Community Partnership to Co-Develop an Intervention to Promote Equitable Uptake of the COVID-19 Vaccine Among Pediatric Populations.

Objective: To describe the process of engaging community, caregiver, and youth partners in codeveloping an intervention to promote equitable uptake of the COVID-19 vaccine in non-Hispanic Black (Black) and Hispanic youth who experience higher rates of COVID-19 transmission, morbidity, and mortality but were less likely to receive the COVID-19 vaccine. Methods: A team of 11 Black and Hispanic community partners was assembled to codevelop intervention strategies with our interdisciplinary research team. We used a mixed-methods crowdsourcing approach with Black and Hispanic youth (n=15) and caregivers of Black and Hispanic youth (n=20) who had not yet been vaccinated against COVID-19, recruited from primary care clinics, to elicit perspectives on the acceptability of these intervention strategies. Results: We codeveloped five strategies: (1) community-tailored handouts and posters, (2) videos featuring local youth, (3) family-centered language to offer vaccines in the primary care clinic, (4) communication-skills training for primary care providers, and (5) use of community health workers to counsel families about the vaccine. The majority (56-96.9%) of youth and caregivers rated each of these strategies as acceptable, especially because they addressed common concerns and facilitated shared decision-making. Conclusions: Engaging community and family partners led to the co-development of culturally- and locally-tailored strategies to promote dialogue and shared decision-making about the COVID-19 vaccine. This process can be used to codevelop interventions to address other forms of public health disparities. Policy Implications: Intervention strategies that promote dialogues with trusted healthcare providers and support shared decision-making are acceptable strategies to promote COVID-19 vaccine uptake among youth from historically underserved communities. Stakeholder-engaged methods may also help in the development of interventions to address other forms of health disparities.

Patient-Reported Outcomes to Describe Global Health and Family Relationships in Pediatric Weight Management.

Background: Patient-reported outcomes (PROs) can assess chronic health. The study aims were to pilot a survey through the PEDSnet Healthy Weight Network (HWN), collecting PROs in tertiary care pediatric weight management programs (PWMP) in the United States, and demonstrate that a 50% enrollment rate was feasible; describe PROs in this population; and explore the relationship between child/family characteristics and PROs. Methods: Participants included 12- to 18-year-old patients and parents of 5- to 18-year-olds receiving care at PWMP in eight HWN sites. Patient-Reported Outcomes Measurement Information System (PROMIS®) measures assessed global health (GH), fatigue, stress, and family relationships (FR). T-score cut points defined poor GH or FR or severe fatigue or stress. Generalized estimating equations explored relationships between patient/family characteristics and PROMIS measures. Results: Overall, 63% of eligible parents and 52% of eligible children enrolled. Seven sites achieved the goal enrollment for parents and four for children. Participants included 1447 children. By self-report, 44.6% reported poor GH, 8.6% poor FR, 9.3% severe fatigue, and 7.6% severe stress. Multiple-parent household was associated with lower odds of poor GH by parent proxy report [adjusted odds ratio (aOR) 0.69, 95% confidence interval (CI) 0.55-0.88] and poor FR by self-report (aOR 0.36, 95% CI 0.17-0.74). Parents were significantly more likely to report that the child had poor GH and poor FR when a child had multiple households. Conclusions: PROs were feasibly assessed across the HWN, although implementation varied by site. Nearly half of the children seeking care in PWMP reported poor GH, and family context may play a role. Future work may build on this pilot to show how PROs can inform clinical care in PWMP.

The Psychosocial Impact of the COVID-19 Pandemic on Families of Youth of Color: A Prospective Cohort Study.

OBJECTIVE: Prospectively examine racial and ethnic disparities in exposure to COVID-19-related stressors and their impact on families. METHODS: A racially, ethnically, and socioeconomically diverse cohort of caregivers of youth (n = 1,581) representative of the population served by a pediatric healthcare system completed the COVID-19 Exposure and Family Impact Scales in Oct/Nov 2020 and March/April 2021. Linear mixed-effects models were used to examine exposure to COVID-19-related events (Exposure), impact of the pandemic on family functioning and well-being (Impact), and child and parent distress (Distress) across time and as a function of race and ethnicity, adjusting for other sociodemographic variables. RESULTS: Exposure and Distress increased over time for all participants. After adjusting for sociodemographic factors, caregivers of Black and Hispanic youth reported greater Exposure than caregivers of White youth and caregivers of Black youth had a greater increase in Exposure over time than caregivers of White youth. Caregivers of White youth reported greater Impact than caregivers of Black and Other race youth. CONCLUSIONS: Exposure to and impact of the COVID-19 pandemic on family psychosocial functioning varied by race and ethnicity. Although exposure to COVID-19-related events was greater among Hispanic and non-Hispanic Black families, those of marginalized races reported less family impact than non-Hispanic White families, suggesting resiliency to the pandemic. Research should examine such responses to public health crises in communities of color, with a focus on understanding protective factors. These findings suggest the importance of culturally tailored interventions and policies that support universal psychosocial screenings during times of public health crises.

