The Development of a Clinically Relevant Sleep Modification Protocol for Youth with Type 1 Diabetes

Psychosocial Determinants of Sleep Behavior and Healthy Sleep Among Adolescents: A Two-Wave Panel Study

To date, it remains unknown which psychosocial determinants identified by several leading behavior change theories are associated with different sleep parameters among adolescents. Therefore, this study investigates whether changes in knowledge about healthy sleep, attitude toward healthy sleep and going to bed on time, self-efficacy to engage in healthy sleep behavior, perceived parental and peer norms, perceived barriers (e.g., worrying, fear of missing out), and perceived support (e.g., bedtime rules, encouragement) related to healthy sleep are associated with changes in adolescents' sleep duration on school days and free days and sleep quality over a period of 1 year. Two-wave data of 1648 Flemish adolescents (mean age = 15.01, SD = 0.65, 46.3% female) were analyzed using linear models. Increased levels of parental social support, positive attitude towards and perceived advantages of healthy sleep, norm-knowledge, and perceived peer behavior were associated with sleep duration, with parental social support having the strongest association. Increased levels of perceived barriers were associated with decreased levels of sleep quality parameters, and increased levels of self-efficacy, positive attitude, and parental modeling were associated with improved sleep quality parameters, with perceived barriers having the strongest association. The current results indicate that behavior change theories are useful in the context of adolescent sleep behavior and suggest that perceived parental support (i.e., bedtime rules) and perceived barriers are most strongly associated with adolescents' sleep duration and/or quality.

A composite measure of sleep health is associated with glycaemic target achievement in young adults with type 1 diabetes

We investigated whether sleep health (each individual dimension and a composite measure) was associated with better glycaemia among a cohort of young adults with type 1 diabetes (mean age 21.5 years, mean body mass index 24.55 kg m-2 ). Multiple validated self-report questionnaires were completed, and raw continuous glucose monitor data were shared. One self-reported sleep characteristic for each of the five sleep health dimensions was selected. A composite score was calculated by summing the number of "good" sleep health dimensions. We evaluated the associations between sleep health and glycaemia, and whether covariates, including age, type 1 diabetes duration and sleep apnea risk, influenced the relationships among the study variables using multivariable linear regression. Individual dimensions of sleep satisfaction (β = 0.380, p = 0.019; β = -0.414, p = 0.010), timing (β = 0.392, p = 0.015; β = -0.393, p = 0.015) and sleep efficiency (β = 0.428, p = 0.007) were associated with higher achievement of glycaemic targets (J-index and time in range); however, these associations did not persist after considering covariates. A better Sleep Health Composite score was associated with higher achievement of glycaemic targets even after considering covariates. Using a multidimensional framework can guide future research on causal pathways between sleep and diabetes health, interventions to target sleep health profiles, and may improve sleep screening in routine diabetes care.

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

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 aged 18 to 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.

Sleep-Opt-In: A Randomized Controlled Pilot Study to Improve Sleep and Glycemic Variability in Adults With Type 1 Diabetes

PURPOSE

The purpose of this study was to evaluate the feasibility and acceptability of a technology-assisted behavioral sleep intervention (Sleep-Opt-In) and to examine the effects of Sleep-Opt-In on sleep duration and regularity, glucose indices, and patient-reported outcomes. Short sleep duration and irregular sleep schedules are associated with reduced glycemic control and greater glycemic variability.

METHODS

A randomized controlled parallel-arm pilot study was employed. Adults with type 1 diabetes (n = 14) were recruited from the Midwest and randomized 3:2 to the sleep-optimization (Sleep-Opt-In) or Healthy Living attention control group. Sleep-Opt-In was an 8-week, remotely delivered intervention consisting of digital lessons, sleep tracker, and weekly coaching phone calls by a trained sleep coach. Assessments of sleep (actigraphy), glucose (A1C, continuous glucose monitoring), and patient-reported outcomes (questionnaires for daytime sleepiness, fatigue, diabetes distress, and depressive mood) were completed at baseline and at completion of the intervention.

RESULTS

Sleep-Opt-In was feasible and acceptable. Those in Sleep-Opt-In with objectively confirmed short or irregular sleep demonstrated an improvement in sleep regularity (25 minutes), reduced glycemic variability (3.2%), and improved time in range (6.9%) compared to the Healthy Living attention control group. Patient-reported outcomes improved only for the Sleep-Opt-In group. Fatigue and depressive mood improved compared to the control.

