Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort

AIMS/HYPOTHESIS

Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics.

METHODS

In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l).

RESULTS

A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%).

CONCLUSIONS/INTERPRETATION

Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes.

DATA AVAILABILITY

The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study

OBJECTIVE

To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia.

RESEARCH DESIGN AND METHODS

This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected.

RESULTS

A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h.

CONCLUSIONS

In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes

OBJECTIVE

To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study.

METHODS

Adolescents with T1D (n=225; [mean±SD] age=14±2 years; HbA1c=7.1±1.3%; T1D duration=5±4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGM), logged 3,738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within two hours after starting exercise.

RESULTS

RMRF achieved high precision predicting composite risk and was more accurate than RMLR (AUROC 0.737 vs. 0.661; P<0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 hours, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with a glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk.

CONCLUSIONS

Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.

Group engagement in parent-focused telehealth interventions for families of children with type 1 diabetes

INTRODUCTION

Group engagement is an important component of video-based telehealth interventions, yet this construct remains understudied. In the present study, we applied a multidimensional conceptualization of group engagement in two video-based telehealth interventions that either aimed to reduce fear of hypoglycemia or diabetes distress in parents of children with type 1 diabetes. We examined variability in group engagement across parents and assessed the relationship between parents' level of group engagement and their treatment outcomes.

METHODS

Twenty-nine parents participated in one of two manualized, closed-group, telehealth interventions and completed outcome measures pre- and post-treatment. We behaviorally coded telehealth sessions based on six dimensions of group engagement using the Group Engagement Measure (inter-rater reliability = 0.94). We examined correlations between group engagement dimensions, parent psychosocial well-being, and child hemoglobin A1c. Further, we examined independent sample t-tests to assess differences between treatment groups.

RESULTS

Mean parent age was 37.69  ±  6.83 years, mean child age was 7.69  ±  3.76 years, and mean child hemoglobin A1c was 8.06  ±  1.27% (41.4% had a hemoglobin A1c <7.5%). Parents who spent more time attending to other group member's issues, reported lower hypoglycemia fear at post-treatment, and parents who showed more active support of the group leader's purpose/goals during the session also reported fewer depressive symptoms at post-treatment.

DISCUSSION

We identify several dimensions of group engagement that are associate with improved parent psychosocial and child hemoglobin A1c outcomes. Intervention designs that use group engagement to guide treatment planning or inform treatment-related decisions in video-based telehealth interventions could help families achieve more optimal treatment outcomes.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes

Background

The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs.

Objective

Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise.

Methods

Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models.

Results

Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant.

Conclusions

Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative

AIMS

To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise.

METHODS

Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated.

RESULTS

Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose.

CONCLUSIONS

Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.

Online classification of unstructured free-living exercise sessions in people with Type 1 Diabetes

BACKGROUND

Managing exercise in type 1 diabetes (T1D) is challenging, in part because different types of exercise can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval or resistance for the purpose of incorporation into an automated insulin delivery (AID) system.

METHODS

A long short-term memory (LSTM) network model was developed with real world data from 30-minute structured sessions of at-home exercise (aerobic, resistance, or mixed) using tri-axial accelerometer, heart rate and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose.

RESULTS

A total of 1610 structured exercise sessions were used to train, validate and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval and 77% for resistance. Additionally, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dl was achieved for sessions classified as aerobic, -16.2 (39.0) mg/dl for sessions classified as interval and -11.6 (38.8) mg/dl for sessions classified as resistance.

CONCLUSIONS

The proposed algorithm reliably identified physical activity associated with expected change in glucose that could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.

The Relationship Between Parent Fear of Hypoglycemia and Youth Glycemic Control Across the Recent-Onset Period in Families of Youth with Type 1 Diabetes

BACKGROUND

This study aims to examine the relationship between parents' fear of hypoglycemia (FH) over a 1-year period and child glucose metrics in 126 families of youth recently diagnosed with type 1 diabetes (T1D).

METHODS

Parents completed the Hypoglycemia Fear Survey for Parents (HFS-P) and uploaded 14 days of glucose data at a baseline, 6-month, and 12-month assessment.

RESULTS

Parents' HFS-P total and worry scores increased to a clinically meaningful degree from baseline to 6-month assessment, while multilevel models revealed within- and between-person variability in parents' HFS-P worry and behavior scores over time associated with child glycemia. Specifically, a significant negative relationship for within-person worry scores suggested that when parents reported higher than their average worry scores, their children recorded fewer glucose values in the target range, while within-person behavior scores suggested that when parents reported lower than their average behavior scores, their children recorded more values above the target range. There was also a negative relationship for between-person behavior scores with child glycated hemoglobin and a positive relationship for between-person behavior scores with child glucose values in the target range.

CONCLUSIONS

In the recent-onset period of T1D, parental FH worry and behavior associated with child glycemia possibly due to changes in parents' perceptions of their child's hypoglycemia risk. The clinically meaningful increases in parent FH in the recent-onset period and the negative association for between-person behavior scores with child glycated hemoglobin suggest that clinics should consider screening parents for FH, especially among parents of children with lower glycemic levels.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study

OBJECTIVE

Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes.

RESEARCH DESIGN AND METHODS

In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads.

RESULTS

Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001).

CONCLUSIONS

Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.

Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections

OBJECTIVE

Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia.

MATERIALS AND METHODS

We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations.

RESULTS

The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico.

DISCUSSION

Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events.

CONCLUSION

A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.

Parental diabetes distress is a stronger predictor of child HbA1c than diabetes device use in school-age children with type 1 diabetes

INTRODUCTION

Diabetes distress (DD) describes the unrelenting emotional and behavioral challenges of living with, and caring for someone living with, type 1 diabetes (T1D). We investigated associations between parent-reported and child-reported DD, T1D device use, and child glycated hemoglobin (HbA1c) in 157 families of school-age children.

RESEARCH DESIGN AND METHODS

Parents completed the Parent Problem Areas in Diabetes-Child (PPAID-C) and children completed the Problem Areas in Diabetes-Child (PAID-C) to assess for DD levels. Parents also completed a demographic form where they reported current insulin pump or continuous glucose monitor (CGM) use (ie, user/non-user). We measured child HbA1c using a valid home kit and central laboratory. We used correlations and linear regression for our analyses.

