and more sporadic sleep schedules: “I think more than anything the curveballs that can come around like an odd deadline that you need extra time on, not that you should really sacrifice sleep” (Participant 10, age 21). Dependence on technology, whether for blood glucose monitoring or personal use, was discussed as a direct limitation on sleep hygiene practice. Specifically, T1D self-management technology added an additional exposure to blue light and nighttime awakenings: “You know what’s so interesting about having Type 1 diabetes and what I learned about blue light today? It’s that my devices are electronic so some of it you can’t get away from. I don’t know if you have some of the continuous glucose monitoring arrangements, they have phone integration, so the Bluetooth syncs to the cellphone and a lot of people use their cell phone. I still have a little tiny receiver; it’s got a little light and it’s easier sometimes. So that was kind of interesting on that how it impacts sleep in that way” (Participant 5, age 19). A majority of the young adults in the study discussed the need to frequently work, study, and socialize on devices and how that may often cause a strain – a strain most could not afford to avoid: “in college it’s kind of hard, because you have like a lot of deadlines, you have to do and so much of the stuff is on the computer now “(Participant 7, age 21).

Facilitators of incorporating intervention

Goal facilitation was most visible around five key themes: lifestyle timing, bed/wake time, diabetes self-management, daytime function, and an increase in self-awareness. Adopting the intervention was projected to impact other current patterns including the timing of meals, exercise, and other daily responsibilities (e.g., school and/or work) to accommodate a healthier lifestyle. Participants described adjusting the timing of meals, exercise, and responsibilities (e.g., school and/or work) to help facilitate healthier bed and wake times. For some, it was thought to be easier to adjust a current habit than to create a new one from the start: “honestly prompting myself to change that behavior but I already have some semblance of a routine, so I think it’ll be easier to expand upon that than if I were to be making a new one” (Participant 10, age 21). For others, having the ability to naturally wake up was further encouraged by setting bed and wake times for the week, including weekends. Participants also described the importance of having accountability and achievement benchmarks with a research member along with peer-to-peer support to facilitate behavior change.

Discussion

We adapted a cognitive-behavioral sleep self-management intervention (CB-sleep) originally developed for adolescents with T1D (Perfect et al., 2016) for young adults with T1D. We engaged with a purposive sample to co-develop and provide feedback on the first dose and other components of the intervention. Through this process we learned the importance of assessing baseline knowledge, establishing sleep goals, and anticipated facilitators and barriers of the cognitive behavioral sleep self-management intervention in a population with an intensive self-management regimen. We propose that sleep self-management should be integral to current diabetes self-management education and support programs. The three overarching themes from this study included sleep knowledge (previous, new, and additional requested), sleep health goals, and barriers and facilitators of the intervention/behavior change

The themes from the current study build on previous studies in T1D and in the general population of young adults (Bergner et al., 2018; Jaser et al., 2020; Perfect et al., 2016; Perfect et al., 2022). First, a majority of the young adults in the current study reported knowing the general benefits of sleep on overall health which is consistent with other studies of adolescents with T1D (Perfect et al., 2016; Perfect et al., 2012; Perfect et al., 2022) and young adults with and without T1D (Griggs, Conley, et al., 2020). The largest gap identified by study participants was in how to navigate nightly responsibilities to both initiate and maintain sleep overnight. This finding is supported in other similar studies of both children and adolescents with T1D, where nocturnal self- and family monitoring of T1D is difficult to manage with little support in navigating this condition overnight (Bergner et al., 2018; Jaser et al., 2020; Perfect et al., 2016; Perfect et al., 2012; Perfect et al., 2022). Second, sleep health goals were convergent with another study of young adults with T1D including promoting sleep quantity, quality, hygiene, and bed/waketime (Griggs, Grey, Toly, et al., 2021). The young adults in the current study identified needing additional knowledge to manage diabetes nocturnal complications, but specific diabetes self-management goals related to sleep were not expressed in this study. In the other similar study of young adults with T1D (Griggs, Grey, Toly, et al., 2021), time in range, nocturnal sleep and glucose monitoring, and diet were additional goals not expressed in the current study. It may be that we incorporated some of these aspects into the intervention presentation and thus these specific goals were not a priority. Another possibility for this divergence could be interview guide differences where young adults were asked specifically to identify three health goals in the other study. The goal of this study was to elicit knowledge gained and feedback on a cognitive behavioral intervention tailored for young adults with T1D, therefore we needed to make decisions on structuring an interview without a significant time burden. In future sleep behavioral intervention studies, we recommend requesting at least three health goals from young adults with T1D to identify and work on through the course of the study and beyond.

Third, there were several convergent subthemes related to barriers and facilitators when comparing the young adults in the current study to two studies of adolescents with T1D (Bergner et al., 2018; Perfect et al., 2016). Convergent barriers included external factors and routine (sleep and school schedule) and facilitators included earlier bedtime and setting a bed/waketime (Bergner et al., 2018; Jaser et al., 2020). There were unique T1D specific barriers and facilitators to consider in the next steps of intervention development. Participants in the current study raised awareness about the additional barrier of school and work responsibilities and how their current diabetes self-management behaviors could facilitate the intervention. These findings are not surprising considering the current study sample of young adults with T1D was older than the adolescents with T1D in a comparison intervention development study (Perfect et al., 2016; Perfect et al., 2022). A number of barriers to better sleep were uncovered in the current research and included inconsistent routines, and personal events such as a death in the family or various social events to accommodate the new adult responsibilities of work and school. Other barriers interfered with the young adult with T1D’s ability to consistently have positive sleep hygiene practices. Dependence on technology was a central barrier consistent with previous research as technology is commonly used for blood glucose monitoring or school / personal use (Barnard et al., 2016; Griggs, Whittemore, et al., 2020; Paterson et al., 2019)

Willingness to change behaviors to improve sleep was replicated in other similar studies with participants desiring more regularity in bedtime, having effective sleep, a comfortable sleep environment, feeling tired, and maintaining blood glucose within range during sleep time (Griggs et al., 2020; Patterson et al., 2019). Participants in the current study described concerns about the impact of the social and external environment on their sleep specifically a lack of financial resources and poor community surroundings. Although authors in previous studies have reported a connection between a poorer socioeconomic status and achievement of glycemic targets (Chaturvedi et al., 1996; Mühlhauser et al., 1998; Weatherspoon et al., 1994), less is known about how individual environments among those identifying as racial minorities or those who are socioeconomically disadvantaged impact sleep goal trajectories.

