Poor Sleep Habits in Youth Linked to Type 1 Diabetes

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New data show that youths with type 1 diabetes perceive their sleep as disrupted, but investigators find similar objective outcomes in youths without diabetes.

A recent study found that adolescents and young adults with type 1 diabetes mellitus (T1D) and high-risk glycemic control experience sleep difficulties due to sleep deficits and poor quality of sleep, compared to youth without diabetes.

Investigators, led by Benjamin J. Wheeler, MB of the Department of Women’s & Children’s Health, Dunedin School of Medicine at the University of Otago, said that while youth generally face challenges with adequate sleep, youth with T1D and high-risk glycemic control reported poorer subjective sleep outcomes.

The National Sleep Foundation (NSF) recommends 9–11 hours of sleep each day for 13-year-olds, 8–10 hours for adolescents aged 14-17 years old, and 7–9 hours for young adults, based on self-reported sleep measures from large cohort studies. Despite this, investigators say that sleep regular and behavior is more likely to be disputed in teens, creating a “perfect storm” of sleep disruption.

The team used subjective and objective measures to determine the impact of poor sleep on youth with T1D, including spanned timing, duration, and quality variables.

Data show that while the objectively measured sleep onset and wake times were later for youth with T1D before adjustment, the outcomes were similar after adjustment for age, gender, ethnicity, and school term time.

Due to this, investigators said the perception of poor sleep may be more of an issue in those with T1D than those measured objectively.


The team compared data on self-reported sleep health components for youth with T1D and youth without diabetes, as well as comparing data on these groups to objectively measured components.

Youth with T1D enrolled in the Flash study, a randomized controlled trial (RCT) for a six-month, controlled, open-label study of intermittently scanned continuous glucose monitoring (isCGM), compared with usual care.

Patients were recruited from three diabetes centers in New Zealand from April – November 2018 and the 64 individuals enrolled were required to have high-risk glycemic control (HbA1c ≥75 mmol/mol, ≥9%), which investigators said may not represent youth with T1D in total.

The Snap-It study included control participants without T1D in were taken from an observational study conducted over the same timeframe, measuring sleep, screen use, nutrition, and physical activity in teenagers using automated cameras. Eligibility for the study included residence in Dunedin, New Zealand, and an age of 13–17 years. After recruitment efforts, a total of 166 teenagers were enrolled.

The study took place between December 2017 and May 2019, with youth wearing accelerometers for 7 days and nights, as well as answering questionnaires on sleep quality, screen use, puberty, and household composition.

Investigators used Sleep Period Time (SPT) to determine the time between sleep onset and offset, while Total Sleep Time (TST) was the time between inset and offset, minus periods of awakening. Sleep Efficiency (SE) was the percent of time asleep between sleep onset and offset (TST/SPT *100).

Investigators used the Pittsburgh Sleep Quality Index (PSQI) for participants in both studies, to assess sleep timing and quality.

The PSQI uses 7 domains including subjective sleep quality, sleep onset latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, daytime dysfunction. It also uses a PSQI global score, ranging from 0 – 21 points with a score of >5 indicating a subject is having ‘severe difficulties’ in sleep quality.

The measures of sleep timing were reported as mean (SD) for continuous variables. The difference between youth with T1D and control used linear regression models to estimate mean difference and 95% confidence interval (CI) with adjustment.

PSQI sleep quality scores were reported as median (25th, 75th percentile), with odds ratio (OR) and 95% CI for reporting a ‘poor score’ before and after adjusting for age, gender, ethnicity, and school term time.


A total of 230 youth participated in the 2 studies, with 64 youth with T1D in the Flash study and 166 patients enrolled in the Snap It study.

Self-reported data from the PSQI was collected for youth with T1D (n = 62) and control without diabetes (n = 147).

The T1D group reported significantly later bedtimes (MD, +36 minutes; 95% CI, 8, 65; P <.05) and shorter sleep duration (MD. -53 minutes; 95% CI, -84, -22; P <.05), after adjustment.

Data also show that youth with T1D were more likely to rate sleep duration as “poor” (OR 3.57; 95% CI, 1.41-9.01; P = .007).

Data also show that youth with T1D rated sleep efficiency (OR 4.03; 95% CI, 1.43-11.40) and sleep quality (OR 2.59; 95% CI, 1.16-5.76) as “poor”.

A poor score indicated SE ≤ 74%; and ‘poor’ subjective sleep quality (OR 2.59; 95% CI, 1.16, 5.76; P = .020) after adjustments.


Investigators concluded that while youth with T1D report various negative impacts on their sleep compared to youth without diabetes, the objective measured sleep onset and wake times were similar between the two groups.

“These data add to our knowledge in this area in that whilst sleep health remains a very important aspect of youth health in general, perceptions of poor sleep in those with T1D appear more of an issue than those measured objectively,” investigators wrote.

The team concluded that data suggest poor sleep is a problematic issue among all adolescents, so youth should be aware of the positive role sleep has in overall health.

“Advice from diabetes teams on simple measures to promote sleep health (e.g. consistent sleep and wake times across the week) may be especially important for people living with type 1 diabetes due to the bidirectional relationship between sleep and glycemic control,” investigators wrote.

The study, “Impact of high-risk glycemic control on habitual sleep patterns and sleep quality among youth (13-20 years) with type 1 diabetes mellitus compared to controls without diabetes,” was published online in Pediatric Diabetes.