Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05762783 |
| Other study ID # |
T_023 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 27, 2023 |
| Est. completion date |
April 16, 2024 |
Study information
| Verified date |
April 2024 |
| Source |
Steno Diabetes Center Copenhagen |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Adolescents with type 1 diabetes (T1D) experience more disturbed sleep compared to their
healthy peers, especially because they tend to spend less time in deep sleep, the most
restoring part of sleep, potentially impacting diabetes management. Disturbed sleep may
adversely affect diabetes management which requires day-to-day decision-making, emotional and
behavioural regulation, attention, and planning. Despite a massive increase in new
technology, more than 50% of adolescents do not reach their glycaemic target. Lack of sleep
impairing diabetes management including blood glucose monitoring may play an important role
in reaching the goal. For approximately 4000 children and adolescents in Denmark living with
T1D, sleep disturbances may therefore account for short and long-term diabetes complications.
Our overall aims are to investigate: (1) If and how glycaemic variability (GV) influences
sleep quality and sleep stages and (2) if and how poor sleep quality influences time-in-range
(TIR), time-above-range (TAR) and time-below-range (TBR) the following day.
Description:
Background: Sleep distrubances are increasing among adolescents in Denmark. In 2017, a large
health survey reported that 14.9 % of the 16-24 year-olds ( n= 7,627) had sleep problems over
the last two weeks-an alarmingly high increase of 33.3% since the first survey in 2010.
Adolescents with type 1 diabetes (T1D) experience more disturbed sleep compared to their
healthy peers, especially because they tend to spend less time in deep sleep, the most
restoring part of sleep. Disturbed sleep may adversely affect diabetes management which
requires daily blood glucose monitoring (BGM), day-to-day decision-making, emotional and
behavioural regulation, attention and planning (behavioural pathway). Diabetes management
plays an important role in reaching glycemic targets and the worsening glycemic outcome may
impact sleep and start a vicious circle. Currently, >50% of adolescents do not reach the
glycemic target. Furthermore, the disturbance of circadian rhythmicity may in itself have
metabolic consequences (metabolic pathway). Disturbed sleep may contribute to the 40%
increased risk of psychiatric disease (anxiety, depression and eating disorders) associated
with childhood diabetes. A link between psychiatric disease, stress and sleep has been
confirmed. For the approximately 4000 children and adolescents in Denmark living with T1D,
this means sleep problems may contribute negatively and increase the risk of short and
long-term diabetes complications.
Over the past decades, the use of technology such as insulin pumps and continuous glucose
monitors (CGM) in T1D treatment has increased dramatically, but still far from all reaches
glycemic targets. This may, in part, be explained by the vicious circle and the additional
disturbances from alarms and difficulties in initiating sleep due to itching or irritation
from having devices attached to the body. The more beneficial effect on sleep from advanced
insulin pumps like hybrid closed loop systems with less glycemic variability and fewer alarms
overnight, has yet to be proved in children and adolescents.
The term sleep is defined as sleep quality and duration. Disturbed sleep is defined as too
short or long duration for age based on guidelines from the American Academy of Sleep
Medicine (AASM) and/or poor quality sleep. The ability to be more resilient, have better
coping strategies and more self-compassion may impact your sleep through the influence on
rumination, reducing sleep latency (period from going to bed to falling asleep) and duration
of wake-up periods, why resilience, coping and compassion are potentially modifiable factors.
Other factors like social activities and media uses are important factors associated with
changes in circadian rhythmicity, sleep length, sleep disturbances and poorer daytime
performance. Furthermore, poor glycemic control is associated with sleep disorders like
obstructive sleep apnea.