RESULTS

Children were 49% boys and 77.1% non-Hispanic white (child age (mean±SD)=10.2±1.5 years, T1D duration=3.8±2.4 years, HbA1c=7.96±1.62%). Most parents self-identified as mothers (89%) and as married (78%). Parents' mean PPAID-C score was 51.83±16.79 (range: 16-96) and children's mean PAID-C score was 31.59±12.39 (range: 11-66). Higher child HbA1c correlated with non-pump users (r=-0.16, p<0.05), higher PPAID-C scores (r=0.36, p<0.001) and higher PAID-C scores (r=0.24, p<0.001), but there was no association between child HbA1c and CGM use. A regression model predicting child HbA1c based on demographic variables, pump use, and parent-reported and child-reported DD suggested parents' PPAID-C score was the strongest predictor of child HbA1c.

CONCLUSIONS

Our analyses suggest parent DD is a strong predictor of child HbA1c and is another modifiable treatment target for lowering child HbA1c.

Mealtime Insulin BOLUS Score More Strongly Predicts HbA1c Than the Self-Care Inventory in Youth With Type 1 Diabetes

BACKGROUND

To meet their glycated hemoglobin (HbA1c) goals, youth with type 1 diabetes (T1D) need to engage with their daily T1D treatment. The mealtime insulin Bolus score (BOLUS) is an objective measure of youth's T1D engagement which we have previously shown to be superior to other objective engagement measures in predicting youth's HbA1c. Here, to further assess the BOLUS score's validity, we compared the strengths of the associations between youth's HbA1c with their mean insulin BOLUS score and a valid, self-report measure of T1D engagement, the Self-Care Inventory (SCI).

METHODS

One-hundred and five youth with T1D self-reported their T1D engagement using the SCI. We also collected two weeks of insulin pump data and a concurrent HbA1c level. We scored youth's SCI and calculated their mean insulin BOLUS score using standardized methods. For the analyses, we performed simple correlations, partial correlations, and multiple regression models.

RESULTS

Youth had a mean age of 15.03 ± 1.97 years, mean time since diagnosis of 8.11 ± 3.26 years, and a mean HbA1c of 8.78 ± 1.49%. The sample included n = 58 boys (55%) and n = 96 families (91%) self-identified as white. Simple correlations between youth's age, HbA1c, SCI total score, and BOLUS score were all significant. Partial correlation and regression models revealed that youth's insulin BOLUS score was more strongly associated with HbA1c than the SCI.

CONCLUSIONS

Youths' BOLUS score has better concurrent validity with HbA1c than the SCI. We should consider reporting the BOLUS score as an outcome metric in insulin pump data reports.

The Development and Initial Validation of Items to Assess Parent Fear of Nighttime Hypoglycemia

OBJECTIVE

Parents of youth with type 1 diabetes (T1D) are fearful their children will experience nighttime hypoglycemia. Currently, the Hypoglycemia Fear Survey for Parents (HFS-P) lacks items that specifically assess parents' nighttime fear. This study aimed to fill this gap by rigorously identifying new items to specifically assess parent fear of nighttime hypoglycemia and then examine the psychometric properties of the revised Hypoglycemia Fear Survey for Parents including Nighttime Fear (HFS-P-NF).

METHODS

For Phase 1, we recruited 10 pediatric diabetes providers and 15 parents/caregivers of youth with T1D to generate items related to fear of nighttime hypoglycemia. For Phase 2, we recruited an additional 20 parents/caregivers to pilot-test the newly generated items. For Phase 3, we recruited another 165 parents/caregivers to evaluate structural validity via confirmatory factor analyses, reliability, and content validity of the revised HFS-P-NF.

RESULTS

In Phase 1, we generated 54 items. In Phase 2, we removed 34 items due to violations of distributional normality and nonsignificant correlations. In Phase 3, a four-factor model reflecting behaviors maintaining high glucose, helplessness, negative social consequences, and nighttime worries was the best fitting model for the HFS-P-NF. The new items demonstrated strong internal consistency (α = 0.96) and strong to moderate relationships with criterion and content validity measures.

CONCLUSION

The current study provides initial evidence of validity and reliability for new items on the HFS-P-NF that broadened the conceptualization of parent fear of nighttime hypoglycemia. These findings are important to clinicians who may consider screening for parent fear of nighttime hypoglycemia more comprehensively.

Development of an "all-data-on-hand" deep learning model to predict hospitalization for diabetic ketoacidosis (DKA) in youth with type 1 diabetes (T1D)

BACKGROUND

While prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically use of a long short-term (LSTM) model, to accurately predict 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D).

OBJECTIVE

To describe the development of a LSTM model to predict 180-day risk of DKA-related hospitalization for youth with T1D.

METHODS

We used 17 consecutive calendar quarters of clinical data (01/10/2016-03/18/2020) for 1745 youth 8 to 18-years with T1D from a pediatric diabetes clinic network in the Midwestern US. Input data included demographics, discrete clinical observations (lab results, vital signs, anthropometric measures, diagnosis and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and non-diabetes-related clinical notes via natural language processing (NLP). We trained the model using input data from quarters 1-7 (n=1377), validated using input from quarters 3-9 in a partial out-of-sample cohort (OOS-P; n=1505), and further validated in a full out-of-sample cohort (OOS-F; n=354) with input from quarters 10-15.

RESULTS

DKA admissions occurred at a rate of 5% per 180-days in both OOS cohorts. For the OOS-P and OOS-F cohorts, respectively: median age was 13.7 years (IQR=11.3,15.8) and 13.1 years (10.7,15.5); and HbA1c at enrollment was 8.6% (7.6,9.8) [70 (60,84) mmol/mol] and 8.1% (6.9,9.5) [65 (52,80) mmol/mol]; 14% and 13% had prior DKA admissions (post-T1D-diagnosis); and recall was 0.33 and 0.50 for the top-ranked 5% of youth with T1D. For lists rank-ordered by probability of hospitalization, precision increased from 0.33 to 0.56 to 1.0 for positions 1-80, 1-25, and 1-10 in the OOS-P cohort and from 0.50 to 0.60 to 0.80 for positions 1-18, 1-10, and 1-5 in the OOS-F cohort.

CONCLUSIONS

The proposed LSTM model for predicting 180-day DKA-related hospitalization is valid in the present sample. Future work should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (e.g., racially and/or socioeconomically diverse cohorts). Rank-ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.

