Daily variations in sleep and glucose in adolescents with type 1 diabetes

Clinical relevance of short-term glycemic variability in children and adolescents with type 1 diabetes: a narrative review

Background and Objective

In recent years, there has been growing interest in glycemic variability within the scientific community, particularly regarding its potential as an independent risk factor for diabetes-related long-term complications. This narrative review aimed to provide a comprehensive overview of short-term glycemic variability in children and adolescents with type 1 diabetes (T1D).

Methods

We performed a search of published literature on the PubMed MEDLINE database using the following combination of search terms: "glycemic variability", "pediatric", "type 1 diabetes", and "children".

Key Content and Findings

The widespread use of continuous glucose monitoring (CGM) systems has facilitated the characterization and quantification of glycemic fluctuations. Over the years, several metrics for assessing glycemic variability have been developed. Children and adolescents with T1D often experience wide and frequent glycemic excursions due to behavioral and hormonal factors. Several studies suggest a potential link between glycemic variability and an increased risk of diabetes-related complications.

Conclusions

Glycemic variability has become an integral aspect of the routine clinical management of youths with T1D, serving as a valuable therapeutic target. However, achieving recommended glycemic targets in this population remains challenging. Further long-term data are needed to definitively establish the role of glycemic variability in the development of complications.

Impact of 6-months of an advanced hybrid closed-loop system on sleep and psychosocial outcomes in youth with type 1 diabetes and their parents

INTRODUCTION

Youth with type 1 diabetes (T1D) and parents experience reduced quality of life and sleep quality due to nocturnal monitoring, hypoglycemia fear, and diabetes-related disruptions. This study examined the sleep and quality of life impact of advanced technology.

METHODS

Thirty-nine youth with T1D, aged 2-17 years, starting an advanced hybrid closed-loop (HCL) system and a parent participated in an observational study. Surveys, actigraphy, sleep diaries, and glycemic data (youth) were captured prior to HCL, at one week, 3 months, and 6 months. Outcomes were modeled using linear mixed effects models with random intercepts to account for within-subject correlation, with least-squares means at each timepoint compared to baseline.

RESULTS

Parents and youth reported improvements in health-related quality of life and fear of hypoglycemia after HCL initiation. Concurrently, nocturnal glycemia improved. Actigraphy-derived sleep outcomes showed improved 6 month adolescent efficiency and 3 and 6 month parent wake after sleep onset. Additionally, parents reported improved subjective sleep quality and child sleep-related impairment at 3 months.

CONCLUSIONS

With nocturnal glycemic improvements in youth using HCL technology, some aspects of parent and youth sleep and quality of life improved. This may reflect decreased parental monitoring and worry and highlights benefits for youth beyond glycemia.

Associations between sleep characteristics and glycemic variability in youth with type 1 diabetes

OBJECTIVE

This study aimed to determine sleep characteristics and their associations with glycemic variability in youth with type 1 diabetes (T1D).

MATERIAL AND METHODS

This cross-sectional study conducted at two pediatric diabetes centers in Istanbul, Turkey, included 84 children with T1D (mean age 10.5 years). Sleep characteristics and glycemic variability were determined by actigraphy, DSM-5 Level 2-Sleep Disturbance Scale Short Form and continuous glucose monitoring. Circadian preference was evaluated by the Children's Chronotype Questionnaire. Sleep disturbances were assessed by the. The sleep quality was determined by actigraphy-derived sleep measures.

RESULTS

Eighty-eight percent of participants had insufficient age-appropriate total sleep time (TST) (<9 h for 6-13-year-olds and <8 h for 14-17-year-olds). Chronotype was classified as intermediate in 50%, evening in 45.2%, and morning in 4.8%. A higher chronotype score indicating a stronger eveningness preference was associated with more time spent in hypoglycemia (β = 0.433, p = 0.002). On nights when participants had lower sleep efficiency and longer sleep onset latency, they had significantly higher overnight glycemic variability (β = -0.343, p = 0.016, β = 0.129, p = 0.017, respectively). Prolonged nocturnal wake duration was significantly associated with more time spent in daytime hypoglycemia (β = 0.037, p = 0.046) and higher overnight glycemic variability (J index, β = 0.300, p = 0.015). The associations between TST and glycemic variability indices were not significant.

CONCLUSIONS

Sleep quality rather than TST was significantly associated with glycemic variability in children with T1D. Eveningness preference might contribute to an increased risk of hypoglycemia. Addressing sleep patterns and chronotypes can be crucial in management plans for youth with T1D.

