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Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered. [ABSTRACT FROM AUTHOR]

Background: Young children with Type 1 diabetes (T1D) are at risk for extreme blood glucose variability, a risk factor for suboptimal glycated hemoglobin A1c (HbA1c) and long-term health complications. We know that a reciprocal relationship exists between sleep and glycemic outcomes in older youth with T1D; however, little research has examined objective sleep in young children (<7 years) with T1D.Purpose: This study examines bidirectional associations between sleep behaviors and glycemic variability in young children with T1D.Methods: Thirty-nine young children with T1D (Mage 4.33 ± 1.46 years; MHbA1c 8.10 ± 1.06%) provided accelerometry data to objectively measure sleep onset latency, number of nighttime awakenings, and total sleep time. We also assessed HbA1c, average blood glucose, and glycemic variability (i.e., standard deviation of blood glucose from device downloads). We evaluated bidirectional relationships using multilevel modeling in SAS, with weekday/weekend as a Level 2 moderator.Results: Children averaged 8.5 ± 1.44 hr of sleep per night, but only 12.8% met current sleep recommendations. Children experienced more nighttime awakenings, higher blood glucose, and more glycemic variability on weekends. Sleep onset latency and nighttime awakenings predicted greater glycemic variability on weekends, and weekend glycemic variability predicted increased nighttime awakenings.Conclusions: Most young children with T1D did not meet sleep recommendations. Young children experienced more nighttime awakenings, higher blood glucose, and increased glycemic variability on weekends only, when routines may be less predictable. Findings suggest that one way families of young children with T1D may be able to decrease glycemic variability is to keep consistent routines on weekdays and weekends. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the efficacy of a new video‐based telehealth intervention to reduce hypoglycemia fear in parents of young children with type 1 diabetes. Methods: We randomized 42 parents to either immediate treatment (reducing emotional distress for childhood hypoglycemia in parents, REDCHiP; n = 22) or a waitlist control (WAITLIST; n = 21) condition. REDCHiP parents completed a 10‐session video‐based telehealth intervention, while WAITLIST parents continued in usual care. After 14‐weeks, WAITLIST parents completed the telehealth treatment. We examined for between group changes in parental hypoglycemia fear and parenting stress (n = 18 per condition), 3‐month maintenance of treatment effects for parents randomized to REDCHiP (n = 15), and pre‐post changes for the entire sample (n = 36). Results: Mostly mothers participated (97.6%). They reported a mean age of 35.2 ± 5.0 years at pre‐treatment. Children were 4.4 ± 1.4 years old and 59.5% boys. Between group comparisons showed a significant reduction in hypoglycemia fear (P =.04) and a trend toward reduction in parenting stress‐frequency (P =.092) for REDCHiP parents compared to WAITLIST parents. After the three‐month maintenance period, REDCHiP parents reported significant reductions in hypoglycemia fear, parenting stress‐frequency, and parenting stress‐difficulty (P's <.01) compared to pre‐treatment. When all parents received the telehealth treatment, we also observed significant reductions in hypoglycemia fear, parenting stress‐frequency, and parenting stress‐difficulty (P's <.001), and sensitivity analyses revealed a significant reduction in child glycated hemoglobin for children who entered the treatment above target (P <.05). Conclusion: Our new video‐based telehealth intervention appears to reduce hypoglycemia fear and parenting stress and may help parents of very young children with T1D to better achieve optimal child glycemic control when children are above target. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: The Self-Efficacy for Diabetes Scale (SED) is a widely used measure of diabetes-specific self-efficacy with three subscales: diabetes-specific self-efficacy (SED-D), medical self-efficacy (SED-M), and general self-efficacy (SED-G). The present study examined the factor structure and construct validity of the SED in 116 youth, aged 10-16 years (13.60 ± 1.87), with type 1 diabetes mellitus.Methods: Confirmatory factor analysis (CFA) was used to examine the factor structure of the SED. Correlational and regression analyses examined relations between subscales and select outcomes.Results: CFA of the original three-factor structure provided a poor fit to the data. Factor models using rescaled items were tested. Results provided preliminary evidence for the SED-D as an independent one-factor model, and for a reduced one-factor model. Significant associations were found between the SED subscales, responsibility for diabetes management, and glycated hemoglobin.Conclusions: Results provide limited support for the SED-D as a reliable and valid measure of diabetes-specific self-efficacy. [ABSTRACT FROM AUTHOR]

Objective: We examined how parent and youth responsibility for type 1 diabetes (T1D) care is related to adherence and glycemic outcomes, namely, glycemic variability and risk of glycemic excursions.Methods: One hundred thirty-five parent-youth dyads (10-16 years old; diagnosed with T1D for at least 6 months) participated in this study. Percent responsibility of T1D care attributed to the youth, parent, or shared was measured using the Diabetes Family Responsibility Questionnaire. We collected youth's hemoglobin A1c (HbA1c) and glucometer downloads to examine relationships between responsibility and HbA1c, frequency of blood glucose monitoring (self-monitoring blood glucose, SMBG), risk of glycemic excursions, and actual glycemic variability using bivariate correlations and path analysis.Results: Participants reported shared responsibility for almost half of T1D self-care tasks. Bivariate correlations showed shared responsibility was associated with less variability, whereas parent responsibility was associated with greater glycemic variability and risk for glycemic excursions. Youth responsibility was associated with lower frequency of SMBG. The path analyses confirmed our correlational findings (ps<.05) and better characterized interactions with age for youth-reported responsibility.Conclusions: Our results support the hypothesis that shared T1D responsibility is associated with better diabetes outcomes in youth. [ABSTRACT FROM AUTHOR]

Background Adherence to the type 1 diabetes ( T1D) regimen, while predictive of glycemic control, decreases during adolescence. For adolescents, attaining adequate sleep is an additional challenge. This study evaluates the impact of sleep on adherence in teens with T1D. Subjects Forty-five adolescents aged 12-18 yr, with T1D for at least 6 months while on insulin pump therapy. Methods Adolescents logged their sleep on a written diary for 2 wk. Corresponding insulin pump/glucometer downloads as well as sleep habit questionnaires were also obtained. Results Data from 20 girls and 25 boys, with a mean age of 15 ± 1.6 yr and mean glycated hemoglobin of 8.7 ± 1.1% (72 mmol/mol), were analyzed. Overall, average sleep was 8.6 ± 0.9 h per night. Sleep durations were compared to the next day's frequency of self-monitored blood glucose ( SMBG) and total daily insulin bolus frequency. Associations were found between sleep duration and youths' SMBG and insulin bolus frequencies (p < 0.03 and p < 0.001, respectively). Specifically, a 15- and 20-min increase in sleep was associated with one additional SMBG check and one additional insulin bolus, respectively. Conclusion Analyses suggest an associated increase in T1D self-management behaviors in youths with increased sleep duration. These findings highlight the importance of assessing sleep in clinical practice, and encourage further research to examine effective strategies to address sleep hygiene as part of routine diabetes management. [ABSTRACT FROM AUTHOR]