Physical Activity Management for Youth With Type 1 Diabetes: Supporting Active and Inactive Children

Regular physical activity and exercise are important for youth and essential components of a healthy lifestyle. For youth with type 1 diabetes, regular physical activity can promote cardiovascular fitness, bone health, insulin sensitivity, and glucose management. However, the number of youth with type 1 diabetes who regularly meet minimum physical activity guidelines is low, and many encounter barriers to regular physical activity. Additionally, some health care professionals (HCPs) may be unsure how to approach the topic of exercise with youth and families in a busy clinic setting. This article provides an overview of current physical activity research in youth with type 1 diabetes, a basic description of exercise physiology in type 1 diabetes, and practical strategies for HCPs to conduct effective and individualized exercise consultations for youth with type 1 diabetes.

Is There an Optimal Time of Day for Exercise? A Commentary on When to Exercise for People Living With Type 1 or Type 2 Diabetes

Exercise is a cornerstone of diabetes self-care because of its association with many health benefits. Several studies that have explored the best time of day to exercise to inform clinical recommendations have yielded mixed results. For example, for people with prediabetes or type 2 diabetes, there may be benefits to timing exercise to occur after meals, whereas people with type 1 diabetes may benefit from performing exercise earlier in the day. One common thread is the health benefits of consistent exercise, suggesting that the issue of exercise timing may be secondary to the goal of helping people with diabetes establish an exercise routine that best fits their life.

Examining the Acute Glycemic Effects of Different Types of Structured Exercise Sessions in Type 1 Diabetes in a Real-World Setting: The Type 1 Diabetes and Exercise Initiative (T1DEXI)

OBJECTIVE

Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear.

RESEARCH DESIGN AND METHODS

The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4 weeks. Participants self-reported study and nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data.

RESULTS

A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 6.6 ± 0.8% [49 ± 8.7 mmol/mol]) assigned to structured aerobic (n = 162), interval (n = 165), or resistance (n = 170) exercise were analyzed. The mean (± SD) change in glucose during assigned exercise was -18 ± 39, -14 ± 32, and -9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70-180 mg/dL (3.9-10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001).

CONCLUSIONS

Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time in range but may have slightly increased time below range.

Telehealth and Type 1 Diabetes Care During COVID-19: Perceptions From Youth of Color, Caregivers, and Health Care Providers

Objective

At the outset of the coronavirus disease 2019 (COVID-19) pandemic, health care systems rapidly implemented telehealth services to maintain continuity of type 1 diabetes care. Youth of color are more likely to have suboptimal glycemic control and may benefit most from efforts to ensure continuity of care. However, research examining the perspectives of families of youth of color regarding telehealth for pediatric type 1 diabetes care is limited. We gathered perspectives from youth of color, their caregivers, and health care providers (HCPs) on telehealth for type 1 diabetes care during COVID-19.

Methods

Fifty participants (22 caregivers, 19 youth, and nine HCPs) completed semi-structured interviews conducted in English (n = 44) or Spanish (n = 6). Transcripts containing mentions of telehealth (n = 33) were included for qualitative analysis to extract themes pertaining to perceptions of type 1 diabetes care and telehealth use during COVID-19.

Results

Themes related to perceptions, feasibility, and quality of telehealth diabetes care were obtained. Most families had positive perceptions of telehealth. Families and HCPs described logistical and technical challenges and noted the potential for disparities in telehealth access and use. Furthermore, caregivers and HCPs felt that the lack of in-person interaction and limited access to clinical data affected the quality of care.

Conclusion

Families of youth of color with type 1 diabetes mostly had positive perceptions of telehealth but also identified issues with feasibility and quality of care. Our findings highlight a need for interventions promoting equal access to telehealth and quality care for all youth with type 1 diabetes to minimize disruptions in care.

Cash-Only INcentives to promote insulin DOSE engagement: A protocol paper for the pilot randomized controlled trial of COIN2DOSE

BACKGROUND

Most adolescents with type 1 diabetes (T1D) do not achieve a glycated hemoglobin (HbA1c) <7.0%, which is the current clinical target. mHealth can offer a scalable and age-appropriate delivery method for behavioral interventions to lower adolescents' HbA1c levels, while applying established behavior change and behavioral economics theories can enhance scientific rigor.

METHODS

We aim to conduct a pilot randomized clinical trial of a novel mHealth intervention called Coin2Dose (Cash-Only INcentives To promote insulin DOSE engagement), in a sample of youth with T1D: 1) to obtain measures of feasibility and acceptability and 2) to examine preliminary efficacy versus a standard care control group based on differences in youth's daily BOLUS scores, HbA1c levels, and Time in Range (TIR) at post-intervention and 3-month post-intervention follow-up. This pilot RCT is already registered in http://ClinicalTrials.gov (NCT#05280184).

RESULTS

Our pilot will recruit youth with T1D 11-17 years-old who use an insulin pump or Bluetooth connected insulin pen and have an average daily BOLUS score ≤2.5. Youth randomized to Coin2Dose will receive the intervention for 12 weeks followed by a 12-week maintenance period. The pilot is scheduled to start July 2022 and to conclude in 2025.

DISCUSSION

At the conclusion of the pilot, we will have information about the feasibility and acceptability of two different behavioral economic incentive structures for improving BOLUS scores. The work is anticipated to progress to final efficacy trial. We will disseminate study results through presentations at local, national, and international conferences and through peer-reviewed diabetes and psychology journals.

An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes

BACKGROUND

Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D).

METHODS

We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample's CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period.

RESULTS

The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D.

CONCLUSIONS

Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.

Risky self-management behaviors in adolescents with type 1 diabetes: Measurement validation for the Diabetes-Specific Risk-Taking Inventory

OBJECTIVE

Among persons with type 1 diabetes (T1D), adolescents often experience the greatest challenge achieving optimal treatment engagement and glycemic targets. Risk-taking behaviors often increase during adolescence and may interfere with engagement in T1D care. We developed the Diabetes-Specific Risk-Taking Inventory (DSRI) to assess risky T1D self-management behaviors in adolescents with T1D. In the current study, we aimed to examine the DSRI's psychometric properties.

RESEARCH DESIGN AND METHODS

We surveyed a national sample of 224 adolescents from the T1D Exchange registry (M age = 16.9 ± 1.1, 49% female, M A1c = 8.5% ± 1.3, 76.8% on insulin pumps) in a cross-sectional design. Participants completed the DSRI and measures of engagement, general risk-taking, and executive functioning and reported on incidence of severe hypoglycemia and diabetic ketoacidosis over the past year.

RESULTS

The DSRI demonstrated reliability (internal consistency: α = 0.89; test-retest reliability: r = 0.86, p < 0.01). Concurrent validity was demonstrated through significant associations between the DSRI and T1D engagement (r = -0.75), general risk-taking (r = 0.57), executive dysfunction (r = 0.34), and report of severe hypoglycemia over the past year (r = 0.22). The DSRI accounted for unique variance in adolescents' most recent glycated hemoglobin, above and beyond other variables, indicating its incremental validity.