Associations of longitudinal BMI percentile classification patterns in early childhood with neighborhood-level social determinants of health.

Background: Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable. Objectives: This study aimed to identify distinct subpopulations based on BMI percentile classification or changes in BMI percentile classifications over time and explore these longitudinal associations with neighborhood-level SDOH factors in children from 0 to 7 years of age. Methods: Using Latent Class Growth (Mixture) Modelling (LCGMM) we identify distinct BMI% classification groups in children from 0 to 7 years of age. We used multinomial logistic regression to study associations between SDOH factors with each BMI% classification group. Results: From the study cohort of 36,910 children, five distinct BMI% classification groups emerged: always having obesity (n=429; 1.16%), overweight most of the time (n=15,006; 40.65%), increasing BMI% (n=9,060; 24.54%), decreasing BMI% (n=5,058; 13.70%), and always normal weight (n=7,357; 19.89%). Compared to children in the decreasing BMI% and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher rates of poverty, unemployment, crowded households, and single-parent households, and lower rates of preschool enrollment. Conclusions: Neighborhood-level SDOH factors have significant associations with children's BMI% classification and changes in classification over time. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of the children living within them.

Persistent Disparities in Pediatric Health Care Engagement During the COVID-19 Pandemic.

OBJECTIVE: The COVID-19 pandemic has disrupted traditional health care, including pediatric health care. We described the impact of the pandemic on disparities in pediatric health care engagement. METHODS: Using a population-based cross-sectional time-series design, we compared monthly ambulatory care visit volume and completion rates (completed vs no-show and cancelled visits) among pediatric patients aged 0-21 years in 4 states in the mid-Atlantic United States during the first year of the COVID-19 pandemic (March 2020-February 2021) with the same period before the pandemic (March 2019-February 2020). We used unadjusted odds ratios, stratified by visit type (telehealth or in-person) and sociodemographic characteristics (child race and ethnicity, caregiver primary language, geocoded Child Opportunity Index, and rurality). RESULTS: We examined 1 556 548 scheduled ambulatory care visits for a diverse pediatric patient population. Visit volume and completion rates (mean, 70.1%) decreased during the first months of the pandemic but returned to prepandemic levels by June 2020. Disparities in in-person visit completion rates among non-Hispanic Black versus non-Hispanic White patients (64.9% vs 74.3%), patients from socioeconomically disadvantaged versus advantaged communities as measured by Child Opportunity Index (65.8% vs 76.4%), and patients in rural versus urban neighborhoods (66.0% vs 70.8%) were the same during the remainder of the first year of the pandemic as compared with the previous year. Concurrent with large increases in telehealth (0.5% prepandemic, 19.0% during the pandemic), telehealth completion rates increased. CONCLUSIONS: Disparities in pediatric visit completion rates that existed before the pandemic persisted during the pandemic. These findings underscore the need for culturally tailored practices to reduce disparities in pediatric health care engagement.

Active vs Traditional Methods of Recruiting Children for a Clinical Trial in Rural Primary Care Clinics: A Cluster-Randomized Clinical Trial.