CONCLUSIONS

Sleep-Opt-In is feasible, acceptable, and promising for further evaluation as a means to improve sleep duration or regularity in the population of people with type 1 diabetes.

Promoting Sleep Duration in the Pediatric Setting Using a Mobile Health Platform: A Randomized Optimization Trial

Objective

Determine the optimal combination of digital health intervention component settings that increase average sleep duration by ≥30 minutes per weeknight.

Methods

Optimization trial using a 25 factorial design. The trial included 2 week run-in, 7 week intervention, and 2 week follow-up periods. Typically developing children aged 9-12y, with weeknight sleep duration <8.5 hours were enrolled (N=97). All received sleep monitoring and performance feedback. The five candidate intervention components (with their settings to which participants were randomized) were: 1) sleep goal (guideline-based or personalized); 2) screen time reduction messaging (inactive or active); 3) daily routine establishing messaging (inactive or active); 4) child-directed loss-framed financial incentive (inactive or active); and 5) caregiver-directed loss-framed financial incentive (inactive or active). The primary outcome was weeknight sleep duration (hours per night). The optimization criterion was: ≥30 minutes average increase in sleep duration on weeknights.

Results

Average baseline sleep duration was 7.7 hours per night. The highest ranked combination included the core intervention plus the following intervention components: sleep goal (either setting was effective), caregiver-directed loss-framed incentive, messaging to reduce screen time, and messaging to establish daily routines. This combination increased weeknight sleep duration by an average of 39.6 (95% CI: 36.0, 43.1) minutes during the intervention period and by 33.2 (95% CI: 28.9, 37.4) minutes during the follow-up period.

Conclusions

Optimal combinations of digital health intervention component settings were identified that effectively increased weeknight sleep duration. This could be a valuable remote patient monitoring approach to treat insufficient sleep in the pediatric setting.

Extending sleep to improve glycemia: The Family Routines Enhancing Adolescent Diabetes by Optimizing Management (FREADOM) randomized clinical trial protocol

Sleep deficiencies amongst individuals with type 1 diabetes mellitus (T1DM) have been linked with dysregulated glycemic control and greater morbidities. Sleep extension (EXT) has been identified as a viable intervention target to improve adolescent outcomes. The intervention aims to emphasize collaborative work with families to engage in behaviors that increase the likelihood of the youth increasing their sleep duration consistently. This study will randomize up to 175 youth with T1DM and at least one caregiver to either an EXT intervention or a family routines support (FRS) consultation. It is hypothesized that the EXT condition will lead to improvements in sleep, which in turn, will contribute to improved glycemic control. The primary endpoint is improved glycemic control assessed via a continuous glucose monitor (CGM) to ascertain average glucose levels across a week, glycemic variability, and percent time in the target range at one month and HbA1c at three months. Analyses will control for co-morbid conditions, including sleep-disordered breathing and obesity. This study will provide the needed data to support addressing sleep as part of the standards of care in youth with T1DM.

Sleep and Circadian Rhythm Disturbances in Diabetes: A Narrative Review

Sleep and circadian rhythm disturbances are less-known risk factors for the development and suboptimal outcomes of diabetes. The goal of this narrative review is to highlight the importance of sleep and circadian rhythm disturbances in the development and outcomes of type 1 diabetes (T1D) and type 2 diabetes (T2D), assess current treatment options and the possible mediating mechanisms. We performed a literature search using PubMed and selected relevant English and Dutch papers. Disturbances of sleep and circadian rhythm are common in people with diabetes. They are associated with an increased risk of developing T2D as well as with suboptimal diabetes outcomes (including higher HbA1c levels and reduced quality of life) for T1D and T2D. Preliminary data suggest that treatment of sleep and circadian rhythm disturbances could improve diabetes outcomes in people with T1D and T2D. Finally, the association with medical parameters appears to be mediated by disturbance in hormones, and by suboptimal self-care including forgetting or postponing glucose monitoring or medication use as well as higher consumption of high fat/high sugary foods. Diabetes may also disturb sleep, for example through nocturnal hypoglycemia and nocturia. We concluded that sleep and circadian rhythm disturbances are closely linked with diabetes. More attention to sleep in regular diabetes care is warranted, while further research is needed on treatment of sleep and circadian rhythm disturbances in the prevention of diabetes and its suboptimal outcomes.