CONCLUSIONS

Overall, initial psychometrics suggest the DSRI is a reliable and valid measure of risks that adolescents may take with their T1D care. This innovative self-report measure has potential to be an actionable clinical tool to screen for high-risk behaviors not routinely assessed in T1D clinical care.

Adjusted Cutoff Scores Increase Sensitivity of Depression Screening Measures in Adolescents With Type 1 Diabetes

OBJECTIVE

To measure the acceptability and diagnostic accuracy of commonly used depression screening measures to determine ideal cutoff scores that sensitively identify depressive disorders in adolescents with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

One hundred adolescents (12-17 years old) completed a reference standard, semistructured diagnostic interview and both long and short versions of five commonly used depression screening measures in the United States. To assess feasibility and acceptability, we used screener completion time and participant ratings, respectively. We used descriptive statistics, area under the receiver operating characteristic (ROC) curve analyses, and paired-sample area differences under the ROC curve to assess each measure's diagnostic validity against our reference standard and to determine ideal cutoff scores for this sample.

RESULTS

Adolescents had a mean age of 15.0 ± 1.7 years, time since T1D diagnosis of 6.0 ± 4.1 years, and glycated hemoglobin (HbA1c) of 8.9 ± 1.8%. Sixty percent of adolescents were male, 15% endorsed a current depressive disorder, and 15% endorsed lifetime suicidality. Measures demonstrated low sensitivity (0.33-0.67) to detect current depressive disorders using preexisting cutoff scores. However, adjusted cutoff scores increased sensitivity and reduced false negatives. All depression screening measures demonstrated "good" to "excellent" predictive validity, and the Children's Depression Inventory-2 Short version demonstrated significantly greater diagnostic accuracy than the Patient Health Questionnare-2 item version for adolescents.

CONCLUSIONS

Clinics should consider using screening measures with the greatest diagnostic accuracy as identified in this study and adjusting measure cutoff scores to increase sensitivity and reduce false negatives.

Physical Activity, Glycemic Variability, and Parental Hypoglycemia Fear in Preschoolers With Type 1 Diabetes

PURPOSE

The authors examined associations between preschoolers' daily glycemic variability, parents' report of hypoglycemia fear, and preschoolers' daily moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) in 25 families of preschoolers with type 1 diabetes.

METHODS

Parents completed a valid measure of hypoglycemia fear, and their child wore an accelerometer for up to 7 days. Parents provided glucose data from their child's devices. The authors used multiple regression and multilevel modeling to analyze their data.

RESULTS

Preschoolers (mean age 4.2 [1.7] y; 50% boys) engaged in a mean of 154.5 (59.6) and 339.2 (85.1) minutes of MVPA and SB per day, respectively, and parents reported relatively low levels of hypoglycemia worry and avoidance behaviors. Preschoolers' SB (r = .19, P = .02) and MVPA (r = -.20, P = .01) levels were significantly correlated with parental hypoglycemia worry scores but not with parents' hypoglycemia behavior scores (P = .15 and P = .92, respectively). While multilevel models did not show an association between MVPA and preschoolers' glycemic variability, preschoolers who engaged in more daily SB experienced higher glycemic variability (P = .04).

CONCLUSIONS

Research exploring MVPA, SB, and parental hypoglycemia fear in preschoolers with type 1 diabetes could have important clinical implications because it may reveal modifiable treatment targets that can impact preschoolers' health and activity patterns.

Childhood diabetes and sleep

Sleep modulates glucose metabolism, both in healthy states and in disease. Alterations in sleep duration (insufficient and excessive) and obstructive sleep apnea may have reciprocal ties with obesity, insulin resistance and Type 2 diabetes, as demonstrated by emerging evidence in children and adolescents. Type 1 diabetes is also associated with sleep disturbances due to the influence of wide glycemic fluctuations upon sleep architecture, the need to treat nocturnal hypoglycemia, and the need for glucose monitoring and insulin delivery technologies. In this article, we provide an extensive and critical review on published pediatric literature regarding these topics, reviewing both epidemiologic and qualitative data, and provide an overview of the pathophysiology linking sleep with disorders of glucose homeostasis.

Psychosocial Needs for Newly Diagnosed Youth with Type 1 Diabetes and Their Families

PURPOSE OF REVIEW

To synthesize findings from studies published within the last 5 to 10 years and recruiting families of children with new-onset type 1 diabetes (T1D).

RECENT FINDINGS

Children can establish glycated hemoglobin (HbA1c) trajectories in the new-onset period that may persist for up to a decade. Demographic factors, family conflict, and diabetic ketoacidosis at the time of diagnosis may be risk factors for sub-optimal child HbA1c, while new immune modulating therapies and a treatment approach that combines advanced technologies and remote patient monitoring may improve child HbA1c. Nonetheless, recent trials in the new-onset period have largely overlooked how treatments may impact families' psychosocial functioning and longitudinal observational studies have been limited. The new-onset period of T1D is an important time for research and clinical intervention, though gaps exist specific to families' psychosocial needs. Filling these gaps is essential to inform clinical management and standard of care guidelines and improve outcomes.

Remedy to Diabetes Distress (R2D2): Development protocol for a scalable screen-to-treat program for families of school-age children

BACKGROUND

School-age children with type 1 diabetes (T1D) need help from parents or other adult caregivers (caregivers) to effectively manage T1D, resulting in greater vulnerability to Diabetes Distress (DD) for both children and caregivers. Unfortunately, there are no scalable screen-to-treat programs for clinics to adopt to identify and treat DD in school-age families.

METHODS AND ANALYSES

We aim to design a scalable, clinic-based screen-to-treat program for DD in families of school-age children and to test whether our new program to reduce caregiver and child DD also reduces child glycemic levels. Our Remedy to Diabetes Distress (R2D2) program will target caregivers and children with T1D who are between 8 and 12 years old. It will merge routine and automated surveillance of DD in the clinical setting with at home digital delivery of a theory-based behavioral and psychological treatment of DD. We will use the ORBIT Model for Behavioral Intervention development to guide four small and cost-effective formative studies to develop our R2D2 program and assess initial treatment effects. In tandem, we will implement clinic-based DD screening in school-age families and assess feasibility and acceptability of our screening platform as a Quality Improvement activity. The study started in September 2020 and is scheduled to conclude in 2025.

