Delaware INBRE

特拉华州INBRE

• 批准号: 10851166
• 负责人: MELINDA K DUNCAN
• 金额: $ 78.35万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-30 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10851166
• 关键词: Address; Affect; Age; Anxiety; Attention; Attention deficit hyperactivity disorder; Behavior; Behavioral; Birth; Cardiovascular Diseases; Child; Child Health; Childhood; Chronic; Clinical; Cognitive; Communities; Complex; Data; Data Pooling; Data Science; Data Scientist; Data Set; Databases; Delaware; Development; Developmental Delay Disorders; Diabetes Mellitus; Diagnosis; Diagnostic; Dietary intake; Dietitian; Discipline; Down Syndrome; Ensure; Environmental Risk Factor; Epidemiologist; Face; Family; Future; Goals; Government; Health; Health behavior; Health trends; Healthy Eating; Heart Diseases; Income; Individual; Institution; Insurance Coverage; Intellectual functioning disability; Intervention; LGALS3BP gene; Lead; Learning Disabilities; Life Expectancy; Life Style; Literature; Longitudinal trends; Low income; Machine Learning; Measures; Mental Depression; Mental Health; Minority; Moods; Motor; Neighborhoods; Neurodevelopmental Disability; Obesity; Outcome; Overweight; Parents; Pattern; Performance; Persons; Physical activity; Preventive; Problem behavior; Public Health; Quality of life; Recommendation; Recreation; Reporting; Resources; Risk; Risk Reduction; Sampling; School-Age Population; Schools; Science; Scientist; Seizures; Services; Severities; Sleep; Sleep disturbances; Social isolation; Speech Delay; Subgroup; Surveys; System; Universities; Well in self; Work; autism spectrum disorder; autistic children; cohort; community barrier; community-level factor; dietary; family support; health care availability; health equity; improved; insight; large scale data; negative affect; novel; open source; pandemic disease; peer; physical conditioning; physical inactivity; physical therapist; preference; sleep pattern; social; social health determinants; trend

Abstract Children with neurodevelopmental disabilities (NDD) face many individual (diagnosis-specific, demographic), parent/family-related, and community-level barriers that affect their ability to optimize their health behaviors (being physically active, eating healthy, sleeping appropriately) and developmental outcomes (motor/participation, social, behavioral, cognitive/academic performance). Children with NDD are at a greater risk for poor developmental outcomes, physical inactivity, and reduced school and community-based recreational participation compared to their neurotypical (NT) peers. These challenges faced by children with NDD lead to higher rates of overweight/obesity and social isolation and in turn affect their long-term physical health (cardiovascular disease, diabetes, shorter life expectancy), psychological health (i.e., anxiety, depression, behavioral problems), and future development. Identifying and addressing the various complex, multi-level factors affecting health behaviors and developmental outcomes of children with NDD will eventually improve their overall physical/mental well-being, quality of life, as well as life expectancy. To ask these complex questions, we propose a multi/inter-disciplinary team including a physical therapist (Bhat, PL), dietitian (Robson, Co-PL), epidemiologist (Patterson, Co-PL), cardiologist (Baker-Smith, Co-PL), and data scientists (Zhang, Brewer, Co- Is) who will work together to examine the National Survey of Children’s Health (NSCH), a large national, open- source database. We will compare cross-sectional data collected across 6 timepoints from years 2016-2021 to understand differences in children’s health and development before versus during the pandemic. We will also pool data across all timepoints (N=50K-90K per year, ~94K total sample in 2021) to report patterns of children’s health behaviors, development, and factors influencing them. NSCH tracks parent-reported behaviors of neurotypical children (~70% sample, N~67,000 in 2021) along with children with various neurodevelopmental disabilities (NDD, ~30% sample, N~27,000 in 2021) such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorders (ADHD), Anxiety, Learning Disability (LD), Speech Delay (SD), Intellectual Disability (ID), Down Syndrome (DS), Developmental Delay (DD), Seizures, and Behavioral/Mood Problems (BP). The majority of the current literature is focused on the 2016-2017 NSCH sample. So far, no one has compared the changing trends in health and development in children with NDD using the NSCH dataset. Few studies have used a holistic perspective including complex, multi-level factors to evaluate health and developmental outcomes. We will use statistical / data science approaches (e.g., machine learning) to characterize subgroups based on various factors and identify the key and proximal factors to make recommendations for generalized and tailored interventions targeting the needs of children with NDD. In the future, we will build on this work through a RM1 / multi-PI R01 team science application to conduct a more in-depth study using objective measures obtained from large-scale cohorts to inform holistic interventions (lifestyle changes, community resources, etc.) for children with NDD.

抽象的 神经发育不景气（NDD）的儿童面临许多个人（诊断特定，人群）， 父母/家庭有关，以及社区级别的障碍，他们优化其健康行为的能力 （身体活跃，健康饮食，适当地睡觉）和发展成果 （运动/参与，社会，行为，认知/学业表现）。 出现不良发展结果，身体不活动以及减少学校和社区娱乐活动的风险 与他们的神经型（NT）同行相比 超重/敬意和社会隔离的比率更高，进而影响其长期身体健康 （心血管疾病，糖尿病，寿命较短），心理健康（即焦虑，抑郁， 行为问题）以及未来的发展。 感兴趣的健康行为和NDD儿童的发展结果的因素最终有所改善 他们的整体身体/心理福祉，生活质量以及生活的竞争问题。 我们建议一个多/跨学科的团队，包括物理治疗师（BHAT，PL），营养师（Robson，Co-PL）， 流行病学家（Patterson，Co-PL），心脏病学（Baker-Smith，Co-PL）和数据科学家（Zhang，Brewer，Co-- IS）谁将共同研究国家儿童健康调查（NSCH），这是一个大型国家，开放的 - 来源数据库。 在大流行期间了解儿童健康和发展的差异 所有时间点的池数据（n =每年50k-90k，2021年总样本〜94k总样本），以报告儿童的模式 健康行为，发展和影响他们的因素。 神经型儿童（〜70％样本，2021年n〜67,000）以及各种神经发育的儿童 疾病（NDD，〜30％样本，2021年n〜27,000），例如自闭症谱系障碍（ASD），ATTINTIN不足 多动症（ADHD），焦虑，学习障碍（LD），语音延迟（SD），智力障碍（ID）， 唐氏综合症（DS），发育延迟（DD），癫痫发作和情绪问题（BP） 目前的文献集中在2016 - 2017年NSCH样本上。 使用NSCH数据集的NDD儿童健康和发展的趋势。 包括复杂的多层次因素，以评估健康和发展结果 统计 /数据科学方法（例如机器学习）以根据各种因素来表征亚组 并确定关键因素和近端因素，以进行通用和量身定制干预措施的推荐 针对NDD的儿童的需求。 团队科学应用程序，使用从大规模获得 为NDD儿童提供整体干预措施（生活方式改变，社区资源等）的同伙。