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Objective: This paper reports on the initial outcomes of a new mHealth intervention to reduce diabetes distress (DD) in families of school-age children living with type 1 diabetes (T1D) entitled, 'Remedy to Diabetes Distress' (R2D2). Methods: We randomized 34 families (mean child age = 10 ± 1.4 years; 53% male, 85% White, mean HbA1c = 7.24 ± 0.71%) to one of three delivery arms differing only by number of telehealth visits over a 10-week period: zero visits = self-guided (SG), three visits = enhanced self-guided (ESG), or eight visits = video visits (VV). All families had 24 × 7 access to digital treatment materials for 10 weeks. We examined the feasibility and acceptability of R2D2. We used the Problem Areas in Diabetes-Child (PPAIDC and PAIDC, parent and child, respectively) to examine treatment effects by time and delivery arm. We performed sensitivity analyses to characterize families who responded to R2D2. Results: It was feasible for families to access R2D2 mHealth content independently, though attendance at telehealth visits was variable. Parents and children reported high satisfaction scores. There were significant pre-post reductions in PPAIDC (p = 0.026) and PAIDC (p = 0.026) scores but no differences by delivery arm. There were no differences in child age, sex, race, or pre-treatment HbA1c for responders versus non-responders, though families who responded reported higher PPAID-C scores pre-treatment (p = 0.01) and tended to report shorter diabetes duration (p = 0.08). Conclusions: Initial results support the acceptability and treatment effects of R2D2 regardless of the frequency of adjunctive virtual visits. Characterizing responders may help to identify families who could benefit from R2D2 in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A correction to the article "Feasibility of Implementing a Pediatric Diabetes Clinic via Telehealth" in a 2021 issue is presented.

Background: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D., Objective: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study., Methods: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available)., Results: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min)., Conclusions: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D., (© Susana R Patton, Robin L Gal, Simon Bergford, Peter Calhoun, Mark A Clements, Jennifer L Sherr, Michael C Riddell. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org).)

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Z.L. reports no conflict of interests. M.R.R. reports consultancy fees from Zealand Pharma. R.L.G. reports no conflict of interests. P.C. reports no conflict of interests. P.G.J. reports receiving grants from the National Institutes of Health, The Leona M. and Harry B. Charitable Trust, the Juvenile Diabetes Research Foundation, Dexcom, and the Oregon Health & Science University Foundation; consultancy fees from CDISC; US patents 62/352,939, 63/269,094, 62/944,287, 8810388, 9,480,418, 8,317,700, 61/570382, 8,810,388, 7,976,466, and 6,558,321; and reports stock options from Pacific Diabetes Technologies, outside submitted work. M.A.C. is Chief Medical Officer of Glooko, Inc and has received grants or contracts from Dexcom, Abbott Diabetes Care, National Institutes of Health, the Juvenile Diabetes Research Foundation, the Emily Rosebud Foundation, Eli Lilly, Tolerion, and Garmin. F.J.D. reports no conflict of interests. S.R.P. reports receiving grants from The Leona M. and Harry B. Helmsley Charitable Trust, the National Institutes of Health, and the Jaeb Center for Health Research and honorarium from the American Diabetes Association, outside the submitted work. J.R.C. reports receiving grants from the Juvenile Diabetes Research Foundation, the National Institutes of Health, Dexcom, and Medtronic and consultancy fees from Novo Nordisk, Insulet, and Zealand, outside the submitted work. M.B.G. reports no conflict of interest. R.W.B. reports receiving consulting fees, paid to his institution, from Insulet, Bigfoot Biomedical, vTv Therapeutics, and Eli Lilly, grant support and supplies, provided to his institution, from Tandem and Dexcom, and supplies from Ascenia and Roche. C.K.M. reports no conflict of interests. M.C.R. reports receiving consulting fees from the Jaeb Center for Health Research, Eli Lilly, Zealand Pharma, and Zucara Therapuetics; speaker fees from Sanofi Diabetes, Eli Lilly, Dexcom Canada, and Novo Nordisk; and stock options from Supersapiens and Zucara Therapeutics.

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., Competing Interests: Competing interests: MAC receives consulting fees from Glooko and has research support from Dexcom and Abbott Diabetes Care that is unrelated to this project. LAF receives material research support from Dexcom which is not related to this project., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: To meet their glycated hemoglobin (HbA 1c ) 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 HbA 1c . Here, to further assess the BOLUS score's validity, we compared the strengths of the associations between youth's HbA 1c 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 HbA 1c 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 HbA 1c 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, HbA 1c , 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 HbA 1c than the SCI., Conclusions: Youths' BOLUS score has better concurrent validity with HbA 1c than the SCI. We should consider reporting the BOLUS score as an outcome metric in insulin pump data reports., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Mark A. Clements is Chief Medical Officer of Glooko, Inc and has received grants or contracts from Dexcom, Abbott Diabetes Care, National Institutes of Health, JDRF, the Emily Rosebud Foundation, Eli Lilly, Tolerion, and Garmin. Dr Susana R. Patton has received grants from the National Institutes of Health.

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.