Pediatric Obesity: Complications and Current Day Management

Obesity affects approximately 1 in 5 youth globally and increases the risk of complications during adolescence and young adulthood, including type 2 diabetes, dyslipidemia, hypertension, non-alcoholic fatty liver disease, obstructive sleep apnea, and polycystic ovary syndrome. Children and adolescents with obesity frequently experience weight stigma and have an impaired quality of life, which may exacerbate weight gain. Pediatric obesity is typically defined using sex-, age-, and population-specific body mass index percentiles. Once identified, pediatric obesity should always be managed with lifestyle modification. However, adolescents with obesity may also benefit from anti-obesity medications (AOM), several of which have been approved for use in adolescents by the US Food and Drug Administration, including liraglutide, phentermine/topiramate, and semaglutide. For children with specific, rare monogenic obesity disorders, setmelanotide is available and may lead to significant weight loss. Metabolic and bariatric surgery may be used for the management of severe obesity in youth; though highly effective, it is limited to specialized centers and has had relatively low pediatric uptake. In this narrative review using pediatric-focused data from original research, reviews, clinical practice guidelines, governmental agencies, and pharmaceutical companies, we review obesity-related metabolic complications in youth and management strategies, including AOM and bariatric surgery.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia

BACKGROUND

This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).

METHODS

Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.

RESULTS

Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).

CONCLUSION

AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Sleep health dimensions are associated with next-day symptoms in young adults with type 1 diabetes

OBJECTIVE

We investigated intra-individual reciprocal associations between sleep health dimensions (individual and composite) and symptoms among young adults with type 1 diabetes (T1D).

DESIGN AND MEASUREMENTS

Cross-lagged multilevel models were used to analyze electronic diary-reported sleep and symptom patterns over 7 days at waketime in 42 young adults with T1D. Sleep health dimensions included regularity, satisfaction, alertness, timing, efficiency (percentage of time spent asleep), and duration (total sleep time) and symptoms included mood, fatigue, and pain. Covariates included biological sex and age.

SETTING AND PARTICIPANTS

We recruited young adults (mean age 21.5 ± 2.1 years, HbA1c 6.8%, 85% female, 10% gender minority) with T1D for at least 6 months and no other major medical or psychiatric comorbidity from social media platforms, the College Diabetes Network, and ResearchMatch.

RESULTS

On days with a better sleep health composite, participants reported lower next-day symptoms (higher mood, lower fatigue, and lower pain) and on days when participants reported lower symptoms, participants reported better sleep health (as a composite). Several individual sleep health dimensions led to lower next-day symptoms (eg, higher satisfaction, alertness, and efficiency and higher mood); however, symptoms were no longer predictive of next-day sleep when controlling for prior day sleep.

CONCLUSIONS

Optimal sleep health is an antecedent of fewer next day symptoms. Sleep health dimensions likely have positive additive effects on lower symptoms as some of the individual sleep health dimensions were not significantly associated with some symptoms among young adults with T1D.

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.

24-Hour Movement Behaviours (Physical Activity, Sedentary Behaviour and Sleep) Association with Glycaemic Control and Psychosocial Outcomes in Adolescents with Type 1 Diabetes: A Systematic Review of Quantitative and Qualitative Studies

Type 1 Diabetes (T1D) is a condition requiring 24-hour management. The way in which an individual combines their 24-hour movement behaviours (24-h MBs), which is comprised of physical activity (PA), sedentary behaviour (SB), and sleep, throughout the day can have a significant impact on physical and mental health. This mixed methods systematic review aimed to investigate 24-h MBs' relationship with glycaemic control and psychosocial outcomes in adolescents (11-18 years) with T1D. Ten databases were searched for quantitative and qualitative English language articles reporting at least one of the behaviours and their relationship with outcomes. There were no restrictions on article publication dates or study design. Articles were subjected to title and abstract screening, full text screening, data extraction and quality assessment. Data were summarised narratively, and a meta-analysis was conducted where possible. From 9922 studies, 84 were included for data extraction (quantitative (n = 76), qualitative (n = 8)). Meta-analyses revealed a significant favourable association between PA and HbA1c (-0.22 [95% CI: -0.35, -0.08; I² = 92.7%; p = 0.001). SB had an insignificant unfavourable association with HbA1c (0.12 [95% CI: -0.06, 0.28; I² = 86.1%; p = 0.07]) and sleep had an insignificant favourable association (-0.03 [95% CI: -0.21, 0.15; I² = 65.9%; p = 0.34]). Importantly, no study investigated how combinations of behaviours collectively interacted and impacted on outcomes.

