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Wrist-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. The 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. We 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/m ; 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. Fitbit 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<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

IntroductionDiabetes 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 methodsParents 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.ResultsChildren 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.ConclusionsOur analyses suggest parent DD is a strong predictor of child HbA1c and is another modifiable treatment target for lowering child HbA1c.

Type 1 diabetes (T1D) management requires following a complex and constant regimen relying on child or caregiver behaviors, skills, and knowledge. Psychological factors such as diabetes distress (DD), depression, and burnout are pertinent considerations in the treatment of pediatric T1D. Approximately 40% of youth and 61% of caregivers experience DD. Implementation of DD screening as part of clinical best practice is recommended and may facilitate treatment referral, perhaps leading to improved health or well-being for youth with T1D and their caregivers. By building on existing institutional infrastructure when available, screening via digital health platforms (applications, or \"apps\") may allow for timely screening of, and response to, DD. This work details the creation, implementation, and refinement of a process to screen for DD in youth and their caregivers in the context of routine T1D care using a digital health platform. DD screening was implemented in an outpatient endocrinology clinic over 1 year as part of a larger screen-to-treat trial for children aged 8-12.99 years and their caregivers. Validated measures were sent via digital health platform to be completed prior to the clinic visit. Results were initially reviewed manually, but a digital best practice alert (BPA) was later built to notify staff of elevated scores. Families experiencing DD received resources sent via the digital health platform. For this secondary analysis, child demographics and glycated hemoglobin A1c (HbA1c) were collected. During the screening period, absolute completion rates were 36.78% and 38.83%, with adjusted screening rates at 52.02% and 54.48%, for children and caregivers, respectively. A total of 21 children (mean HbA1c 8.04%, SD 1.39%) and 26 caregivers (child mean HbA1c 8.04%, SD 1.72%) reported elevated DD. Prior to BPA development, resources were sent to all but 1 family. After BPA implementation, all families were sent resources. Early findings indicate that DD education, screening, and response can be integrated via digital platforms in a freestanding outpatient endocrinology clinic, thereby facilitating timely treatment referral and provision of resources for those identified with distress. Notably, in the observed 1-year screening period, screening rates were low, and barriers to implementation were identified. While some implementation challenges were iteratively addressed, there is a need for future quality improvement initiatives to improve screening rates and the identification of, or response to, DD in our pediatric patients and their families.

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.

IntroductionIn a cohort of families of school-age children (8–12.99 years old) with type 1 diabetes, we examined the stability of parent and child diabetes-related distress (DRD) over 6 months and the associations between parent and child DRD and child glycated hemoglobin (HbA1c) over time.Research design and methodsWe recruited families from two large pediatric hospital systems in the USA and used validated measures of parent (Parent Problem Areas in Diabetes-Child, PPAID-C) and child (Problem Areas in Diabetes-Child, PAID-C) DRD and children’s HbA1c. We collected data at baseline and 6 months. We calculated minimal clinically important differences in PPAID-C and PAID-C to examine DRD stability and used a linear regression model to examine associations between PPAID-C and PAID-C scores and child HbA1c over time.ResultsWe recruited n=132 parent–child dyads (mean child age=10.23±1.5 years; 50% male, 86% non-Hispanic white). 60% of children and 55% of parents reported stable DRD levels, 20% of children and 14% of parents reported increasing DRD levels, and 20% of children and 31% of parents reported decreasing DRD levels from baseline to 6 months. In the regression model, child HbA1c and DRD scores at baseline significantly predicted child HbA1c 6 months later, β=0.013, t(157)=2.32, p=0.02.ConclusionsAcross 6 months, DRD remained stable or increased in 80% of school-aged children and 69% of parents. Only child HbA1c and DRD at baseline predicted higher child HbA1c 6 months later. Our results suggest it may be valuable to screen families of school-age children for DRD routinely and to develop treatments to help them reduce DRD.

IntroductionWe 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 methodsParents 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.ResultsParents’ 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).ConclusionsIncreasing 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.

Although 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 the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D). We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D. We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory 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 nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15. DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively. The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially 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.