CONCLUSIONS

The study will use a single Institutional Review Board (IRB) with Children's Mercy-Kansas City operating as the IRB of record. We will disseminate study results through presentations at scientific conferences and through peer-reviewed journals read by the psychology and diabetes care communities.

Applying Behavioral Economics Theories to Interventions for Persons with Diabetes

PURPOSE OF REVIEW

To introduce behavioral economics (BE), provide a description of how recent prevention and treatment interventions in persons with diabetes have incorporated BE in their intervention strategies, and discuss how BE could be used to inform new treatments for the clinical setting or research.

RECENT FINDINGS

In most of the trials described, researchers incorporated BE into their design in the form of incentives, which can align with present bias, optimism bias, and loss aversion. With only two exceptions, these trials reported preliminary support for using incentives to promote lifestyle modifications and diabetes-related tasks. Additionally, two trials reported promising results for behavior change strategies informed by default bias, while three trials reported promising results for behavior change strategies informed by social norms. Recent trials incorporating BE in prevention and treatment interventions for persons with diabetes generally report promising results, though gaps exist for research and clinical deployment.

A Nonrandomized Pilot of a Group Video-Based Telehealth Intervention to Reduce Diabetes Distress in Parents of Youth with Type 1 Diabetes Mellitus

OBJECTIVES

Our aim in this study was to refine and pilot a video-based telehealth intervention to reduce diabetes distress, depressive symptoms and hypoglycemia fear in parents of school-age children with type 1 diabetes and to assess for changes in child glycated hemoglobin (A1C).

METHODS

We recruited 41 parents of children (5 to 12 years) to participate in a manualized, video-based telehealth intervention (Cognitive Adaptions to Reduce Emotional Stress [CARES]). Of these, 29 parents completed either a 12-week (n=13) or 8-week (n=16) version of CARES based on the timing of their recruitment. We assessed feasibility (i.e. attrition, attendance) and parent satisfaction with CARES. We used repeated-measures analysis of variance with parent group (8 vs 12 sessions) as a between-subject variable and time as a within-subject variable to measure change in our dependent variables.

RESULTS

Mostly mothers participated (97.3%). Parents' mean age was 39.65±6.84 years and children's mean age was 9.86±1.57 years at pretreatment. CARES had low attrition (20% to 25%) and good attendance (96% to 98%). Parents also reported high levels of treatment satisfaction (>85%). There were significant main effects for time for parent-reported diabetes distress and depressive symptoms at posttreatment and 3-month follow-up. There was a statistical trend suggesting a time × group interaction for parent depressive symptoms at posttreatment. There was a significant main effect for time for hypoglycemia fear at the 3-month follow-up but no change at posttreatment. There was no change in child A1C at posttreatment.

CONCLUSION

CARES showed high parent satisfaction, good feasibility and promising results for reducing diabetes distress in parents of school-age children with type 1 diabetes.

Erratum: Feasibility of Implementing a Pediatric Diabetes Clinic via Telehealth. Diabetes Spectrum 2021;34:190-197 (https://doi.org/10.2337/ds20-0060)

[This corrects the article on p. 190 in vol. 34, PMID: 34149260.].

Longitudinal associations between family conflict, parent engagement, and metabolic control in children with recent-onset type 1 diabetes

INTRODUCTION

We prospectively investigated the associations between diabetes-related family conflict, parent engagement in child type 1 diabetes (T1D) care, and child glycated hemoglobin (HbA1c) in 127 families of school-age children who we recruited within the first year of their T1D diagnosis.

RESEARCH DESIGN AND METHODS

Parents completed the Diabetes Family Conflict Scale-Revised (DFCS-R) to assess for diabetes-related family conflict and the Diabetes Self-Management Questionnaire-Brief (DSMQ-Brief) to assess parent engagement in child T1D care at the initial study visit (T1) and at 12 (T2) and 27 (T3) months later. We also collected child HbA1c at these time points. Our analyses included Pearson correlations and repeated measures linear mixed models controlling for child age, sex, and T1D duration at T1.

RESULTS

Parents' DFCS-R scores negatively correlated with DSMQ-Brief scores (r=-0.13, p<0.05) and positively correlated with children's HbA1c (r=0.26, p<0.001). In our linear mixed models, parents' DSMQ-Brief scores were unchanged at T2 (β=-0.71, 95% CI -1.59 to 0.16) and higher at T3 (β=8.01, 95% CI 6.89 to 9.13) compared with T1, and there was an association between increasing DFCS-R and decreasing DSMQ-Brief scores (β=-0.14, 95% CI -0.21 to -0.06). Child HbA1c values were significantly higher at T2 (β=0.66, 95% CI 0.38 to 0.94) and T3 (β=0.95, 95% CI 0.63 to 1.27) compared with T1, and there was an association between increasing DFCS-R scores and increasing child HbA1c (β=0.04, 95% CI 0.02 to 0.06).

CONCLUSIONS

Increasing diabetes-specific family conflict early in T1D may associate with decreasing parent engagement in child T1D care and increasing child HbA1c, suggesting a need to assess and intervene on diabetes-specific family conflict. Trial registration number NCT03698708.

Continuous Glucose Monitoring Decreases Hypoglycemia Avoidance Behaviors, but Not Worry in Parents of Youth With New Onset Type 1 Diabetes

BACKGROUND

Existing research shows that hypoglycemia fear (HF) is common in parents of children with established type 1 diabetes (T1D). We examined parental HF in the T1D recent-onset period and evaluated whether continuous glucose monitoring (CGM) adoption relates to improved outcomes of parental HF.

METHODS

In TACKLE-T1D, a prospective study of five- to nine-year olds with recent-onset T1D, parents completed the Hypoglycemia Fear Survey-Parents (HFS-P) at baseline (T1) and 6 (T2) and 12 (T3) months post-baseline. The HFS-P measures worry about hypoglycemia (HFS-Worry score) as well as hypoglycemia avoidance behaviors (HFS-Behavior score). We recorded CGM start dates for youth during the same time period through medical record review.

RESULTS

Between T1 and T2, 31 youth (32.3%) initiated CGM therapy, and between T2 and T3, an additional 17 youth (17.7%) began using CGM, leaving 48 youth who never initiated CGM therapy (50%) in the recent-onset period. Parents reported moderate HFS-Worry scores at T1 (32.9 ± 11.9), which increased between T1 and T2 (37.6 ± 11.4, P < .001) and plateaued between T2 and T3 (37.7 ± 12.4, P = .89). In contrast, parental HFS-Behavior scores decreased between T1 (33.1 ± 5.8) and T2 (32.2 ± 6.0, P = .005) and plateaued between T2 and T3 (32.2 ± 6.0, P = .95). Baseline HFS-Behavior and Worry scores were associated with increased adoption of CGM between T1-T2 and T2-T3, respectively. Parents of children initiating CGM therapy between T1 and T2 showed the largest decrease in HFS-Behavior (P = .03).

