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Effectiveness and predictors of response for a technology-based reading intervention in the home

基于技术的家庭阅读干预的有效性和反应预测因素

基本信息

批准号：
10058660
负责人：
Nicole Landi
金额：
$ 68.86万
依托单位：
UNIVERSITY OF CONNECTICUT STORRS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10058660
关键词：
Address Adult Affect Aftercare Age Anatomy Area Attention Base of the Brain Behavior Brain Child Clinical Cognitive Control Groups Country Data Development Disabled Children Disadvantaged Educational process of instructing Effectiveness Electroencephalography Emotional Enrollment Environment Environmental Risk Factor Ethics Family Fostering Goals Home environment Hour Image Impaired cognition Intervention Learning Learning Disabilities Learning Disorders Letters Life Machine Learning Magnetic Resonance Imaging Matched Group Mathematics Mental Health Meta-Analysis Methodology Modeling Nature Neurobiology Occupational Outcome Participant Patient Recruitments Phenotype Play Prediction of Response to Therapy Predictive Factor Public Health Randomized Randomized Controlled Trials Reader Reading Reading Disabilities Recommendation Research Personnel Resources Sample Size Sampling School-Age Population Schools Services Specialist Students Supervision Technology Time Training Treatment outcome Variant Work active control base boys comorbidity cost dosage effectiveness evaluation evidence base functional disability girls intervention effect language impairment multimodality phonology post intervention predicting response predictive modeling programs random forest remediation response skills sound

项目摘要

Project Summary/Abstract. Reading Disability (RD) is the most common learning disability, affecting 10 – 15% of school age children. It incurs major functional impairments at all stages of life, with a wealth of data documenting lifelong disadvantages in educational and occupational attainment. Therefore, identifying effective and affordable treatments for RD is a high priority for reading researchers, clinicians and educators. Problematically, current evidence-based reading interventions largely rely on services by trained specialists, either in well-resourced classrooms or clinical settings. As such, under-resourced schools (or countries) often are unable to provide reading interventions for their students. In recent years, technology-based reading interventions have been proposed as a means of overcoming these challenges, as they can be administered in the home, without direct expert supervision - thereby minimizing resource demands. In the area of reading-focused EdTech, GraphoLearn has emerged as a leader, with the largest evidence-base to date. However, the vast majority of studies to date have been conducted in highly controlled settings, rather than the home environment it was intended for – leaving open questions about effectiveness. Additionally, similar to any intervention, not all children with RD benefit equally from treatment; however little attention has been given to identifying predictors of treatment response. Here we propose to evaluate the effectiveness of home-administered GraphoLearn through the implementation of a large-scale, randomized controlled trial (RCT) in 450 reading disabled children (boys and girls, ages 6.0- 10.0). To accomplish this goal rapidly and with minimal cost, we will recruit participants from the Healthy Brain Network [HBN], an ongoing study of mental health and learning disorders in children, ages 5.0-21.0, whose family have concerns about behavior and/or learning (target n = 10,000; current n = 3000+). The availability of comprehensive characterizations (e.g., demographic, cognitive, mental health, EEG, multimodal MRI) for all HBN participants makes the sample optimal for exploring an extensive set of participant and environmental factors that may affect treatment outcomes (i.e., demographic, cognitive, emotional, neurobiological, environmental). Specific aims of the proposed work are to: 1) Evaluate the effectiveness of GraphoLearn in a large sample of children with RD, and 2) Identify participant-related and environment-level factors that are significantly associated with GraphoLearn outcomes. To accomplish this latter aim, sophisticated machine learning approaches (Random Forest Regression models) will be employed.

项目总结/抽象。