Background:Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions designed to address rising glycemic levels is limited. Although electronic health records (EHR)–based risk predictions have been used to forecast health outcomes in T1D, no study has investigated the potential for using EHR data to identify youth with T1D who will experience a clinically significant rise in glycated hemoglobin (HbA1c) ≥0.3% (approximately 3 mmol/mol) between diabetes clinic visits.Objective:We aimed to evaluate the feasibility of using routinely collected EHR data to develop a machine learning model to predict 90-day unit-change in HbA1c (in % units) in youth (aged 9‐18 y) with T1D. We assessed our model’s ability to augment clinical decision-making by identifying a percent change cut point that optimized identification of youth who would experience a clinically significant rise in HbA1c.Methods:From a cohort of 2757 youth with T1D who received care from a network of pediatric diabetes clinics in the Midwestern United States (January 2012-August 2017), we identified 1743 youth with 9643 HbA1c observation windows (ie, 2 HbA1c measurements separated by 70‐110 d, approximating the 90-day time interval between routine diabetes clinic visits). We used up to 5 years of youths’ longitudinal EHR data to transform 17,466 features (demographics, laboratory results, vital signs, anthropometric measures, medications, diagnosis codes, procedure codes, and free-text data) for model training. We performed 3-fold cross-validation to train random forest regression models to predict 90-day unit-change in HbA1c(%).Results:Across all 3 folds of our cross-validation model, the average root-mean-square error was 0.88 (95% CI 0.85‐0.90). Predicted HbA1c(%) strongly correlated with true HbA1c(%) (r=0.79; 95% CI 0.78‐0.80). The top 10 features impacting model predictions included postal code, various metrics related to HbA1c, and the frequency of a diagnosis code indicating difficulty with treatment engagement. At a clinically significant percent rise threshold of ≥0.3% (approximately 3 mmol/mol), our model’s positive predictive value was 60.3%, indicating a 1.5-fold enrichment (relative to the observed frequency that youth experienced this outcome [3928/9643, 40.7%]). Model sensitivity and positive predictive value improved when thresholds for clinical significance included smaller changes in HbA1c, whereas specificity and negative predictive value improved when thresholds required larger changes in HbA1c.Conclusions:Routinely collected EHR data can be used to create an ML model for predicting unit-change in HbA1c between diabetes clinic visits among youth with T1D. Future work will focus on optimizing model performance and validating the model in additional cohorts and in other diabetes clinics.

Objectives: Severe hypoglycemia is more common among young children with type 1 diabetes mellitus (T1DM) than older youth, and parents report significant hypoglycemia fear (HF). Parents experiencing HF describe constant and extreme worry about the occurrence of hypoglycemia and may engage in potentially risky behaviors to avoid hypoglycemia. Our team developed and tested a behavioral intervention, Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP), to decrease HF in parents of young children with T1DM. Here, we qualitatively analyzed parent feedback to refine and optimize future intervention iterations. Methods: The randomized pilot study included parents (n = 73) of young children with T1DM who participated in the 10-session video-based intervention. We qualitatively analyzed 21 recordings of the final intervention session, where parents provided feedback about intervention content. Trained coders independently reviewed each session. The frequency of parent quotes regarding active REDCHiP treatment components were calculated. Results: The coded themes reflected active treatment components [viz., Use of Cognitive Behavioral Therapy (CBT) Skills, Coping, Behavioral Parenting Strategies]. Also, two secondary process codes were identified: Appreciate REDCHiP Content and Challenges in Applying REDCHiP Strategies. Parents provided examples of skills or concepts they applied from REDCHiP, the challenges they encountered, and if they planned to apply these skills in the future. Conclusions: A qualitative analysis provided insight into parent perceptions of the active treatment components within the REDCHiP intervention, their acceptability, and parents’ intention to apply REDCHiP skills/concepts within daily T1DM cares. Future iterations of the intervention that trial alternative formats (i.e., individual vs. group and asynchronous vs. telehealth) may increase accessibility and scalability.

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. 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. 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). 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). 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.