CONCLUSIONS

Initiating CGM therapy within the first 12 months of T1D may help reduce parents' use of hypoglycemia avoidance behaviors, but has little effect on parents' hypoglycemia worry.

Identifying HbA1c trajectories and modifiable risk factors of trajectories in 5- to 9-year-olds with recent-onset type 1 diabetes from the United States

OBJECTIVE

To explore glycated haemoglobin (HbA1c) patterns in 5- to 9-year-olds in the recent-onset period of type 1 diabetes and identify parent psychosocial factors that may predict children's HbA1c trajectory using a prospective, longitudinal design.

RESEARCH DESIGN AND METHODS

We measured family demographics and parent psychosocial factors at baseline. We collected HbA1c levels from children every 3 months for up to 30 months. Deriving several features around HbA1c trends, we used k-means clustering to group trajectories and linear and logistic regressions to identify parent psychosocial predictors of children's HbA1c trajectories.

RESULTS

The final cohort included 106 families (48 boys, mean child age 7.50 ± 1.35 years and mean diabetes duration 4.71 ± 3.19 months). We identified four unique HbA1c trajectories in children: high increasing, high stable, intermediate increasing and low stable. Compared to a low stable trajectory, increasing parent-reported hypoglycaemia fear total score was associated with decreased odds of having a high stable or intermediate increasing trajectory. Increasing parent-reported diabetes-specific family conflict total score was associated with increased odds of having a high stable or intermediate increasing trajectory.

CONCLUSIONS

We are the first to identify distinct HbA1c trajectories in 5- to 9-year-olds with recent-onset type 1 diabetes as well as parent psychosocial factors that may predict high stable or increasing trajectories and could represent future treatment targets.

More Time in Glucose Range During Exercise Days than Sedentary Days in Adults Living with Type 1 Diabetes

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15-68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70-180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time <70 mg/dL higher (9.3% ± 11.0% vs. 7.1% ± 9.1%, P = 0.04) on exercise days compared with sedentary days. Glucose variability and % time <54 mg/dL did not differ significantly between exercise and sedentary days. No significant differences in glucose control by exercise type were observed. Conclusion: Participants had lower 24-h mean glucose levels and a greater time in range on exercise days compared with sedentary days, with mode of exercise affecting glycemia similarly. In summary, this study offers data supporting frequency of exercise as a method of facilitating glucose control but does not suggest an effect for mode of exercise.

Feasibility of Implementing a Pediatric Diabetes Clinic via Telehealth

OBJECTIVE

In response to the coronavirus disease 2019 (COVID-19) pandemic and social distancing guidelines, our pediatric diabetes team rapidly changed the format of conducting diabetes clinic from in person to telehealth. We compared the actual number and rate of completed, canceled, and no-show visits between an 8-week period in 2019, when we exclusively conducted visits in person and the same 8-week period in 2020, during the COVID-19 quarantine, when we exclusively conducted visits via telehealth.

METHODS

We used electronic health record data for all patients, as well as Dexcom continuous glucose monitoring data collected for a subset of youths during the COVID-19 quarantine and the immediate pre-COVID-19 period.

RESULTS

Although there was a difference in the absolute number of in-person versus telehealth visits canceled during these two time periods, there was no difference in the rates of completed, canceled, and no-show visits completed in person or via telehealth. This finding suggests that, despite a rapid shift to a completely new health care delivery model, our providers completed a similar rate of patient care via telehealth during the COVID-19 quarantine and that telehealth may be a feasible method for providing diabetes care. However, our results also suggested that youths' glucose management was less optimal during the quarantine period.

CONCLUSION

COVID-19 presented an opportunity to adopt and test the feasibility of using a telehealth delivery model for routine diabetes care. Yet, to make telehealth a viable treatment delivery alternative will likely involve the uptake of new clinic procedures, investment in institutional infrastructure, and team-based flexibility.

Parent Perspectives on Educational and Psychosocial Intervention for Recent-Onset Type 1 Diabetes in Their School-Age Child: A Qualitative Study

OBJECTIVE

The recent-onset period of type 1 diabetes (T1D) in early school-age children should include comprehensive, parent-focused T1D education as well as family-centered resources and support to help with adjustment. Here, we present parent/caregiver perspectives on specific areas of concern during the recent-onset period of T1D and their preferred timing for different topics related to T1D education.

METHODS

Parents/caregivers of 5- to 9-year-old children with T1D completed a card-sorting task and qualitative interview to describe ongoing areas of concern and preferred educational topics during the first year after T1D diagnosis.

RESULTS

Thirteen parents/caregivers (aged 35.1 ± 6.9 years) of a child with T1D (aged 8.9 ± 0.8 years, 11.3 ± 7.0 months post-diagnosis) completed the card-sorting task, and 11 completed the qualitative interview. Parents/caregivers endorsed four preferred stages of education: basic education and T1D survival skills during month 1 post-diagnosis, application and practice of T1D skills from months 1-3, access to community supports to cope with anxiety and distress from months 3-6, and support to build autonomy and manage burnout beyond month 6 post-diagnosis. Parents/caregivers endorsed four main themes for ongoing concerns: anxiety, autonomy, distress, and support.

CONCLUSION

Parents endorsed four time points for education and psychosocial services within the first year of a T1D diagnosis. Parents/caregivers may benefit the most from psychosocial interventions 3-6 months post-diagnosis, once they have had sufficient time to develop basic T1D management skills. These findings support the need for regular parent psychosocial screening and access to scalable psychosocial interventions in the first year post-diagnosis of T1D.

The Association Between Glycemic Variability and Macronutrients in Young Children with T1D

OBJECTIVE

There is limited information regarding the potential effect macronutrients have on postprandial glycemic variability in young children with type 1 diabetes (T1D). To date, studies examining nutrition and glycemic outcomes either assess these factors at a single timepoint, or aggregate large datasets for group level analyses. This study examined how inter- and intraindividual fluctuations in carbohydrate, fat, and protein intake impact glycemic variability in the postprandial period for young children with T1D.