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.

Evaluating the influence of sleep quality and quantity on glycemic control in adults with type 1 diabetes

BACKGROUND

Sleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control.

MATERIALS AND METHODS

An observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality.

RESULTS

A total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure.

CONCLUSIONS

This study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.

Friend or Foe: a Narrative Review of the Impact of Diabetes Technology on Sleep

PURPOSE OF REVIEW

The purpose of this review is to present a review of sleep science, the relationship between sleep and type 1 diabetes, and highlight the current literature on sleep outcomes in adult and pediatric diabetes technology research.

RECENT FINDINGS

Sleep quality is associated with glycemic outcomes, diabetes self-management, and mental health in people with type 1 diabetes. Diabetes technologies, including insulin pumps, continuous glucose monitors, and hybrid closed-loop systems improve glycemic outcomes. However, many people find this technology challenging for a variety of reasons, including increased burden and frequent alarms, especially during the night. The impact of different devices on sleep quality and quantity has been mixed. The newest technology, the hybrid closed-loop systems, offers the best opportunity for nocturnal glycemic regulation and has improved patient and family perspectives on sleep quality. However, objective sleep assessment has not shown significant improvement on sleep duration. Sleep quality and quantity in people with type 1 diabetes are widely recognized as an important component of health care, and the literature regarding the impact of diabetes devices on sleep is increasing. However, sleep disruptions are common and a barrier to device use. Despite finding minimal changes to sleep duration with device use, subjective accounts of sleep quality are overall positive, especially in those using hybrid closed-loop systems. Sleep quantity and quality are important outcomes to consider as diabetes technology continues to evolve.

Development and validation of the Chrononutrition Profile - Diary

The circadian timing of food intake (i.e., chrononutrition) has been linked to various markers of health status, such as body weight and insulin sensitivity. However, a valid assessment of day-to-day, within-person patterns in chrononutrition has not yet been developed. This paper details the development and initial validity testing of the Chrononutrition Profile - Diary (CP-D). The CP-D assesses 6 components of chrononutrition that are likely to influence health (breakfast skipping, largest meal, evening eating, evening latency, night eating, and eating window). This measure demonstrated preliminary evidence of convergent validity with the ASA24 (Automated Self-Administered 24-hour dietary assessment tool). The CP-D is designed for use by both healthcare professionals and researchers. It can serve as an independent assessment of day-to-day patterns of chrononutrition, and can also be used along with existing dietary measures to provide a comprehensive assessment of participants' and patients' daily eating behaviors and meal timing patterns.

Rest-activity circadian rhythm and impaired glucose tolerance in adults: an analysis of NHANES 2011-2014

INTRODUCTION

Circadian rhythm disturbance occurs in type 2 diabetes, yet it is unknown whether it also exists in the prediagnostic phase of the disease. Thus, we examined the association of rest-activity circadian rhythm with 2-hour glucose levels and the risk of impaired glucose tolerance (IGT) in a nationally representative sample of adults without diabetes using a cross-sectional design.

RESEARCH DESIGN AND METHODS

We analyzed data from 2760 adults without diabetes (age ≥20) with at least 4 days of validated accelerometer recordings and a valid oral glucose tolerance test from the National Health and Nutrition Examination Survey 2011-2014. Non-parametric rest-activity circadian rhythm parameters were derived from the accelerometer recordings.

RESULTS

In the models adjusting for multiple covariates, a one-quantile increase in relative amplitude (ie, increased circadian rhythmicity) was associated with 2.66 mg/dL decrease in 2-hour glucose level (95% CI -3.94 to -1.38, p<0.001) and a decreased odds of IGT (OR 0.75, 95% CI 0.63 to 0.89, p=0.002). A one-quantile increase in intradaily variability (ie, increased rhythm fragmentation) was associated with 3.01 mg/dL increase in 2-hour glucose level (95% CI 1.52 to 4.49, p=0.001) and an increased odds of IGT (OR 1.37, 95% CI 1.19 to 1.58, p<0.001).

CONCLUSIONS

Circadian disruption is significantly associated with impaired glucose homeostasis in a general population of adults without diabetes. The association of circadian rhythm abnormalities with indicators of the pre-diabetic state suggests that circadian dysfunction may contribute to early disease pathogenesis.

