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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: Behav Sleep Med. 2021 Sep 24;20(5):649–658. doi: 10.1080/15402002.2021.1977305

Sleep Habits of Early School-Aged Children with Type 1 Diabetes and Their Parents: Family Characteristics and Diabetes Management

Troy Morrow 1, Shivani H Bhatia 1, Angelee M Parmar 1, Logan Baker 1, Fayo Abadula 1, Dylan Williamson 1, Arjun Choudhary 1, Sarah S Jaser 1
PMCID: PMC8943443  NIHMSID: NIHMS1752201  PMID: 34559603

Abstract

Study Objectives:

School-aged children with type 1 diabetes (T1D) and their parents are at risk for sleep disturbances, yet few studies have used objective measures to assess sleep characteristics in young children with T1D.

Methods:

Forty children (ages 5–9) with T1D and their parents wore actigraph watches and completed sleep diaries for 7 nights. Parents also completed questionnaires about demographic information, diabetes distress, fear of hypoglycemia, and family routines. Children’s clinical data (HbA1c and blood glucose data) were extracted from the medical record.

Results:

Most of the children and their parents obtained insufficient sleep. Based on actigraphy data, children slept an average of 7.9 hours/night and parents slept 6.7 hours/night, below the recommendations of 9–11 and 7-9 hours of sleep, respectively. Shorter child sleep latency was significantly associated with better glycemic levels, and parents’ sleep duration and efficiency were related to child’s glycemic levels. Parental fear of hypoglycemia and lack of family routines were associated with poorer sleep quality in parents and children, and with parental diabetes distress.

Conclusions:

Sleep duration and quality is a modifiable target for potentially improving glycemic levels and parental distress in early school-aged children with T1D.

Introduction

Type 1 diabetes (T1D) is one of the most common childhood chronic health conditions, affecting about 1 in 400 children, and the incidence in younger children is rising (Hamman et al., 2014). Diabetes management is complex and demanding, and parents are primarily responsible for daily tasks (e.g., blood glucose monitoring, insulin administration, carbohydrate counting). The majority of youth with T1D struggle to meet treatment goals - only 22% of 6–12-year-olds in a national sample were meeting the recommended glycemic targets, as measured by hemoglobin A1c (HbA1c) (Miller et al., 2015) - and parents of children with T1D report high levels of stress related to the “constant vigilance” of treatment management (Sullivan-Bolyai et al., 2003). Emerging evidence suggests sleep is a potential risk or protective factor for diabetes outcomes (Perez et al., 2018), but little is known about sleep in relation to diabetes indicators among early school-aged children with T1D and their parents.

Children with T1D are at a greater risk for sleep disturbances than their peers without T1D, and these disturbances may have negative effects on diabetes management and glycemic levels (Perez et al., 2018; Reutrakul et al., 2016). In the largest study of sleep among children with T1D and their caregivers (n = 515), parent surveys revealed that the majority of children ages 2–12 years exhibited clinically significant sleep disturbances (67%), and most parents were not meeting recommendations for sleep duration (mean parent sleep duration was 6.5 hours/night) (Jaser et al., 2017). Lower sleep quality in children was significantly associated with poorer glycemic levels and greater risk for hypoglycemic episodes and diabetic ketoacidosis (Jaser et al., 2017). In adults with T1D, experimental sleep restriction has been shown to decrease insulin sensitivity (Donga et al., 2010).

Studies of younger children found that fear of hypoglycemia was linked to poorer parental sleep (Herbert et al., 2014), and parents who conducted nighttime blood glucose checks reported greater fear of hypoglycemia than those who did not check (Monaghan et al., 2009). Introduction of an automated insulin delivery system in children with T1D was related to improvements in parental sleep quality and diabetes distress (Bisio et al., 2021), indicating that improved diabetes management may improve both of these outcomes. These studies were limited by the use self-report questionnaire measures to assess sleep, and objective measures of sleep are needed to enhance our understanding of the role of sleep in diabetes management and psychological outcomes. Studies on family chaos suggest that there is a link between family confusion, hubbub, and order and sleep disturbances in pediatric populations (Boles et al., 2017; Lumeng et al., 2007). Additionally, a significant relationship between household chaos and glycemic control has been found in youth with T1D (Chae et al., 2016; Levin et al., 2013). Further research is needed to probe the relationship between family chaos, sleep, and glycemic control in children with T1D.

