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

摘要 在过去的十年里，数字媒体和电子屏幕的使用大幅增长。现有方法 对于研究数字媒体对儿童健康和福祉的影响，不足以评估间歇性的、关于- 需求和互动形式的媒体(例如平板电脑、智能手机)交织在家庭日常生活中 活动。人们呼吁进行更高质量的研究，以更好地理解和评估 关于儿童健康结果的电子屏幕。虽然科学进步已经取得了先进的 使用可穿戴设备进行其他健康行为的分析和数据处理技术 在运动、久坐时间和睡眠方面，关于发展和校准的研究还不充分。 可穿戴式传感器测量电子屏幕的使用。包括彩色光传感器的可穿戴设备 与先进的机器学习方法相结合是一种新兴的、有前途的电子屏幕方法 在成人中暴露。然而，有科学上的需要将这种方法扩展到自由生活校准 儿童的自然观察和验证。该项目的总体目标是开发和验证 儿童电子屏幕使用的基于设备的测量。具体目标是：1)评估评估 可控自由播放屏幕和非屏幕下开发的机器学习算法的准确性 使用从可穿戴式多传感器提取的功能进行活动，2)比较电子屏幕使用的准确性 对活动(例如，看电视、看书)、屏幕类型(例如，电视、智能手机、平板电脑)、身体 位置(例如，坐着、躺着、站着)和环境光线水平(例如，房间荧光灯、自然光)，以及3) 评估磨损方法的位置、日常变化，并将估计值与已建立的值进行比较 家庭媒体曝光率(CAFE)综合评估工具在7天的自由生活穿戴期间获得。 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，训练和改进传感器派生的机器学习算法，用于评估屏幕时间，如 与直接观察相比。此外，还将使用自由生活校准方案来评估 可穿戴传感器算法，用于在自然环境中估计屏幕时间。这项工作的结果将是- -首次提供一种创新和可翻译的方法来评估自由生活的电子屏幕在 孩子们。

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.