METHODS

Thirty-nine young children, aged 2-6 years, wore a continuous glucose monitor for 72 hr, while their parents completed detailed diet records of all food intake. The analyses tested three multilevel models to examine intra- and interindividual differences between food intake and postprandial glycemic variability.

RESULTS

The results suggest carbohydrate intake, relates to greater postprandial glycemic variability. In contrast, the results reveal the inverse effect for protein, suggesting a tendency for young children who ate more protein at some meals to have lower postprandial glycemic variability, with the exception of lunch. There was no effect for fat on postprandial glycemic variability.

CONCLUSION

These results suggest protein consumption may be an important consideration when aiming for optimal glycemic levels for some meals. When counseling parents of young children with T1D on common behaviors underlying glycemic excursion, pediatric psychologists may consider discussing the nutritional make up of children's meals. Further, the results demonstrate retaining longitudinal data at the person level, versus aggregating individual data for group level analyses, may offer new information regarding macronutrient intake and glycemic outcomes.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App

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.

Family mealtime behaviors in children who are tube fed and preparing to transition to oral eating: A comparison to other pediatric populations

This study examined differences in observed mealtime behaviors between children preparing to transition to oral feeding and children with various other chronic illnesses using a standardized measure of mealtime beaviors. The parent-child mealtime relationship can become strained due to problematic mealtime behaviors that limit food intake, as well as inadvertent reinforcement of disruptive behavior by caregivers. Frequency/rate of behaviors were compared between children with tube feeding (CwTF) and from previous studies of children with chronic illnesses using the Dyadic Interactive Nomenclature for Eating (DINE). Parents of CwTF used more coaxing, physical prompts, and reinforcement during meals, while parents of children with chronic illnesses used more direct commands and engaged in more parent talk. Findings support differences in parent-child mealtime interactions and eating behaviors across pediatric illness subgroups.

Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP): Protocol for a Randomized Clinical Trial to Test a Video-Based Telehealth Intervention

Background

Despite the introduction of new insulin analogs, insulin pumps, and continuous glucose monitoring (CGM), young children with type 1 diabetes mellitus (T1D) remain vulnerable to episodes of hypoglycemia because of their unpredictable eating and activity patterns and high degree of insulin sensitivity. Caregivers and young children living with T1D learn to fear hypoglycemia because it is uncomfortable, unpredictable, and dangerous. Up to 60% of caregivers of young children with T1D report moderate to severe levels of fear of hypoglycemia, and caregiver fear of hypoglycemia relates to lower quality of life for families and suboptimal child glycemic control. Yet, until recently, there have been no studies reporting on a targeted intervention to treat caregiver fear of hypoglycemia in families of young children.

Objective

The aim of this project is to conduct a randomized clinical trial of an innovative, video-based telehealth intervention to treat fear of hypoglycemia in caregivers of young children with T1D versus a relevant, age-appropriate attention control intervention.

Methods

We created the Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP) intervention by merging age-appropriate T1D education and behavioral parenting strategies with cognitive behavioral therapy strategies that are effective for reducing fear and promoting adaptive coping. REDCHiP uses 10 video-based telehealth sessions that are a combination of group and individual sessions. We will recruit up to 180 families of young children with T1D to participate in this clinical trial from two pediatric diabetes clinics located in the midwestern and southern United States. Once families have been enrolled, we will randomize caregivers based on child age (age 2-3 years or 4-5 years), child sex, and family CGM use to participate in the REDCHiP or attention control intervention. Families will complete 3 assessment visits that coincide with study entry, end of treatment, and 3-month posttreatment. At each assessment visit, we will collect questionnaire data from caregivers, accelerometry data from caregivers and children, CGM data from children, and a blood sample to measure glycated hemoglobin levels from children.

Results

Recruitment began in July 2019, and enrollment is ongoing. The first wave of intervention delivery began in December 2019. We anticipate completing enrollment in 2023. Final reporting of results will occur within 12 months of the primary completion date.

Conclusions

If the REDCHiP intervention is efficacious, next steps will be to examine multiple implementation strategies to determine how best to disseminate the intervention to pediatric diabetes clinics around the world.

Trial Registration

ClinicalTrials.gov NCT03914547; https://clinicaltrials.gov/ct2/show/NCT03914547

International Registered Report Identifier (IRRID)

PRR1-10.2196/17877

Associations between autism symptom severity and mealtime behaviors in young children presented with an unfamiliar food

BACKGROUND

Feeding problems are common in children with Autism Spectrum Disorder (ASD), and there are associations between parent reports of child ASD symptom severity and feeding problems. The current study further explores this association between ASD severity and family mealtime behaviors using directly observed naturalistic mealtime interactions.

METHODS AND PROCEDURES

Seventy-three children (M_age = 5.42 years) were presented an unfamiliar food during a videotaped but otherwise typical home meal. Mealtime behavior was assessed through coding of the videotaped meal using the Dyadic Interaction Nomenclature for Eating (DINE) and parent report (Brief ASD Mealtime Behavior Inventory; BAMBI). ASD severity was assessed with the clinician-completed Childhood Autism Rating Scale-Second Edition (CARS-2).

OUTCOMES AND RESULTS

Greater ASD severity was associated with fewer bites of the unfamiliar food, greater disruptive behavior during meals, and greater parental commands to take bites during meals. We found negative associations between limited food variety and food refusal (BAMBI subscales) and child bites of the unfamiliar food, with higher levels of limited food variety and food refusal associated with fewer bites of the unfamiliar food.

CONCLUSIONS AND IMPLICATIONS

Children with more severe ASD may eat less and be more disruptive during meals, despite parent redirection. We also found associations between the BAMBI and DINE which suggest the BAMBI may be a sensitive measure of mealtime behaviors such as food flexibility and food refusal.

Depression as a predictor of hypoglycemia worry in parents of youth with recent-onset type 1 diabetes

BACKGROUND

During the recent-onset period of type 1 diabetes (T1D), parents may be at increased risk for depression, stress, and hypoglycemia fear; however, current studies have not examined the parental psychological experience and anxiety from hypoglycemia fear (ie, hypoglycemia worry) over time. This study examined the trajectory of parental hypoglycemia worry (Hypoglycemia Fear Survey-Worry [HFS-Worry]) in families of children with recent-onset T1D and the effects of baseline parental depression on parents' trajectory of HFS-Worry.

METHODS

We enrolled 128 families of children ages 5- to 9-years-old with recent onset T1D in this study. At baseline, 125 parents completed measures of depression and HFS-Worry, followed by 111 at 6-month follow-up, 113 at 12-month follow-up, and 107 at 18-month follow-up. We used multilevel modeling to examine the 18-month trajectories of HFS-Worry and to examine if parental depression modified these trajectories.