More recently, researchers have used actigraphy to assess sleep characteristics in youth with T1D and the effects of diabetes technology on sleep. For example, a small study (n = 13) of parents of children with T1D who transitioned to using continuous glucose monitoring (CGM) found that, while parents did not report changes in sleep quality, objective measures of sleep (actigraphy and sleep diaries) revealed shorter sleep duration and increased awakenings after their child started using the device (Landau et al., 2014). This study did not assess sleep in children, however, and it included a wide age range (5–16 years). A more recent study of young children with T1D (age 2–5) that used actigraphy to assess sleep in both children with T1D and their parents found that the use of CGM was associated with fewer sleep disturbances among children but poorer sleep quality in their parents (Sinisterra et al., 2020). Finally, a recent study that included qualitative interviews and surveys with parents (n = 20) and actigraphy in their children with T1D (n = 16) observed sleep disturbances occurred more frequently at the time of diagnosis and at the initiation of new diabetes technology (Macaulay et al., 2020). This study did not objectively measure sleep in parents, however, and included a wide age range (1–17 years). While these studies included objective measures of sleep, sample sizes were small, and the focus was either on very young children or a wide age range, making it difficult to discern challenges associated with sleep in early school-aged children with T1D.

Families with early school-aged children with T1D, from the ages of 5–9, face additional management challenges related to increasing childhood autonomy, as this is the age when children typically begin attending school and participating in other structured activities away from home. Further, this age range captures a developmental change in sleep patterns during which sleep becomes more consolidated (with infrequent naps), and REM periods lengthen (Meltzer & Mindell, 2006; Montgomery-Downs et al., 2006). Children in this age range also begin to share the responsibility of managing their diabetes with their parents; the American Diabetes Association suggests that school-aged children (ages 6–12) begin to take responsibility of their own blood glucose checks and begin to administer insulin with caregiver supervision (Silverstein et al., 2005). Family factors, such as organization and routines, may serve as a protective factors during this developmental transition (Greening et al., 2007). Research indicates that higher levels of family organization (i.e., systems, routines, planned events) is associated with better glycemic levels and fewer behavior problems in youth with T1D (Herge et al., 2012). Sleep habits are a component of family routines that have been largely overlooked in early-school aged children with T1D.

Current study

The current study is a secondary analysis of baseline data from a pilot trial of a sleep-promoting intervention for school-aged children with T1D and their caregivers (ClinicalTrials.gov #NCT03397147; Jaser et al., 2020). In this analysis, we sought to describe sleep habits in children and their caregivers using both objective and questionnaire measures, and to examine associations between sleep characteristics and demographic and clinical factors, including the use of diabetes devices (insulin pumps, CGMs). We also explored associations between child and parent sleep with family functioning, including parental fear of hypoglycemia, diabetes distress, and family chaos. Sleep represents a potential risk or protective factor for diabetes outcomes, and a greater understanding of sleep habits among early school-aged children and their parents may inform interventions to improve outcomes in this population.

Methods

Children and their parents were recruited for the study during regularly scheduled visits at an outpatient diabetes clinic in a Southeastern academic medical center. Children were eligible for the study if they (1) were between the ages of 5 and 9 years old; (2) had been diagnosed with T1D for at least 12 months; and (3) had no other major health problems or sleep disorders other than insomnia, as we sought to describe sleep disturbances, such as difficulty falling asleep and/or staying asleep in this population. Caregivers were eligible if they (1) lived with the child at least 50% of the time and (2) read and spoke English. Children who were diagnosed with T1D for at least 12 months were studied to avoid confounding the effects of the initial adjustment period after diagnosis when the child could still be producing small amounts of insulin. If families were eligible, a member of the research team described the study in detail and obtained informed consent, in line with the protocol approved by the Vanderbilt University Institutional Review Board (#180,771).

Of the 80 families approached, 41 consented, 30 declined (most common reasons were lack of time and interest in participating) and 3 expressed interest but wanted to be approached at a later clinic visit. The remaining 6 families were ineligible; thus, participation of eligible families was 55%. One family consented but decided not to continue the study at that time (no data were collected), so the final sample consisted of 40 parent-child dyads. There were no significant demographic or clinical differences (age, sex, race/ethnicity, duration of diabetes or HbA1c) between children who enrolled and those who chose not to participate.