There are several clinical implications nurses should consider when assessing sleep health and habits in young adults with T1D. First, addressing sleep hygiene habits by limiting nocturnal light exposure could be achieved by recommending blue light filters, nighttime screen settings, and limiting screen time 30 minutes to one hour before bed. Second, nurses can ask open ended questions to gain a better understanding of the young adult’s bedtime routine and a typical bed and waketime to determine whether sleep duration is between the recommended 7 to 9 hours (Hirshkowitz et al., 2015). Third, the adolescent to young adulthood transition was described to impact one’s perceived sleep satisfaction, along with next day negative effects on multiple areas of health in the current study. It is therefore important to consider the multiple transitions the young adult is managing as additional stressors when performing a sleep health assessment and evaluating the current treatment plan. Finally, the built environment and the neighborhood the young adult resides may be conducive to or may hinder restful and restorative sleep. Facilitators and barriers to sleeping in these environments must therefore be considered and addressed when working with young adults with T1D to improve their sleep. One simple and economical consideration may be to recommend ear plugs and an eye mask or room darkening shades to promote melatonin secretion (Tähkämö et al., 2019; Wu et al., 2015).

Overall, the intervention facilitated a young adult with T1D’s understanding of sleep concepts, the benefits of quality sleep, and what to consider when planning activities before bedtime. The results should be considered within the context of limitations. Individuals were purposively selected with poor sleep (< 7 h on average or short sleep nights), currently using continuous glucose monitors, not currently achieving targets (A1C or GMI < 7% or >80% time in glucose range 70–180 mg/dL), with a goal to recruit a representative population balanced by biological sex (50% male/female) and from underrepresented racial minority groups. The present sample was mostly Non-Hispanic White (81.3%), female (68.8%), and previous or current CGM use was a requirement of the study. It is estimated that only 30% of the population uses CGM based on national data from 2016–2018 (Foster et al., 2019), limiting the generalizability of the current findings. Thus, we cannot determine racial/ethnic differences in responses nor whether individuals who do not use CGM experience different diabetes specific barriers and facilitators.

Despite these limitations, several strengths should be considered. To our knowledge we are the first team to adapt a cognitive behavioral sleep self-management intervention specifically for young adults with T1D. Young adults with T1D have unique developmental considerations and intensive insulin and monitoring diabetes specific sleep management needs. We structured our intervention to meet the biological and social needs of young adults with T1D. We systematically examined the responses to an intervention with rigorous triangulation methods while engaging with and seeking feedback to ensure relevance, acceptability, and attractiveness for future pilot and efficacy intervention trials. Our findings complement the existing cross-sectional data in a broad range of ages to move intervention development forward for young adults with T1D. Sleep cognitions and behavior are modifiable targets that may improve achievement of glycemic targets.

The approach we used in the current study facilitated the design of a meaningful cognitive behavioral intervention that could readily be implemented into a young adult with T1D’s natural environment. Engaging with the population of interest when developing interventions in research enables academic researchers to increase study validity by improving measurement accuracy. Sleep health like general health is not the absence of sleep disorders or disease and should be addressed and prioritized alongside physical activity and diet in standard diabetes care. Promoting multiple dimensions of sleep health in young adults with T1D, addresses the entire young adult with T1D population rather than only those with sleep disorders. Pilot randomized controlled trials and efficacy trials are needed to demonstrate that improving multiple sleep health dimensions (e.g., increasing sleep duration and improving sleep timing) leads to positive diabetes self-management outcomes in this population.

Acknowledgments and declaration of author contributions:

SG, PI on the grant (R00NR018886), secured the funding, designed the study, collected, analyzed, and interpreted the data, and wrote the manuscript. EH conducted the interviews, checked the transcripts for accuracy, coded the qualitative data, and co-wrote the manuscript. PB coded the qualitative data and co-wrote the manuscript. RLH contributed to the study design, interpreted the findings, and co-wrote the manuscript. KPS, MG, SK, CSRL contributed to the study design, reviewed the content, and critically revised the manuscript. All authors have seen and approved the final version of this manuscript.

Funding:

SG is funded by the National Institute of Nursing Research (NINR) R00NR018886. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH.

Footnotes

Conflict of interest: The authors have no conflicts of interest to disclose.

Ethical statement: The PI obtained institutional review board approval from Case Western Reserve University (STUDY20201829) for this study.

Contributor Information

Stephanie Griggs, Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, Ohio, 44106.

Estefania Hernandez, Case Western Reserve University, Frances Payne Bolton School of Nursing, Department of Anthropology.

Pamela Bolton, Case Western Reserve University, Frances Payne Bolton School of Nursing.

Kingman P. Strohl, Case Western Reserve University, School of Medicine, Cleveland, OH 44106.

Margaret Grey, Yale University, School of Nursing and School of Medicine, West Haven, Connecticut 06477.

Sangeeta R. Kashyap, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44106.

Chiang-shan R. Li, Yale University, School of Medicine, New Haven, Connecticut 06519.

Ronald L. Hickman, Jr, Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, OH 44106.

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