The golden standard for sleep assessment is polysomnography (PSG), but it is limited because
it (1) typically provides only 1-2 nights of information about a child's sleep, (2) requires
in some cases observation outside the natural environment in a sleep lab and (3) has
significant financial costs. We have access to a new home sleep test with eye movement
detection (HST REM+) from Somnomedics GmbH which is validated against PSG in adults. The HST
REM+ is a simplified PSG which measures sleep architecture (sleep stages) with three
electrodes in the forehead and one behind the ear. The HST REM+ can make measurements for up
to 5 nights at home with a tablet showing how to apply the electrodes. The HST REM+ is
combined with ActiGraph wGT3X-BT (AG), a small wristwatch measuring movements and validated
against PSG in adolescents. With the majority of children being treated with continuous
glucose monitoring (CGM) and insulin pumps, we have the unique opportunity to combine data
from these different objective sleep measures and thorough diabetes management data from
downloads from insulin pump and CGM. This opens for the opportunity to study 24/7 variation
in diabetes management and glycemic outcome including device alarm frequency and relate this
to the current and previous night sleep. Additionally, we are able to study how advanced
insulin pumps including hybrids closed loop systems impact sleep parameters such as sleep
duration, sleep stages, sleep efficiency and sleep latency.
Our overall aims are to investigate: (1) If and how glycaemic variability influences sleep
quality and sleep stages and (2) if and how poor sleep quality influences time-in-range
(TIR), time-above-range (TAR) and time-below-range (TBR) the following day.
Main hypothesis
1. Glycaemic variability is negatively associated with sleep quality and time spent in deep
sleep (sleep stage N3+N4)
2. Sleep quality is positively associated with time in range (TIR) the following day
Study design: Observational, cross-sectional study. Methods: Children and adolescents with
T1D are recruited from the Diabetes Outpatient Clinic, Steno Diabetes Center Copenhagen
(SDCC), providing approximately 750 of the 900 children and adolescents using CGM and insulin
pumps. One or both parents are invited to participate as well as healthy siblings as
controls. The families are recruited from a notice in the waiting room, from the SDCC webpage
and asked to participate during out-patient visits, where written and oral information are
given. Questionnaires and clinical parameters will be collected. One-week simultaneous
observation at home with AG, a sleep diary (which they receive on their mobile device after
each night), HST REM+ (5 days) and diabetes management (insulin pump and CGM) will be
offered. Eligibility criteria: Participants are included if they are 6-17 years of age with >
6 months T1D duration and are using an insulin pump and CGM (all types). Exclusion criteria
are severe psychiatric diseases using ICD-10 codes (e.g. ADHD, autism spectrum disorder,
eating disorders, retarded mental development), unwillingness to participate and inability to
read and understand Danish or English. Primary Outcome WP1: Sleep quality is defined as %
time in deep sleep stages (N3 and N4). Secondary outcomes: Deep sleep (minutes), REM sleep %
and minutes, sleep quality (AG), TST (HST REM+ and AG), sleep score from the Scandinavian
Sleep Questionnaire - Children and Adolescents sleep (SSQ-CA). Exposure: GV during the night.
Additionally, as exploratory analysis: Diabetes-specific stressors e.g., alarms and fear of
hypoglycemia, resilience, quality of life, strengths and difficulties and stress in the
caregiver. Covariates: Diabetes duration, treatment modality, age and gender. Primary outcome
WP2: Daytime TIR. Secondary outcomes: a) Glycaemic outcomes (Daytime TAR and TBR, Day + night
TIR, TBR, TAR, HbA1c, Glycaemic risk index, mean glucose, GV and standard deviation (SD)); b)
diabetes-management (use of bolus-guide including blood glucose and carbohydrate from pump
download). Exposure: Sleep quality is defined as % time in deep sleep stages (N3 and N4).
Additionally, sleep quality (SSQ-CA and AG), REM sleep (HST REM+). Covariates: Diabetes
duration, treatment modality, age, gender. Statistics/power: Statistical methods will include
descriptive statistics, T-test, Spearman's correlation, RASCH modelling of questionnaire
data. All variables in the models will be tested for effect size using univariate analysis.
Multiple regression models will include explanatory variables and covariates depending on
effect size and importance. Inverse probability weighting will be used to examine selection
bias due to non-participation Regarding hypothesis 1: To detect a change of 3 % in deep sleep
(N3) with an SD of 7 %, we have a power of 80% if 43 participate in the study (significance
level of 5%). Regarding hypothesis 2: To detect a change of 5 % in TIR with an SD of 12 %, we
have a power of 80% if 45 participate in the study (significance level of 5%).