Measures

Demographics

Caregivers completed a demographic questionnaire during the baseline parent survey that asked about the race/ethnicity of the parent and child, family income, parental education, and use of diabetes technology (insulin pumps and CGMs). Clinical data including HbA1c, diagnosis date, blood glucose data (average glucose and Time in Range), and use of diabetes devices were extracted from the electronic health record.

Sleep

Actigraphy data were used as the objective measure of child and parent sleep characteristics. Philips Actiwatch Spectrum Plus™ (n = 18 dyads) and Actigraph wGT3X-BT™ devices (n = 22 dyads) were used to collect data, and parent-child dyads wore the same brand for data collection.1 Participants were instructed to wear the devices continuously for 7 days, but if that was not possible, they were instructed to put the actigraphy watches on an hour before bed and take them off an hour after waking. Based on earlier work (Goldman et al., 2017), Philips devices were configured for 60-second epochs, with a sleep interval of 10 epochs for sleep onset, and an awake threshold setting of 40 (medium). For the Actigraph devices, the Cole-Kripke algorithm (10 second epoch) was used to score parent data, while the Sadeh algorithm (60 second epochs) was used to score child data, in line with recommendations (Quante et al., 2018). Sleep characteristics included in analyses were child and parent total sleep time, sleep efficiency, and child sleep onset latency, as these are most reliably measured in this age group (Ohayon et al., 2017).

Sleep diaries were completed by parents (separate diaries for themselves and for their children) to record bedtime and wake time. At the end of the week, participants noted any abnormalities, such as problem with the watch, illness, new medication, or special events. Diaries were used to score actigraphy data when bedtime/wake time was not clear from the actigraphy devices (e.g., participants pressed the interval marker multiple times, or not at all, or a change in activity or light levels was not evident).

Child Sleep was assessed with the Children’s Sleep Habits Questionnaire (CSHQ, Owens et al., 2000) completed by the parent to evaluate common sleep problems that children encounter. This measure includes 33 items that ask about bedtime, sleep behavior, waking during the night, morning waking, and daytime sleepiness. Scores range from 33–99, and a score of 41 or higher indicates clinically significant sleep disturbances. In the current sample, Cronbach’s alpha was .75.

Parent Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI, Buysse et al., 1989). This self-report measures subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, uses of sleeping medication, and daytime dysfunction. Global scores range from 0–21, where a higher number indicates poorer sleep quality, and a score greater than 5 is considered clinically significant. In the current sample, Cronbach’s alpha was .77.

Parental distress and family functioning

Home environment was assessed with the Confusion, Hubbub, and Order Scale (CHAOS, Matheny et al., 1995). This scale consists of 15 statements that evaluate how ordered or chaotic the home environment is from the perspective of the parent (e.g., “There is often a fuss going on at our home” and “We are usually able to stay on top of things” (reverse scored)). Scores range from 15–60, where a higher score indicates a more chaotic home environment. In the current sample, Cronbach’s alpha was .85.

Diabetes Distress was measured with the Problem Areas in Diabetes: Parent Revised version (PAID-PR, Markowitz et al., 2012). This scale consists of 18 items to identify areas that cause distress specifically for parents of children with T1D (e.g., “I feel alone in managing my child’s diabetes” and “I feel ‘burned-out’ by the constant effort to manage diabetes”). Scores range from 0–72, where a higher score indicates greater diabetes distress, and scores of 56 or higher are clinically significant (Tinsley et al., 2018). In the current sample, reliability was α = .91.

Parental Fear of Hypoglycemia was assessed with the Worry Subscale of the Parent Hypoglycemia Fear Survey (PHFS, Clarke et al., 1998). This self-report measure consists of 15 statements that identify worries or concerns that a parent of a child with T1D may feel (e.g., “Child having a low while asleep” or “Child losing control of behavior due to low blood sugar”). Scores range from 0–60, and higher scores indicate more diabetes-related concern and worry. In the current sample, α = .87.

Data analysis plan

Statistical analyses were performed with IBM SPSS version 26. First, we conducted descriptive analyses to identify the characteristics of child and parent sleep habits, including means, standard deviations, and ranges. Next, we conducted independent t-tests to assess for differences in sleep characteristics related to demographic or clinical characteristics. Finally, we conducted bivariate correlations to examine associations between sleep characteristics, parental psychosocial factors, and diabetes indicators.