Importance: To our knowledge, there are no published randomized clinical trials of recruitment strategies. Rigorously evaluated successful recruitment strategies for children are needed. Objective: To evaluate the feasibility of 2 recruitment methods for enrolling rural children through primary care clinics to assess whether either or both methods are sufficiently effective for enrolling participants into a clinical trial of a behavioral telehealth intervention for children with overweight or obesity. Design, Setting, and Participants: This cluster-randomized clinical trial of 2 recruitment methods was conducted at 4 primary care clinics in 4 separate states. Each clinic used both recruitment methods in random order. Clinic eligibility criteria included at least 40% pediatric patients with Medicaid coverage and at least 100 potential participants. Eligibility criteria for children included a rural home address, age 6 to 11 years, and body mass index at or above the 85th percentile. Recruitment began February 3, 2020, and randomization of participants occurred on August 17, 2020. Data were analyzed from October 3, 2021, to April 21, 2022. Interventions: Two recruitment methods were assessed: the active method, for which a list of potential participants seen within the past year at each clinic was generated through the electronic health record and consecutively approached by research staff based on visit date to the clinic, and the traditional method, for which recruitment included posters, flyers, social media, and press release. Clinics were randomized to the order in which the 2 methods were implemented in 4-week periods, followed by a 4-week catch-up period using the method found most effective in previous periods. Main Outcomes and Measures: For each recruitment method, the number and proportion of randomized children among those who were approached was calculated. Results: A total of 104 participants were randomized (58 girls [55.8%]; mean age, 9.3 [95% CI, 9.0-9.6] years). Using the active method, 535 child-parent dyads were approached and 99 (18.5% [95% CI, 15.3%-22.1%]) were randomized. Using the traditional method, 23 caregivers expressed interest, and 5 (21.7% [95% CI, 7.5%-43.7%]) were randomized. All sites reached full enrollment using the active method and no sites achieved full enrollment using the traditional method. Mean time to full enrollment was 26.3 (range, 21.0-31.0) days. Conclusions and Relevance: This study supports the use of the active approach with local primary care clinics to recruit children with overweight and obesity from rural communities into clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT04142034.

Conducting a pediatric randomized clinical trial during a pandemic: A shift to virtual procedures.

Background/Objective: Prior to the COVID-19 pandemic, our research group initiated a pediatric practice-based randomized trial for the treatment of childhood obesity in rural communities. Approximately 6 weeks into the originally planned 10-week enrollment period, the trial was forced to pause all study activity due to the COVID-19 pandemic. This pause necessitated a substantial revision in recruitment, enrollment, and other study methods in order to complete the trial using virtual procedures. This descriptive paper outlines methods used to recruit, enroll, and manage clinical trial participants with technology to obtain informed consent, obtain height and weight measurements by video, and maintain participant engagement throughout the duration of the trial. Methods: The study team reviewed the IRB records, protocol team meeting minutes and records, and surveyed the site teams to document the impact of the COVID-19 shift to virtual procedures on the study. The IRB approved study changes allowed for flexibility between clinical sites given variations in site resources, which was key to success of the implementation. Results: All study sites faced a variety of logistical challenges unique to their location yet successfully recruited the required number of patients for the trial. Ultimately, virtual procedures enhanced our ability to establish relationships with participants who were previously beyond our reach, but presented several challenges and required additional resources. Conclusion: Lessons learned from this study can assist other study groups in navigating challenges, especially when recruiting and implementing studies with rural and underserved populations or during challenging events like the pandemic.

Flexible-Window Predictions on Electronic Health Records.

Various types of machine learning techniques are available for analyzing electronic health records (EHRs). For predictive tasks, most existing methods either explicitly or implicitly divide these time-series datasets into predetermined observation and prediction windows. Patients have different lengths of medical history and the desired predictions (for purposes such as diagnosis or treatment) are required at different times in the future. In this paper, we propose a method that uses a sequence-to-sequence generator model to transfer an input sequence of EHR data to a sequence of user-defined target labels, providing the end-users with "flexible" observation and prediction windows to define. We use adversarial and semi-supervised approaches in our design, where the sequence-to-sequence model acts as a generator and a discriminator distinguishes between the actual (observed) and generated labels. We evaluate our models through an extensive series of experiments using two large EHR datasets from adult and pediatric populations. In an obesity predicting case study, we show that our model can achieve superior results in flexible-window prediction tasks, after being trained once and even with large missing rates on the input EHR data. Moreover, using a number of attention analysis experiments, we show that the proposed model can effectively learn more relevant features in different prediction tasks.

Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the US. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3-20 years using the data from 1-3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.

Medical factors associated with caregiver intention to vaccinate their children against COVID-19.

