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BackgroundFeeding 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 proceduresSeventy-three children (Mage = 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 resultsGreater 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 implicationsChildren 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.

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]

BackgroundDespite 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. ObjectiveThe 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. MethodsWe 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. ResultsRecruitment 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. ConclusionsIf 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 RegistrationClinicalTrials.gov NCT03914547; https://clinicaltrials.gov/ct2/show/NCT03914547 International Registered Report Identifier (IRRID)PRR1-10.2196/17877

BackgroundAdolescents 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. ObjectiveThe 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. MethodsWe 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. ResultsConsistent 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). ConclusionsReACT 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.

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]

BackgroundWrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. ObjectiveThe objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. MethodsWe assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. ResultsFitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P