Variations in Sleep Characteristics and Glucose Regulation in Young Adults With Type 1 Diabetes

CONTEXT

Short sleep duration and sleep disruptions are associated with impaired glucoregulation in type 1 diabetes (T1D). However, the mechanistic pathways between sleep and glucose variability remain unclear.

OBJECTIVE

To determine within- and between-person associations between objective sleep-wake characteristics and glucose variability indices.

METHODS

Multilevel models were used to analyze concurrent sleep and glucose patterns over 7 days in 42 young adults with T1D in their natural home environment. Young adults with T1D (mean age 22.2 ± 3.0 years, HbA1c 7.2%, 32.6% male) for at least 6 months with no other medical or major psychiatric comorbidity were included. Sleep-wake characteristics were measured via wrist actigraphy and glucose variability indices via a continuous glucose monitor (CGM).

RESULTS

Lower sleep efficiency predicted higher glucose variability (less time in range β = 0.011 and more time in hyperglycemia β = -0.011) within-person. A longer wake after sleep onset and more sleep disruptions were associated with higher glucose variability between persons (β = 0.28 and 0.31). Higher glucose variability predicted poorer sleep within-person (delayed bedtime, waketime, mid-sleep time, and lower sleep efficiency), while higher glucose variability was associated with poorer sleep and more sleep disruptions between persons (lower sleep efficiency, longer wake after sleep onset, and a higher sleep fragmentation index).

CONCLUSION

Clinicians can address the reciprocal nature of the sleep-glucose relationship by optimizing sleep and targeting efforts toward a euglycemic range overnight. Sleep habits are a modifiable personal target in diabetes care.

Too Much Dietary Flexibility May Hinder, Not Help: Could More Specific Targets for Daily Food Intake Distribution Promote Glycemic Management among Youth with Type 1 Diabetes?

Average glycemic levels among youth with type 1 diabetes (T1D) have worsened in some parts of the world over the past decade despite simultaneous increased uptake of diabetes technology, thereby highlighting the persistent need to identify effective behavioral strategies to manage glycemia during this life stage. Nutrition is fundamental to T1D management. We reviewed the evidence base of eating strategies tested to date to improve glycemic levels among youth with T1D in order to identify promising directions for future research. No eating strategy tested among youth with T1D since the advent of flexible insulin regimens—including widely promoted carbohydrate counting and low glycemic index strategies—is robustly supported by the existing evidence base, which is characterized by few prospective studies, small study sample sizes, and lack of replication of results due to marked differences in study design or eating strategy tested. Further, focus on macronutrients or food groups without consideration of food intake distribution throughout the day or day-to-day consistency may partially underlie the lack of glycemic benefits observed in studies to date. Increased attention paid to these factors by future observational and experimental studies may facilitate identification of behavioral targets that increase glycemic predictability and management among youth with T1D.

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).

Effect of meal composition and alcohol consumption on postprandial glucose concentration in subjects with type 1 diabetes: a randomized crossover trial

INTRODUCTION

Meal composition is known to affect glycemic variability and glucose control in type 1 diabetes. The objective of this work was to evaluate the effect of high carbohydrate meals of different nutritional composition and alcohol on the postprandial glucose response in patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Twelve participants were recruited to this randomized crossover trial. Following a 4-week run-in period, participants received a mixed meal on three occasions with the same carbohydrate content but different macronutrient composition: high protein-high fat with alcohol (0.7g/kg body weight, beer), high protein-high fat without alcohol, and low protein-low fat without alcohol at 2-week intervals. Plasma and interstitial glucose, insulin, glucagon, growth hormone, cortisol, alcohol, free fatty acids, lactate, and pH concentrations were measured during 6 hours. A statistical analysis was then carried out to determine significant differences between studies.

RESULTS

Significantly higher late postprandial glucose was observed in studies with higher content of fats and proteins (p=0.0088). This was associated with lower time in hypoglycemia as compared with the low protein and fat study (p=0.0179), at least partially due to greater glucagon concentration in the same period (p=0.04). Alcohol significantly increased lactate, decreased pH and growth hormone, and maintained free fatty acids suppressed during the late postprandial phase (p<0.001), without significant changes in plasma glucose.

CONCLUSIONS

Our data suggest that the addition of proteins and fats to carbohydrates increases late postprandial blood glucose. Moreover, alcohol consumption together with a mixed meal has relevant metabolic effects without any increase in the risk of hypoglycemia, at least 6 hours postprandially.

TRIAL REGISTRATION NUMBER

NCT03320993.

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.