OBJECTIVE: To describe medical factors that are associated with caregiver intention to vaccinate their children against COVID-19. METHODS: We conducted a cross-sectional study of families receiving primary care in a mid-Atlantic pediatric healthcare system, linking caregiver-reported data from a survey completed March 19 to April 16, 2021 to comprehensive data from the child's EHR. RESULTS: 513 families were included (28% Black, 16% Hispanic, 44% public insurance, 21% rural, child age range 0-21 years). 44% of caregivers intended to vaccinate their children against COVID-19, while 41% were not sure and 15% would not. After adjusting for socio-demographics, the only medical factors that were associated with caregiver COVID-19 vaccine hesitancy were caregiver COVID-19 vaccination status at the time of the survey (aOR 3.0 if the caregiver did not receive the vaccine compared to those who did, 95% CI 1.7-5.3) and child seasonal influenza immunization history (aOR 3.3 if the child had not received the influenza vaccine in the 2020-2021 season compared to those who did, 95% CI 2.0-5.4). Other medical factors, including family medical experiences with COVID-19, other child immunization history, child health conditions like obesity and asthma, and family engagement with the healthcare system were not associated with caregiver intention to vaccinate their children against COVID-19. CONCLUSIONS: This study highlights important factors, such as general attitudes towards vaccines and understanding of COVID-19 morbidity risk factors, that healthcare providers should address when having conversations with families about the COVID-19 vaccine.

Validation of the COVID-19 Exposure and Family Impact Scales.

OBJECTIVE: The COVID-19 Exposure and Family Impact Scales (CEFIS) were developed in Spring 2020 to assess effects of the COVID-19 pandemic on families and caregivers. Initial psychometric properties were promising. The current study examined the factor structure and evaluated convergent and criterion validity of the CEFIS in a new sample. METHODS: In October and November 2020, caregivers (N = 2,531) of youth (0-21 years) scheduled for an ambulatory care visit at Nemours Children's Hospital, Delaware completed the CEFIS and measures of convergent (PROMIS Global Mental Health Scale, Family Assessment Device) and criterion validity (PTSD Checklist-Civilian). Confirmatory factor analysis was used to examine the factor structure of the CEFIS. Bivariate correlations and logistic regression were used to examine convergent and criterion validity. RESULTS: Factor analysis supported the original six- and three-factor structures for the Exposure and Impact scales, respectively. Second-order factor analyses supported the use of Exposure, Impact, and Distress total scores. Higher scores on the CEFIS Exposure, Impact, and Distress scales were associated with increased mental health concerns and poorer family functioning. Higher scores on all CEFIS scales were also associated with greater odds of having clinically significant posttraumatic stress symptoms. CONCLUSIONS: The CEFIS is a psychometrically sound measure of the impact of the COVID-19 pandemic on family and caregiver functioning and may also be useful in identifying families who would benefit from psychological supports.

Predicting Attrition Patterns from Pediatric Weight Management Programs.

Obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity who are not able to be successfully managed in the primary care setting. Despite their great potential, high dropout rates (referred to as attrition) are a major hurdle in delivering successful interventions. Predicting attrition patterns can help providers reduce the alarmingly high rates of attrition (up to 80%) by engaging in earlier and more personalized interventions. Previous work has mainly focused on finding static predictors of attrition on smaller datasets and has achieved limited success in effective prediction. In this study, we have collected a five-year comprehensive dataset of 4,550 children from diverse backgrounds receiving treatment at four pediatric weight management programs in the US. We then developed a machine learning pipeline to predict (a) the likelihood of attrition, and (b) the change in body-mass index (BMI) percentile of children, at different time points after joining the weight management program. Our pipeline is greatly customized for this problem using advanced machine learning techniques to process longitudinal data, smaller-size data, and interrelated prediction tasks. The proposed method showed strong prediction performance as measured by AUROC scores (average AUROC of 0.77 for predicting attrition, and 0.78 for predicting weight outcomes).

Concurrent Imputation and Prediction on EHR data using Bi-Directional GANs: Bi-GANs for EHR imputation and prediction.

Working with electronic health records (EHRs) is known to be challenging due to several reasons. These reasons include not having: 1) similar lengths (per visit), 2) the same number of observations (per patient), and 3) complete entries in the available records. These issues hinder the performance of the predictive models created using EHRs. In this paper, we approach these issues by presenting a model for the combined task of imputing and predicting values for the irregularly observed and varying length EHR data with missing entries. Our proposed model (dubbed as Bi-GAN) uses a bidirectional recurrent network in a generative adversarial setting. In this architecture, the generator is a bidirectional recurrent network that receives the EHR data and imputes the existing missing values. The discriminator attempts to discriminate between the actual and the imputed values generated by the generator. Using the input data in its entirety, Bi-GAN learns how to impute missing elements in-between (imputation) or outside of the input time steps (prediction). Our method has three advantages to the state-of-the-art methods in the field: (a) one single model performs both the imputation and prediction tasks; (b) the model can perform predictions using time-series of varying length with missing data; (c) it does not require to know the observation and prediction time window during training and can be used for the predictions with different observation and prediction window lengths, for short- and long-term predictions. We evaluate our model on two large EHR datasets to impute and predict body mass index (BMI) values and show its superior performance in both settings.