Sleep optimization to improve glycemic control in adults with type 1 diabetes: study protocol for a randomized controlled parallel intervention trial

BACKGROUND

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 have also been linked to sleep variability and insufficient sleep duration. A significant gap of knowledge exists regarding interventions to improve sleep and 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 [TIR]); diabetes distress; self-management behaviors; quality of life; and other patient-reported outcomes in adults with T1D and habitual increased sleep variability or short sleep duration.

METHODS

A randomized controlled parallel-arm study will be employed in 120 adults (aged 18 to 65 years) with T1D. Participants will be screened for habitual sleep variability (> 1 h/week) or insufficient sleep duration (< 6.5 h per night). Eligible subjects will be randomized to the Sleep-Opt intervention group or healthy living attention control group for 12 weeks. A 1-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 TIR 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. 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. Baseline measures will be repeated at midpoint, program completion, and post-program (weeks 6, 12, and 24, respectively) to determine differences between the two groups and sustainability of the intervention.

DISCUSSION

A better understanding of strategies to improve sleep in persons with T1D has the potential to be an important component of diabetes.

TRIAL REGISTRATION

Clinical Trial Registration: NCT04506151 .

The OPTIMISE study protocol: a multicentre optimisation trial comparing continuous glucose monitoring, snacking habits, sleep extension and values-guided self-care interventions to improve glucose time-in-range in young people (13–20 years) with type 1 diabetes

Purpose

The OPTIMISE study uses a Multiphase Optimisation Strategy (MOST) to identify the best combination of four interventions targeting key diabetes self-care behaviours for use in clinical practice to improve short-term glycaemic outcomes.

Methods

This 4-week intervention trial will recruit 80 young people (aged 13–20 years) with type 1 diabetes ≥ 6 months duration), and pre-enrolment HbA1c ≥ 58 mmol/mol (7.5%) in the prior 6 months. Both main intervention and interaction effects will be estimated using a linear regression model with change in glucose time-in-range (TIR; 3.9–10.0 mmol/L) as the primary outcome. Participants will be randomised to one of 16 conditions in a factorial design using four intervention components: (1) real-time continuous glucose monitoring (CGM), (2) targeted snacking education, (3) individualised sleep extension, and (4) values-guided self-care goal setting. Baseline and post-intervention glucose TIR will be assessed with blinded CGM. Changes in self-care (snacking behaviours, sleep habits and duration, and psychosocial outcomes) will be assessed at baseline and post-intervention to determine if these interventions impacted behaviour change.

Discussion

The study outcomes will enable the selection of effective and efficient intervention components that increase glucose TIR in young people who struggle to achieve targets for glycaemic control. The optimised intervention will be evaluated in a future randomised controlled trial and guide the planning of effective clinical interventions in adolescents and young adults living with type 1 diabetes.

Trial registration

This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry on 7 October 2020 (ACTRN12620001017910) and the World Health Organisation International Clinical Trails Registry Platform on 26 July 2020 (Universal Trial Number WHO U1111-1256-1248).

Risk of incident obstructive sleep apnoea in patients with type 1 diabetes: a population-based retrospective cohort study

AIMS/HYPOTHESIS

People with type 2 diabetes are at increased risk of developing obstructive sleep apnoea. However, it is not known whether people with type 1 diabetes are also at an increased risk of obstructive sleep apnoea. This study aimed to examine whether people with type 1 diabetes are at increased risk of incident obstructive sleep apnoea compared with a matched cohort without type 1 diabetes.

METHODS

We used a UK primary care database, The Health Improvement Network (THIN), to perform a retrospective cohort study between January 1995 and January 2018 comparing sleep apnoea incidence between patients with type 1 diabetes (exposed) and without type 1 diabetes (unexposed) (matched for age, sex, BMI and general practice). The outcome was incidence of obstructive sleep apnoea. Baseline covariates and characteristics were assessed at the start of the study based on the most recent value recorded prior to the index date. The Cox proportional hazards regression model was used to estimate unadjusted and adjusted hazard ratios, based on a complete-case analysis.