Results

Study participants consisted of parents (3 fathers, 37 mothers) and their children (14 boys and 26 girls, mean age 8.1 years). Mean HbA1c was 8.5% and mean duration of diagnosis was 3.5 years. In our sample, 62.5% of children were using insulin pumps and 62.5% were using a CGM. The demographic and clinical characteristics (see Table 1) of the participants indicate that the sample is representative of the diabetes clinic population.

Table 1.

Demographics and clinical characteristics (N = 40).

Range Mean (SD) Recommended Values
Age (years) 5–9 8.1 (1.5)
Duration of Diabetes (years) 1–7 3.5 (1.5)
A1C (%) 6.2–13.0 8.5 (1.5) <7.0%+
Average Blood Glucose Level (mg/dl) 141–336 216 (44.5) 70–180+
Time in Range (%) 0–64 36.1 (15.0) 70%+
Child Sex N (%)
Male 14 (35)
Female 26 (65)
Child Race/Ethnicity
White, Non-Hispanic 30 (75)
Black 4 (11.7)
 Biracial 4 (11.7)
 Hispanic 2 (5)
Annual Family Income (USD)
<80,000 20 (50)
>80,000 20 (50)
 Parental Education 23 (57.5)
 College Degree 17 (42.5)
 No College Degree
Treatment Type
Insulin Pump 25 (62.5)
Injection 15 (37.5)
Continuous Glucose Monitor Use
Yes 25 (62.5)
No 15 (37.5)
Range Mean (SD)
Chaos Hubbub and Order Scale 16–39 24.5 (6.6)
Problem Areas in Diabetes - Parent 21–59 35.6 (10.0)
Parent Hypoglycemic Fear Survey (Worry) 2–34 16.2 (8.9)
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Description of sleep habits

Based on actigraphy data, we found that mean total sleep time was 7.9 hours for children, and none of the children obtained the recommended 9–11 hours of sleep (Hirshkowitz et al., 2015). Mean sleep efficiency was 85.7% (goal is >85%), and sleep latency was 9.1 minutes (goal is <30 minutes) (Ohayon et al., 2017). For parents, mean total sleep time was 6.7 hours, 44% obtained the recommended 79 hours of sleep, and mean sleep efficiency was 87.0% (see Table 2). Based on parent report, 80% of children scored above the clinical cutoff for sleep disturbances on the CSHQ. Similarly, 76% of parents rated their own sleep quality above the clinical cutoff on the PSQI. On the CSHQ, the most commonly endorsed subscales were bedtime resistance and daytime sleepiness. On the PSQI, the most commonly endorsed subscales were sleep latency, quality and disturbance, In addition, child bedtime (reported by parents) ranged from 7:30pm to 10:00pm, with 40% going to bed at 9pm or later.

Table 2.

Child and parent sleep parameters and recommendations.

Range Mean (SD) Recommendation % Meeting Recommendation
Actigraphy Measures
Parent Mean Total Sleep Time (h) 3.5–9.0 6.7 (1.1) 7–9 44%
Child Mean Total Sleep Time (h) 6.5–8.9 7.9 (0.5) 9–11 0%
Parent Sleep Efficiency (%) 58.9–95.7 87.0 (7.0) ≥85% 70%
Child Sleep Efficiency (%) 79.4–93.1 85.7 (3.7) ≥85% 53%
Child Sleep Latency (min) 1–47.5 9.1 (10.7) <30 95%
 Survey Measures
PSQI (Parent Sleep Quality) 1–19 7.97 (4.2) <5 24%
CSHQ (Child Sleep Quality) 34–75 48.1 (8.7) <41 17%
Bedtime (hh:mm) 19:30–22:00 20:43 (:41) <21:00 60%
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PSQI = Pittsburgh Sleep Quality Index; CSHQ = Child’s Sleep Habits Questionnaire. Recommendations based on Hirshkowitz et al. (2015) and Ohayon et al. (2017)

Group differences in sleep related to demographic and clinical characteristics

We did not find any significant differences in parent or child sleep characteristics (based on actigraphy data) or sleep quality (based on parent questionnaires) related to child sex or race/ethnicity or related to use of insulin pumps or CGMs. Income was significantly related to parent and child sleep, such that child and parent sleep efficiency was lower among families reporting annual income <$80,000 year as compared to families with annual income ≥$80,000. In addition, parental sleep efficiency was significantly related to education, such that efficiency was lower among parents who did not have a college degree than in parents who had a degree (see Table 3).

