Doctoral Dissertation Research: Leveraging Intensive Time Series of Accelerometer Data to Assess Impulsivity and Inattention in Preschool Children

博士论文研究：利用加速计数据的密集时间序列来评估学龄前儿童的冲动和注意力不集中

• 批准号: 2120223
• 负责人: Elizabeth Gershoff
• 金额: $ 1.8万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120223&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Leveraging; Intensive

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This doctoral dissertation research project will develop and examine the validity of a sensor-based approach for assessing children's impulsive and inattentive behaviors. Understanding children's impulsive and inattentive behaviors is important because, even early in life, they predict later academic achievement and outcomes as diverse as employment, mental health, and substance use in adulthood. However, current methods to assess these important behaviors in real-world contexts are limited because they are subjective, imprecise, and resource intensive. This project will develop an objective, precise method of assessment with low-participant burden and the ability to assess behavior continually throughout the day. The approach developed in this project will be of value to applied researchers from a variety of fields, including education, pediatrics, and developmental and clinical psychology. Code and de-identified data from the project will be made available to the broader community. The investigators will recruit and mentor undergraduate research assistants from the diverse student body at the University of Texas at Austin.This research project will develop and examine the validity of using wearable accelerometer devices to assess children's impulsive and inattentive behaviors in their preschool classrooms. Preschool-aged children will wear accelerometer devices while at school for one week. The devices will record data each second, capturing complex patterns of movement as children engage in classroom activities. Accelerometers are an ideal choice of sensor because children's impulsivity and inattention involve physical movement, such as getting up from one's chair, that will register as movement on the accelerometers. The analyses will use confirmatory and exploratory machine learning techniques to identify patterns in the intensive accelerometer data that index impulsivity or inattention. Analyses will go beyond the commonly used summaries of movement from accelerometers and instead take advantage of the rich patterns of movement that such summaries discard. The investigators will validate the accelerometer approach against existing measures of children's impulsivity and inattention. These results of this project will facilitate applied research and enhance our understanding of children's behavior in their real-world environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。本博士论文研究项目将开发和检验基于传感器的方法评估儿童的冲动和疏忽行为的有效性。了解儿童的冲动和注意力不集中的行为是很重要的，因为即使在生命的早期，他们预测以后的学业成就和结果，如就业，心理健康和成年后的物质使用。然而，目前的方法来评估这些重要的行为在现实世界中是有限的，因为它们是主观的，不精确的，和资源密集型。该项目将开发一种客观，精确的评估方法，具有低参与者负担和全天持续评估行为的能力。在这个项目中开发的方法将是有价值的应用研究人员从各个领域，包括教育，儿科，发展和临床心理学。该项目的代码和去识别数据将提供给更广泛的社区。研究人员将从德克萨斯大学奥斯汀分校的多元化学生中招募和指导本科生研究助理。该研究项目将开发和检查使用可穿戴加速计设备评估儿童在学前班教室中的冲动和疏忽行为的有效性。学龄前儿童将在学校佩戴加速度计设备一周。这些设备将每秒记录数据，捕捉孩子们在课堂活动中的复杂运动模式。加速度计是传感器的理想选择，因为儿童的冲动和注意力不集中涉及身体运动，例如从椅子上站起来，这将在加速度计上记录为运动。这些分析将使用验证性和探索性机器学习技术来识别指示冲动或注意力不集中的密集加速度计数据中的模式。分析将超越通常使用的加速度计的运动总结，而是利用这种总结所丢弃的丰富的运动模式。研究人员将验证加速度计的方法对现有的措施，儿童的冲动和注意力不集中。该项目的这些成果将促进应用研究，并增强我们对儿童在现实世界环境中行为的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。