Integrating childhood obesity resources into the patient-centered medical home: Provider perspectives in the United States.

Pediatric primary care providers play a critical role in managing obesity yet often lack the resources and support systems to provide effective care to children with obesity. The objective of this study was to identify system-level barriers to managing obesity and resources desired to better managing obesity from the perspective of pediatric primary care providers. A 64-item survey was electronically administered to 159 primary care providers from 26 practices within a large pediatric primary care network. Bivariate analyses were performed to compare survey responses based on provider and practice characteristics. Also factor analysis was conducted to determine key constructs that effect pediatric interventions for obesity. Survey response rate was 69% ( n = 109), with the majority of respondents being female (77%), physicians (67%), and without prior training in obesity management (74%). Time constraints during well visits (86%) and lack of ancillary staff (82%) were the most frequently reported barriers to obesity management. Information on community resources (99%), an on-site dietitian (96%), and patient educational materials (94%) were most frequently identified as potentially helpful for management of obesity in the primary care setting. Providers who desired more ancillary staff were significantly more likely to practice in clinics with a higher percentage of obese, Medicaid, and Hispanic patients. Integrating ancillary lifestyle expert support into primary care practices and connecting primary care practices to community organizations may be a successful strategy for assisting primary care providers with managing childhood obesity, especially among vulnerable populations.

Electronic Gaming Characteristics Associated with Class 3 Severe Obesity in Youth Who Attend the Pediatric Weight Management Programs of the COMPASS Network.

BACKGROUND: The prevalence of severe obesity and electronic game use among youth has increased over time. METHODS: We administered a survey assessing gaming and psycho-demographic characteristics to youth aged 11-17 attending five weight management programs. We conducted chi-square and logistic regression analyses to describe the association between class 3 severe obesity and gaming characteristics. RESULTS: Four hundred twelve youth (51% female, 26% Black, 25% Hispanic, 43% White, and 44% with class 3 severe obesity) completed the survey. There was a stepwise relationship between time spent gaming and class 3 severe obesity, with 28% of those playing 2 to <4 hours a day, 48% of those playing 4 to <6 hours a day, and 56% of those playing ≥6 hours a day having class 3 severe obesity (p = 0.002). Compared to youth without class 3 severe obesity, youth with class 3 severe obesity were more likely to have a TV in the bedroom (76% vs. 63%, p = 0.004) and play games on a console (39% vs. 27%, p = 0.03) and were less likely to report parental limit setting on type of games played (7% vs. 16%, p = 0.006). Youth who played games ≥4 hours a day were 1.94 times (95% confidence interval 1.27-3.00) more likely to have class 3 severe obesity than those who played <4 hours a day, after adjustment for demographic, behavioral, and academic variables. CONCLUSIONS: Our study demonstrates a clear association between gaming characteristics, especially time spent gaming, and severe obesity in youth. Further research testing family-based interventions that target gaming behaviors in youth are needed.

Impact of Psychosocial Risk on Outcomes among Families Seeking Treatment for Obesity.

OBJECTIVES: To test the hypothesis that children with elevated psychosocial risk would have increased attrition and worse weight outcomes in weight management treatment. STUDY DESIGN: This was a prospective cohort study of 100 new patients, aged 4-12 years, in a weight management clinic. Parents completed the Psychosocial Assessment Tool. Logistic regression analyses were conducted to calculate the odds of attrition from the clinic and a nonmeaningful change in body mass index (BMI) z-score (ie, <0.1 unit decrease in BMI z-score) over a 6-month period based on psychosocial risk category, adjusting for child demographics and baseline weight category. RESULTS: The majority of patients were male (59%), black (36%) or white (43%), and had severe obesity (55%), and 59% of families were categorized as having moderate or high psychosocial risk. Over the 6-month period, 53% of families were lost to follow-up, and 67% did not have a clinically meaningful decrease in BMI z-score. Compared with children of families with low psychosocial risk, children of families with moderate or high psychosocial risk were 3.1 times (95% CI, 1.3-7.2 times) more likely to be lost to follow-up and 2.9 times (95% CI, 1.1-7.9 times) more likely to have a non-clinically meaningful change in BMI z-score. CONCLUSIONS: Children presenting with increased psychosocial risk have higher attrition and poorer weight outcomes, supporting the need for psychosocial screening as a standard component of pediatric weight management treatment.