Table 3.

Group differences in demographic and clinical sleep measures.

Child Sleep Characteristics CSHQ Parent Sleep Characteristics PSQI
CTST (h) CSE (%) C-Lat (min) Score PTST (h) PSE (%) Score
Child Sex
Male (n = 14) 7.99 85.17 8.14 44.10 6.80 87.89 8.83
Female (n = 26) 7.89 86.00 9.65 48.72 6.62 86.60 7.58
Race/Ethnicity
White, Non-Hispanic (n = 30) 7.98 85.85 8.86 46.73 6.80 87.39 7.77
Other (n = 9) 7.67 85.27 10.41 48.13 6.25 85.34 9.14
Income (USD)
<80,000 (n = 20) 7.79 84.34* 11.36 48.63 6.60 84.43* 9.05
>80,000 (n = 20) 8.05 87.10* 6.90 45.94 6.76 89.53* 6.89
Treatment Type
Insulin Pump (n = 25) 7.97 85.53 8.37 48.55 6.88 86.95 7.72
Injection (n = 15) 7.82 86.07 10.60 45.46 6.31 87.24 8.46
CGM
No CGM (n = 15) 7.77 85.29 10.75 46.86 6.36 84.86 8.71
CGM (n = 25) 8.01 86.00 8.07 47.76 6.88 88.38 7.54
Education
No Degree (n = 17) 7.77 85.13 11.56 47.43 6.32 83.40* 9.25
Degree (n = 23) 8.03 86.15 7.37 47.38 6.93 89.54* 7.05
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CTST = Child Total Sleep Time; CSE = Child Sleep Efficiency; C-Lat = Child Sleep Latency; CSHQ = Child Sleep Health Questionnaire; PTST =Parent Total Sleep Time; PSE = Parent Sleep Efficiency; PSQI = Parental Sleep Quality Index.

*

p <.05.

Associations between sleep, diabetes indicators, and family functioning

Bivariate correlations indicated a significant association between child and parent sleep quality (based on questionnaire measures), r = .57, p < .001. However, the correlation between parent and child total sleep time measured with actigraphy was not significant. We observed a significant association between parents’ total sleep time and sleep efficiency with children’s glycemic levels, such that higher HbA1c was related to lower parental sleep duration and efficiency (r = −.37, −.38, respectively, both p < .05). Greater child sleep latency was also linked with higher HbA1c (r = .41, p = .011), but child sleep duration and efficiency were not significantly related to glycemic levels .

Although none of the objective sleep measures were related to family functioning or parental psychosocial factors, we did observe a significant association between parental report of child sleep disturbances on the CSHQ with diabetes distress (r = .39, p = .021) and fear of hypoglycemia (r = .41, p = .013). In addition, we found several significant associations between parental measures of psychosocial and family functioning. First, higher levels of family chaos were associated with significantly greater diabetes distress (r = .53, p < .001) and fear of hypoglycemia (r = .42, p = .007) among parents. Similarly, fear of hypoglycemia was related to higher levels of diabetes distress (r = .62, p < .001).

Discussion

The current study is one of the first to examine sleep in early school-aged children with T1D and their parents using multiple types of sleep measures. Similar to studies using questionnaire measures of sleep (e.g., Jaser et al., 2017), we found that children were sleeping an average of 7.9 hours/night and parents were sleeping 6.7 hours/night, well below the recommended duration of 9–11 hours for children and 7–9 hours for adults. Questionnaire data indicated that the majority of children (80%) and parents (76%) in our sample experienced clinically significant sleep disturbances. Furthermore, child sleep characteristics were associated with parental sleep and with parents’ diabetes distress and fear of hypoglycemia.