RESULTS

In total, 34,147 exposed and 129,500 matched unexposed patients were included. The median follow-up time was 5.43 years ((IQR 2.19-10.11), and the mean BMI was 25.82 kg/m2 (SD 4.33). The adjusted HR for incident obstructive sleep apnoea in patients with type 1 diabetes vs those without type 1 diabetes was 1.53 (95% CI 1.25, 1.86; p<0.001). Predictors of incident obstructive sleep apnoea in patients with type 1 diabetes were older age, male sex, obesity, being prescribed antihypertensive or lipid-lowering drugs, atrial fibrillation and depression.

CONCLUSIONS/INTERPRETATION

Individuals with type 1 diabetes are at increased risk of obstructive sleep apnoea compared with people without diabetes. Clinicians should suspect obstructive sleep apnoea in patients with type 1 diabetes if they are old, have obesity, are male, have atrial fibrillation or depression, or if they are taking lipid-lowering or antihypertensive drugs.

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Background

Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.

Objective

Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.

Methods

We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Type 1 Diabetes, Sleep, and Hypoglycemia

PURPOSE OF REVIEW

To review the relationship between sleep and hypoglycemia, sleep characteristics, and their associations with glycemic control in persons with type 1 diabetes (T1D). The effects of sleep interventions and diabetes technology on sleep are summarized.

RECENT FINDINGS

Nocturnal hypoglycemia affects objective and subjective sleep quality and is related to behavioral, psychological, and physiological factors. Sleep disturbances are common, including inadequate sleep, impaired sleep efficiency, poor subjective satisfaction, irregular timing, increased daytime sleepiness, and sleep apnea. Some have a bidirectional relationship with glycemic control. Preliminary evidence supports sleep interventions (e.g., sleep extension and sleep coach) in improving sleep and glycemic control, while diabetes technology use could potentially improve sleep. Hypoglycemia and sleep disturbances are common among persons with T1D. There is a need to develop sleep promotion programs and test their effects on sleep, glucose, and related outcomes (e.g., self-care, psychological health).

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

Engineering a mobile platform to promote sleep in the pediatric primary care setting

Study Objectives

Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion. The objective of this study is to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep.

Methods

Children aged 10–12 years were enrolled (Study #1: N = 30; Study #2: N = 43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms.

Results

In study #1, 1514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: −8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms.

Conclusions

We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.

Clinical Trials

Both studies included in this manuscript were registered at clinicaltrials.gov:

-Study #1: NCT03263338

-Study #2: NCT03426644

Impact of type 1 diabetes mellitus, glucose levels, and glycemic control on sleep in children and adolescents: a case-control study

STUDY OBJECTIVES

To assess differences in habitual sleep patterns and sleep states between children and adolescents with type 1 diabetes mellitus (T1DM) and control subjects, and to explore the relationships between sleep, glucose levels, and glycemic control.

METHODS

Participants included 82 children (5-18 years); 41 with T1DM (cases), and 41 healthy control subjects group matched for age and sex. Sleep was measured by 7-day actigraphy and single-night home-based polysomnography (PSG) recordings. Hemoglobin A1c (HbA1c) and 7 days of continuous glucose monitoring (CGM) data were collected in cases. Regression analyses were used to model all within- and between-group comparisons adjusted for age, sex, and BMI z-scores.

RESULTS

There were no significant differences in sleep duration, efficiency, or awakenings as measured by actigraphy and PSG between cases and controls, nor sleep states measured by PSG. However, cases had significantly later sleep onset and offset than controls (both p < 0.05), partially moderated by age. Cases with suboptimal glycemic control (HbA1c ≥ 58 mmol/mol [≥7.5%]) had significantly shorter actigraphy-derived total sleep time (TST) (mean difference = -40 minutes; 95% confidence interval = -77, -3), with similar differences in TST measured by PSG. Cases with mean CGM glucose levels ≥10 mmol/L (≥180 mg/dL) on PSG night had significantly more stage N3 (%) sleep and less stage REM (%) sleep (both p < 0.05).

CONCLUSIONS

Short- and long-term suboptimal glycemic control in T1DM children appears to be associated with sleep alterations. Pediatric diabetes care teams should be aware of potential interrelationships between sleep and T1DM, including management and glycemic control.