Characteristics of Children 2 to 5 Years of Age With Severe Obesity.

BACKGROUND AND OBJECTIVES: As a distinct group, 2- to 5-year-olds with severe obesity (SO) have not been extensively described. As a part of the Expert Exchange Workgroup on Childhood Obesity, nationally-representative data were examined to better characterize children with SO. METHODS: Children ages 2 to 5 (N = 7028) from NHANES (1999-2014) were classified as having normal weight, overweight, obesity, or SO (BMI ≥120% of 95th percentile). Sociodemographics, birth characteristics, screen time, total energy, and Healthy Eating Index 2010 scores were evaluated. Multinomial logistic and linear regressions were conducted, with normal weight as the referent. RESULTS: The prevalence of SO was 2.1%. Children with SO had higher (unadjusted) odds of being a racial and/or ethnic minority (African American: odds ratio [OR]: 1.7; Hispanic: OR: 2.3). They were from households with lower educational attainment (OR: 2.4), that were single-parent headed (OR: 2.0), and that were in poverty (OR: 2.1). Having never been breastfed was associated with increased odds of obesity (OR: 1.5) and higher odds of SO (OR: 1.9). Odds of >4 hours of screen time were 1.5 and 2.0 for children with obesity and SO. Energy intake and Healthy Eating Index 2010 scores were not significantly different in children with SO. CONCLUSIONS: Children ages 2 to 5 with SO appear to be more likely to be of a racial and/or ethnic minority and have greater disparities in social determinants of health than their peers and are more than twice as likely to engage in double the recommended screen time limit.

Problem Video Gaming Among Children Enrolled in Tertiary Weight Management Programs.

Prior studies show seven percent to nine percent of children demonstrate gaming behaviors that affect a child's ability to function (e.g., problem gaming), but none have examined the association between problem gaming and weight status. The objective of this study was to determine the prevalence of problem gaming among children enrolled in tertiary weight management programs. We administered a computer-based survey to a convenience sample of children aged 11-17 years enrolled in five geographically diverse pediatric weight management (PWM) programs in the COMPASS (Childhood Obesity Multi-Program Analysis and Study System) network. The survey included demographics, gaming characteristics, and a problem gaming assessment. The survey had 454 respondents representing a diverse cohort (53 percent females, 27 percent black, 24 percent Hispanic, 41 percent white) with mean age of 13.7 years. A total of 8.2 percent of respondents met criteria for problem gaming. Problem gamers were more likely to be white, male, play mature-rated games, and report daily play. Children in PWM programs reported problem gaming at the same rate as other pediatric populations. Screening for problem gaming provides an opportunity for pediatricians to address gaming behaviors that may affect the health of children with obesity who already are at risk for worsened health and quality of life.

Psychological contributors to noncompletion of an adolescent preoperative bariatric surgery program.

BACKGROUND: Noncompletion of preoperative bariatric programs is a significant problem among adolescents. Adult studies suggest that psychological factors contribute to noncompletion of preoperative bariatric programs. OBJECTIVE: The aim of this study was to determine the association between adolescent psychological functioning and completion of the preoperative phase of a bariatric program. SETTING: The study was conducted at a tertiary care children's hospital affiliated with a university medical center. METHODS: Seventy-four adolescents and their parents completed an assessment measure of psychological functioning with the Behavior Assessment System for Children, Second Edition. We compared these scores between adolescents who completed the preoperative phase of the bariatric program and proceeded to surgery (completers) to those who did not (noncompleters) using multivariate analysis of covariance and logistic regression analyses, adjusting for demographic characteristics and baseline body mass index. RESULTS: The mean age was 16.0 (1.1) years, most were female (79.8%), and the group was diverse (48.6%, Caucasian; 33.8%, black; 17.6%, other, including Hispanic, Asian, and biracial). Average body mass index was 50.5 (7.6) kg/m2. Forty-two percent of participants were noncompleters. Noncompleters were reported by parents to have more clinically significant externalizing and internalizing behaviors and fewer adaptive behaviors. Noncompleters self-reported more clinically significant internalizing symptoms, emotional problems, and poor personal adjustment. CONCLUSION: Adolescents who did not complete the preoperative phase of a bariatric surgery program had more clinically significant psychological symptoms across multiple domains compared with those who successfully proceeded to bariatric surgery. Early identification and treatment of psychological symptoms may be important in helping adolescents successfully proceed to surgery.