RESULTS

We found that parents HFS-Worry scores increased over time for parents with and without elevated depressive symptoms. Parents' baseline report of depression appeared to modify their trajectory of HFS-Worry over time such that parents with elevated depressive symptoms reported significantly higher levels of worry when compared to parents without depressive symptoms across the 18-month study period (P < .05).

CONCLUSIONS

Parents of children with recent-onset T1D, who reported elevated depressive symptoms, reported higher HFS-Worry across the study period. Our findings suggest clinics should consider screening for parent depression and hypoglycemia worry following a T1D diagnosis. Integrating psychological screening for parents could help clinics to provide relevant treatment resources and tailor diabetes education for parents. Trial Registration NCT03698708.

Mealtime insulin BOLUS score increases prior to clinic visits in youth with type 1 diabetes

INTRODUCTION

Multiple studies confirm the occurrence of 'white coat adherence' (WCA), a term describing an increase in engagement with self-care tasks just prior to a scheduled clinic appointment, across cohorts with multiple chronic conditions. In youth with type 1 diabetes (T1D), research also shows an increase in self-monitoring blood glucose frequency ahead of youths' clinic visits. While studies show preliminary evidence for the occurrence of WCA in youth with T1D, no study has examined the effect of WCA and mealtime insulin dosing behaviors in youth with T1D. The frequency of mealtime insulin bolusing score (BOLUS) is an objective measure of mealtime insulin use in youth with T1D that could be vulnerable to WCA. To fill this gap in the literature and further our understanding of WCA in pediatric diabetes, we determined whether WCA also impacts BOLUS scores in youth with T1D.

RESEARCH DESIGN AND METHODS

We extracted insulin pump records and HbA1c levels from a clinical database for 459 youth with T1D (M_age=12.5±2.9 years). We calculated mean BOLUS scores for 6-5, 4-3, and 2-0 weeks prior to youths' routine clinic visits. We used multilevel modeling to examine patterns of BOLUS scores prior to clinic visits and tested for age differences.

RESULTS

Multilevel modeling showed a significant increase in BOLUS scores in the weeks prior to youths' clinic appointments (β=0.07, p<0.001). On average, adolescents had lower BOLUS scores than school-age children (β=-0.35, p<0.001). Post hoc analyses showed that adolescents consistently had lower BOLUS scores than children across assessments (p's<0.001).

CONCLUSIONS

Youth with T1D increase their mealtime insulin use prior to clinic appointments. The BOLUS may be a viable target for intervention to drive improved glycemic control. Whether increased tendency to WCA is associated with reduced risk of diabetic complications remains to be determined.

Associations Between Objective Sleep Behaviors and Blood Glucose Variability in Young Children With Type 1 Diabetes

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.

Stressful life events, parental psychosocial factors, and glycemic management in school-aged children during the 1 year follow-up of new-onset type 1 diabetes

OBJECTIVE

To monitor occurrence of stressful life events, assess correlations with family functioning and parental psychosocial measures, and examine the impact of stressful life events on diabetes management in the first year after diagnosis of type 1 diabetes (T1D) in children using a mixed methods design.

METHODS

In a prospective study of 5- to 9-year-olds with recent-onset T1D (mean age 7.4 ± 1.3 years, T1D duration 4.7 ± 3.3 months), we monitored glycated hemoglobin A1c (HbA1c), income, job status, family health, and marital status at baseline and every 3 months up to 1 year. We measured coping, parental depression, and diabetes family conflict at baseline.

RESULTS

Of 128 families, 53.9% (n = 69) reported 1+ stressful event, with 25.8% reporting income change (n = 33) during this period, 23.4% additional family health changes (n = 30), 22.7% job changes (n = 29), 21.9% changes in child's school (n = 28), and 3.9% changes in marital status (n = 5). Baseline active avoidance coping, parental depression, and diabetes family conflict correlated with a higher number of stressful life events (r = 0.239, P < .01; r = 0.197, P < .05; r = 0.225, P < .01, respectively). There were also cross-sectional associations between HbA1c and income decrease, school change, and job change at various time points in the study.

CONCLUSIONS

Families can experience concurrent life stressors during the first year of T1D, which relate to coping, depression, and conflict. Consistent with existing literature, stressful life events relate to glycemic management. Future research should explore the individual's or parent's perception of stress and ways that diabetes centers can effectively assist families of youth with T1D and concurrent life stressors.

Adaptive Mobile Health Intervention for Adolescents with Asthma: Iterative User-Centered Development

BACKGROUND

Adolescents diagnosed with persistent asthma commonly take less than 50% of their prescribed inhaled corticosteroids (ICS), placing them at risk for asthma-related morbidity. Adolescents' difficulties with adherence occur in the context of normative developmental changes (eg, increased responsibility for disease management) and rely upon still developing self-regulation and problem-solving skills that are integral for asthma self-management. We developed an adaptive mobile health system, Responsive Asthma Care for Teens (ReACT), that facilitates self-regulation and problem-solving skills during times when adolescents' objectively measured ICS adherence data indicate suboptimal rates of medication use.

OBJECTIVE

The current paper describes our user-centered and evidence-based design process in developing ReACT. We explain how we leveraged a combination of individual interviews, national crowdsourced feedback, and an advisory board comprised of target users to develop the intervention content.

METHODS

We developed ReACT over a 15-month period using one-on-one interviews with target ReACT users (n=20), national crowdsourcing (n=257), and an advisory board (n=4) to refine content. Participants included 13-17-year-olds with asthma and their caregivers. A total of 280 adolescents and their caregivers participated in at least one stage of ReACT development.

RESULTS

Consistent with self-regulation theory, adolescents identified a variety of salient intrapersonal (eg, forgetfulness, mood) and external (eg, changes in routine) barriers to ICS use during individual interviews. Adolescents viewed the majority of ReACT intervention content (514/555 messages, 93%) favorably during the crowdsourcing phase, and the advisory board helped to refine the content that did not receive favorable feedback during crowdsourcing. Additionally, the advisory board provided suggestions for improving additional components of ReACT (eg, videos, message flow).

CONCLUSIONS

ReACT involved stakeholders via qualitative approaches and crowdsourcing throughout the creation and refinement of intervention content. The feedback we received from participants largely supported ReACT's emphasis on providing adaptive and personalized intervention content to facilitate self-regulation and problem-solving skills, and the research team successfully completed the recommended refinements to the intervention content during the iterative development process.