Although sleep latency was the only child sleep characteristic significantly associated with glycemic levels, parental sleep duration and efficiency were both related to HbA1c. This likely reflects the role that parents play in diabetes management in school-aged children, as parents are waking up at night to check glucose levels and treat hypo or hyperglycemia, whereas children may not experience diabetes-related sleep disturbances. Alternatively, this finding could reflect that parental fear of hypoglycemia leads parents to engage in nocturnal caregiving to keep children’s blood glucose levels above range to avoid hypoglycemia, even when children use diabetes devices. Future studies are needed to identify when parent nighttime diabetes management tasks are performed due to fear of hypoglycemia, routine management tasks, or on an as-needed basis (e.g., responding to a CGM alert), in order to clarify these relationships.

The lack of group differences in sleep habits related to demographic or clinical factors is somewhat surprising, particularly the lack of differences related to diabetes devices. We may have expected to see better sleep quality among children using CGM devices, as these have the potential to reduce glycemic variability (Foster et al., 2019), but prior research has also indicated that sleep may be disrupted by diabetes devices (Barnard et al., 2016; Sinisterra et al., 2020). It is possible that we did not observe differences related to device use due to our relatively small sample size, or because these differences are more pronounced among the youngest children (preschool vs. school-aged children). Similarly, there were no differences found related to pump use, unlike a study of adolescents with T1D and young adults, in which those using insulin pumps experienced fewer sleep disturbances and longer sleep duration than those who administered insulin with injections (Jaser & Ellis, 2016). Again, this could be related to sample size or potentially differential impact of the device use on sleep in a younger age group . Taken together, these findings support that parents may need behavioral support and realistic expectations to gain maximum benefits from diabetes devices in this younger population, as the use of devices are unlikely to improve parents’ sleep quality.

The rates of clinically significant sleep disturbances in our sample were somewhat higher than those reported by parents in the T1D Exchange (T1DX) sample (80% vs. 67%), which may be due to families in the current study self-selecting to participate in a sleep-promoting intervention. Our sample also focused on a narrower age range (5–9 years) than the T1DX sample, which included children ages 212 years. Prospective observational studies are needed to show whether sleep quality among children with T1D differs across developmental stages. Observed differences in child and parent sleep efficiency related to income have been found in in 3rd and 4th graders (mean age 9 years), (e.g., El-Sheikh et al., 2013), which may reflect a family’s ability to provide an environment conducive to sleep (e.g., bedding, temperature, low noise levels).

While we did not detect a significant association between family chaos and sleep quality of school-aged children with T1D, chaos was associated with higher levels of diabetes distress and fear of hypoglycemia in parents. This link between chaos, or lack of family routines, may represent an area for intervention. Providers should consider asking about family routines - including bedtimes and sleep habits - as these may help to reduce parental distress and improve sleep quality.

Conclusions

Limitations

The current study was limited by the relatively small sample size, and a short duration of sleep measurement. Additionally, children and parents agreed to participate in a sleep-promoting intervention, so these findings may not generalize to other populations. Only three fathers participated in the study; however, the demographics and clinical characteristics of the child sample suggest that participants did not differ from other children seen in our outpatient diabetes clinic. Furthermore, while the majority of children in our sample were using CGMs, we did not have sufficient glucose data for all participants that would allow us to examine sleep in relation to glucose levels on a daily basis (Monzon et al., 2019).

Future directions

Findings from the current study highlight that most school-aged children with T1D and their parents do not meet recommendations for sleep duration, and they are experiencing poor sleep quality. Thus, addressing sleep disturbances in children with T1D and their caregivers has the potential to improve glycemic control, parental distress, or both. A fully powered trial of a sleep-promoting intervention is needed to test these hypotheses. Future research is needed to examine differences in sleep between types of caregivers, including comparisons between caregivers with different levels of responsibility for overnight diabetes management. In addition, longitudinal studies including detailed blood glucose data are needed to further assess the recursive cycle between sleep, glucose levels, and distress (Monzon et al., 2019). More complete CGM data could also be used to link overnight alarms and glycemic levels to sleep disturbances. Obtaining these data is likely to become easier because CGM use in this age group is rapidly increasing. Given how common sleep disturbances are in this population, diabetes care providers should ask about sleep as part of routine clinical practice, and offer tools to effectively and efficiently manage diabetes at night, as well as manage parental fear of hypoglycemia.

Funding

This work was supported by the Leona M. and Harry B. Helmsley Charitable Trust [2016PG-T1D053].

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

1

Due to contracting issues, we switched to a different brand of actigraph watch after starting enrollment, and no significant differences in parent or child sleep parameters were observed related to device brand.

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