Pilot Trial of a Sleep-Promoting Intervention for Children With Type 1 Diabetes

OBJECTIVE

To assess the feasibility and acceptability of an educational sleep-promoting intervention (Sleep Coach Jr.) for school-aged children (ages 5-9) with type 1 diabetes (T1D) and their parents.

METHODS

Parents and children (N = 39 dyads, mean child age = 8 years, 64% girls,) were randomized to either the Sleep Coach Jr. intervention, consisting of educational materials and three individual phone calls (N = 20), or the Standard Care condition (N = 19). Data were collected at enrollment and 3 months later. Children and parents wore actigraphy devices to obtain an objective measure of sleep characteristics, and parents completed questionnaire measures of sleep quality and psychosocial outcomes. Clinical data (i.e., hemoglobin A1c, glucose data) were obtained from children's medical records.

RESULTS

Feasibility and acceptability of the study were demonstrated to be high; all three sessions were completed by 80% of parents randomized to the Sleep Coach Jr. intervention, and 90% of parents completed follow-up data at 3 months. Parents reported high levels of satisfaction with the study and identified barriers to participation. No changes were observed in children's sleep or diabetes outcomes, but parental sleep quality and well-being improved.

CONCLUSIONS

A brief, behavioral sleep-promoting intervention is feasible and acceptable for school-aged children with T1D and their parents. A larger trial is needed to evaluate efficacy of the intervention.

Engineering a Mobile Platform to Promote Sleep in the Pediatric Primary Care Setting

Background

Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion.

Purpose

Under the preparation phase of the MOST framework, to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep.

Methods

Children aged 10-12y were enrolled (Study #1: N=30; Study #2: N=43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms.

Results

In study #1, 1,514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: -8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms.

Conclusion

We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.

Sleep and depressive symptoms in adolescents with type 1 diabetes not meeting glycemic targets

AIMS

Adolescents with type 1 diabetes (T1D) are at risk for problems with self-management and suboptimal glycemic control, and depressive symptoms and sleep disturbances predict poorer diabetes outcomes. Despite evidence for associations between adolescent depressive symptoms and sleep in the general population, few studies have investigated this link in the vulnerable group of adolescents with T1D not meeting glycemic targets. The current study sought to assess both depressive symptoms and sleep in relation to diabetes indicators in adolescents with T1D.

METHODS

120 adolescents (ages 13-17 years) with above target glycemic control completed measures of depressive symptoms, sleep duration and quality, and self-management; parents also reported on adolescents' diabetes management. Clinical data (i.e., HbA1c) were extracted from medical records.

RESULTS

In our sample, 40% of adolescents reported at least mild depressive symptoms, and 26% reported clinically significant sleep disturbances. Adolescents with sleep disturbances were more likely to report at least mild symptoms of depression, and both depressive symptoms and sleep quality were associated with poorer diabetes management. No significant differences emerged regarding HbA1c or frequency of blood glucose monitoring.

CONCLUSIONS

The current findings highlight the importance of clinical assessment of both depressive symptoms and sleep in the vulnerable group of adolescents with T1D.

Sleep coach intervention for teens with type 1 diabetes: Randomized pilot study

BACKGROUND

Teens with type 1 diabetes (T1D) experience increased sleep disturbances, which have been linked to problems with adherence and glycemic control. As such, sleep represents a novel target to improve outcomes in teens.

OBJECTIVE

To evaluate the feasibility, acceptability, and preliminary efficacy of a sleep-promoting intervention in teens with T1D.

RESEARCH DESIGN AND METHODS

Teens aged 13 to 17 with T1D (n = 39) completed measures of sleep quality and diabetes management and wore actigraphs to obtain an objective measure of sleep. Hemoglobin A1C (HbA1c) was collected from medical records. Teens were randomized to Usual Care (n = 19) or the Sleep Coach intervention (n = 20). Teens in the Sleep Coach group received educational materials on healthy sleep habits and completed three individual telephone sessions. Follow-up data were collected at 3 months, including exit interviews with teens and parents.

RESULTS

Feasibility of the study was excellent; 80% of teens in the Sleep Coach group completed all three sessions, and retention was high (90%). Based on actigraphy data, a significant improvement in sleep efficiency and sleep duration was observed (48-minute increase) among teens randomized to the Sleep Coach intervention, and teens in the control group were 7.5 times more likely to report poor sleep quality after 3 months than intervention participants. No change in HbA1c was observed.

