Novel wearable sensor calibration and validation for automated measurement of screen time in children

新型可穿戴传感器校准和验证，用于自动测量儿童屏幕时间

• 批准号: 10585840
• 负责人: Erik A Willis
• 金额: $ 67.91万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585840
• 关键词: Accelerometer; Adult; Algorithms; Behavior; Books; Calibration; Caregivers; Cellular Phone; Characteristics; Child; Child Health; Child Welfare; Classification; Clip; Code; Color; Computers; Data; Data Collection; Detection; Development; Devices; Disease; Dose; Effectiveness; Electronics; Environment; Error Sources; Eyeglasses; Family; Glass; Guidelines; Health; Health behavior; Household; Hybrids; Intervention; Knowledge; Laboratories; Learning; Life Style; Light; Location; Measurement; Measures; Methods; Modeling; Monitor; Movement; Obesity; Outcome; Parents; Patient Self-Report; Physical activity; Play; Predisposition; Prevalence; Protocols documentation; Qualifying; Randomized; Reading; Research; Research Personnel; Risk; Risk Factors; Risk Management; Sleep; Structure; Tablets; Techniques; Testing; Time; Training; Validation; Validity and Reliability; Video Recording; Work; advanced analytics; body position; classification algorithm; computerized data processing; cost effective measures; digital media; evidence base; evidence based guidelines; innovation; instrument; machine learning algorithm; machine learning method; member; novel; open source; poor sleep; prevent; response; sedentary; sedentary lifestyle; sensor; sleep quality; social contact; standard measure; temporal measurement; tool; tv watching; wearable device; wearable sensor technology

Abstract Over the past decade, the use of digital media and electronic screens has grown substantially. Existing methods for studying effects of digital media on child health and wellbeing are insufficient to assess the intermittent, on- demand, and interactive forms of media (e.g., tablets, smartphones) that are intermeshed within families’ daily activities. Calls for higher quality research have been made to better understand and evaluate the effect of electronic screens on children’s health outcomes. While scientific progress has been made with advanced analytics and data processing techniques using wearable devices for other health behaviors including physical activity, sedentary time, and sleep, insufficient research has been conducted on the development and calibration of wearable sensor measurement of electronic screen use. Wearable devices that include a color light sensor combined with advanced machine learning methods is an emerging and promising measure of electronic screen exposure in adults. However, there is a scientific need to extend this approach to free-living calibrations with natural observation and validation in children. The overarching aim of this project is to develop and validate a device-based measure of electronic screen use for children. The specific aims are to: 1) evaluate estimation accuracy of machine learning algorithms developed under controlled and free-play screen and non-screen activities using features extracted from a wearable multi-sensor, 2) compare accuracy of electronic screen use estimation across activities (e.g., watching tv, reading a book), screen type (e.g., TV, smartphone, tablet), body position (e.g., sitting, lying, standing), and ambient light level (e.g., florescent room light, natural light), and 3) assess wear method placement, day to day variability, and compare estimates with the established Comprehensive Assessment of Family Media Exposure (CAFE) tool obtained during 7 days of free-living wear. Our highly qualified research team will address these aims by using hybrid-structured and semi-structured activity observation to train and refine sensor-derived, machine-learned algorithms for assessing screen time as compared to direct observation. Additionally, a free-living calibration protocol will then be used to evaluate wearable sensor algorithms for the estimation of screen time in naturalistic settings. The results of this work will- -for the first time--provide an innovative and translatable approach to assess free-living electronic screen use in children.

抽象的 在过去的十年中，数字媒体和电子屏幕的使用大幅增长。现有方法 研究数字媒体对儿童健康和福祉的影响不足以评估间歇性、持续性的 需求以及融入家庭日常生活的互动形式的媒体（例如平板电脑、智能手机） 活动。人们呼吁进行更高质量的研究，以更好地理解和评估效果 显示儿童健康状况的电子屏幕。虽然科学技术已经取得了进步，但 使用可穿戴设备进行其他健康行为（包括身体健康）的分析和数据处理技术 活动、久坐时间和睡眠，对开发和校准的研究不足 电子屏幕使用的可穿戴传感器测量。包含色光传感器的可穿戴设备 结合先进的机器学习方法是一种新兴且有前途的电子屏幕测量方法 成人暴露。然而，科学上需要将这种方法扩展到自由校准 儿童的自然观察和验证。该项目的总体目标是开发和验证 基于设备的儿童电子屏幕使用测量。具体目标是：1）评估估计 在受控和自由游戏屏幕和非屏幕下开发的机器学习算法的准确性 使用从可穿戴多传感器提取的特征进行活动，2) 比较电子屏幕使用的准确性 跨活动（例如看电视、读书）、屏幕类型（例如电视、智能手机、平板电脑）、身体的估计 位置（例如，坐着、躺着、站立）和环境光水平（例如，室内荧光灯、自然光），以及 3) 评估佩戴方法的放置、日常变化，并将估计值与既定值进行比较 在 7 天的自由生活佩戴期间获得的家庭媒体暴露综合评估 (CAFE) 工具。 我们高素质的研究团队将通过使用混合结构和半结构来实现这些目标 活动观察，以训练和完善传感器衍生的机器学习算法，以评估屏幕时间 与直接观察相比。此外，随后将使用自由校准协议来评估 可穿戴传感器算法，用于估计自然环境中的屏幕时间。这项工作的结果将—— -首次提供一种创新且可翻译的方法来评估自由生活电子屏幕的使用情况 孩子们。