CONCLUSIONS

The Sleep Coach intervention for teens with T1D is a feasible and acceptable program that increased sleep duration and improved sleep quality for this high-risk population.

Sweet dreams or bitter nightmare: a narrative review of 25 years of research on the role of sleep in diabetes and the contributions of behavioural science

The aim of this review was to provide an overview of developments, clinical implications and gaps in knowledge regarding the relationship between diabetes and sleep over the past 25 years, with special focus on contributions from the behavioural sciences. Multiple prospective observational and experimental studies have shown a link between suboptimal sleep and impaired glucose tolerance, decreased insulin sensitivity and the development of type 2 diabetes. While prevalence rates of suboptimal sleep vary widely according to definition, assessment and sample, suboptimal subjective sleep quality appears to be a common reality for one-third of people with type 1 diabetes and over half of people with type 2 diabetes. Both physiological and psychosocial factors may impair sleep in these groups. In turn, suboptimal sleep can negatively affect glycaemic outcomes directly or indirectly via suboptimal daytime functioning (energy, mood, cognition) and self-care behaviours. Technological devices supporting diabetes self-care may have both negative and positive effects. Diabetes and its treatment also affect the sleep of significant others. Research on the merits of interventions aimed at improving sleep for people with diabetes is in its infancy. Diabetes and sleep appear to be reciprocally related. Discussion of sleep deserves a central place in regular diabetes care. Multi-day, multi-method studies may shed more light on the complex relationship between sleep and diabetes at an individual level. Intervention studies are warranted to examine the potential of sleep interventions in improving outcomes for people with diabetes.

Differences in sleep architecture according to body mass index in children with type 1 diabetes

Slow wave sleep (SWS), or deep sleep, is thought to be the most restorative stage of sleep and may be of a particular interest in the pathophysiology of obesity. The aim of this study was to investigate differences in sleep architecture based on body mass index (BMI) among a pediatric population with type 1 diabetes mellitus (T1DM). We hypothesized that children with T1DM who are obese would have less SWS than those who are not obese. Of 105 children with T1DM (mean age 13.54 years, 49.5% females) in this study, 19% were obese, 22% were overweight, and 59% had a normal BMI (81% non-obese). The overall SWS% among the participants was 13.2%. In contrast to our hypothesis, there was no significant difference in SWS% between obese and non-obese participants. However, the percent of time spent in rapid eye movement (REM) sleep among obese participants was significantly lower than those who were not obese (P = .022), which remained after adjusting the result for multiple covariates. While we found no significant association between the SWS time and BMI, obese adolescents with T1DM spent less time in REM sleep than those who were not obese. This study adds to the growing body of evidence supporting the importance of addressing sleep in clinical care of youth with T1DM.

Sleep-related disorders in patients with type 1 diabetes mellitus: current insights

Type 1 diabetes mellitus (T1DM) is an autoimmune condition that results from destruction of beta cells in the pancreas. Several reviews have concluded that sleep contributes to poor glycemic control, diabetes management, and diabetes-related complications in individuals with T1DM and represents an untapped opportunity for intervention. However, at the current juncture, the American Diabetes Association's Standards of Medical Care are devoid of recommendations about how to address sleep in the management of T1DM. This article summarizes reviews of sleep in youth and adults with T1DM and empirical studies that have examined various sleep parameters ranging from sleep disturbances (general, perceived sleep quality, sleepiness, awakenings, and sleep efficiency), sleep duration, sleep consistency, sleep-disordered breathing (SDB), and sleep architecture. The data show that many individuals with T1DM sleep less than recommendations; individuals with the poorest sleep have difficulties with diabetes management; and sleep deficiency including SDB often corresponds to several disease morbidities (neuropathy, nephropathy, etc). Mixed findings exist regarding direct associations of various sleep parameters and glycemic control. SDB appears to be just as prevalent, if not more, than other conditions that have been recommended for universal screening in individuals with T1DM. The article concludes with recommendations for collaborative research efforts to further elucidate the role of sleep in diabetes-related outcomes; investigations to test behavioral strategies to increase sleep quantity and consistency; and considerations for clinical care to address sleep.

Sleep characteristics in young adults with type 1 diabetes

Only 14% of young adults with Type 1 Diabetes (T1D) achieve targets for glycemic control (HbA1C < 7.0%), with deterioration over time. Complex cognitive processes required to manage glycemia are vulnerable to sleep deficiency. Using Whittemore and Knafl's approach, we conducted an integrative review of research literature on sleep characteristics and glycemia in these young adults. Quality was assessed using the Mixed Methods Appraisal Tool (v. 2011). Multiple databases were searched for articles published in English in peer-reviewed journals from 2003 to 2018, using search terms 'sleep' and 'T1D' with age limiters 18-40. Of 218 studies initially retrieved, 17 original studies met the inclusion criteria. The following themes were identified in young adults with T1D: (1) They had poorer objective and subjective sleep quality, more variability, and impaired awakening response to hypoglycemia compared with controls; (2) They had poorer glycemic control that was associated with shorter sleep duration, poorer sleep quality, and less time in deep sleep; and (3) Hypoglycemia negatively impacted diabetes management, sleep quality, and next day functioning. Sleep deficiency, as indicated by short sleep duration is associated with a range of negative health outcomes for people with T1D; therefore, optimizing sleep should be a priority in practice and research.

Sleep and type 1 diabetes in children and adolescents: Proposed theoretical model and clinical implications

Youth with type 1 diabetes mellitus (T1D) experience more sleep disturbances and shorter sleep durations compared to their healthy peers. Researchers have now uncovered the negative mental health and physical health outcomes associated with poor sleep in youth with T1D. The field of T1D sleep research currently operates under the broad notion that sleep behaviors impact treatment adherence, which ultimately lead to worse long-term health outcomes. This model however does not explain how behavior influences T1D management and sleep outcomes on a day-to-day basis, leading to difficulties in providing tailored treatment recommendations. In this review, we present a theoretical framework that describes the recursive cycle between sleep behaviors, T1D outcomes, and symptoms of negative affect/stress over a 24-hour period. This model is guided by the sleep literature, showing a clear relationship between poor sleep and negative affect, and the T1D literature demonstrating a link between poor sleep and disease management for youth with T1D. Further, emerging literature indicates a need for additional parent sleep assessment considering that T1D management and fear of hypoglycemia negatively impact parent sleep behaviors. Recommendations are provided to move the field toward effective intervention studies and new areas of research to evaluate and modify the proposed model.

Sleep in Type 1 Diabetes: Implications for Glycemic Control and Diabetes Management

PURPOSE OF REVIEW

To highlight recent findings from studies of sleep in type 1 diabetes (T1D), with a focus on the role of sleep in self-management, the cognitive and psychosocial outcomes related to sleep disturbances, and factors associated with sleep disturbances specific to T1D.

RECENT FINDINGS

People with T1D experience higher rates of sleep disturbances than people without diabetes, and these disturbances have negative implications for glycemic control and diabetes management, as well as psychosocial and cognitive outcomes. Inconsistent sleep timing (bedtime and wake time) has emerged as a potential target for interventions, as variability in sleep timing has been linked with poorer glycemic control and adherence to treatment. Sleep-promoting interventions and new diabetes technology have the potential to improve sleep in people with T1D. Sleep is increasingly considered a critical factor in diabetes management, but more multi-method and longitudinal research is needed. We emphasize the importance of sufficient and consistent sleep for people with T1D, and the need for providers to routinely assess sleep among patients with T1D.

Sleep Problems as Consequence, Contributor, and Comorbidity: Introduction to the Special Issue on Sleep, Published in Coordination With Special Issues in Clinical Practice in Pediatric Psychology and Journal of Developmental and Behavioral Pediatrics

Despite long-standing public and scientific interest in the phenomenon of sleep, the current decade has shown tremendous growth in our understanding of the sleep of children who have medical or developmental conditions. To accommodate, promote, and guide that growth, Journal of Pediatric Psychology, Clinical Practice in Pediatric Psychology, and Journal of Developmental and Behavioral Pediatrics have published coordinated special issues, encompassing >30 relevant articles. This article introduces the special issue in Journal of Pediatric Psychology, highlighting papers that illustrate how sleep problems are not only commonly comorbid with childhood medical and developmental conditions; they are also likely caused by and contribute to these conditions. In doing so, these coordinated special issues guide clinical care and reveal opportunities for future research.