Development of a smartphone application to measure physical activity using sensor-assisted self-report
Development of a smartphone application to measure physical activity using sensor-assisted self-report
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DOI：
10.3389/fpubh.2014.00012
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发表时间：
2014-01-01
影响因子：
5.2
通讯作者：
Intille, Stephen
中科院分区：
文献类型：
--
作者：
Dunton, Genevieve Fridlund;Dzubur, Eldin;Intille, Stephen
简介：尽管客观身体活动监测器（例如加速度计）具有已知的优势，但这些设备的不磨损率很高，从而导致数据丢失。客观活动监视器也无法捕获有关行为的有价值的上下文信息。参与体力活动监测和干预研究的青少年将越来越多地拥有智能手机，它们是内置运动传感器的微型计算机。方法：本文描述了一款名为 Mobile Teen 的智能手机应用程序（“应用程序”）的设计和开发，该应用程序通过（1）传感器通知的情境敏感生态瞬时评估（CS-EMA）和（2）传感器辅助的日终回忆结合了客观和自我报告评估策略。结果：Mobile Teen 应用程序使用手机的内置运动传感器自动检测可能出现的手机不佩戴、久坐行为和体力活动的情况。然后，该应用程序使用这些推断状态之间的转换来触发 CS-EMA 自我报告调查，实时测量活动的类型、目的和背景。 MobileTeen 应用程序的一天结束回忆组件允许用户使用手机内置运动传感器自动检测到的主要活动转变的视觉提示，以交互方式查看和标记自己每天晚上的身体活动数据。主要活动转换由应用程序识别，提示用户使用活动类别来标记“大块”或时间段。结论：传感器驱动的 CS-EMA 和日终回忆智能手机应用程序可用于增强客观活动监视器收集的身体活动数据，填补非磨损期间的空白，并提供有关行为的环境、社会和情感相关性的额外实时数据。诸如此类的智能手机应用程序有可能在大规模流行病学和干预研究中以经济实惠的方式部署。
Introduction: Despite the known advantages of objective physical activity monitors (e.g., accelerometers), these devices have high rates of non-wear, which leads to missing data. Objective activity monitors are also unable to capture valuable contextual information about behavior. Adolescents recruited into physical activity surveillance and intervention studies will increasingly have smartphones, which are miniature computers with built-in motion sensors.Methods: This paper describes the design and development of a smartphone application ("app") called Mobile Teen that combines objective and self-report assessment strategies through (1) sensor-informed context-sensitive ecological momentary assessment (CS-EMA) and (2) sensor-assisted end-of-day recall.Results: The Mobile Teen app uses the mobile phone's built-in motion sensor to automatically detect likely bouts of phone non-wear, sedentary behavior, and physical activity. The app then uses transitions between these inferred states to trigger CS-EMA self-report surveys measuring the type, purpose, and context of activity in real-time. The end of the day recall component of the MobileTeen app allows users to interactively review and label their own physical activity data each evening using visual cues from automatically detected major activity transitions from the phone's built-in motion sensors. Major activity transitions are identified by the app, which cues the user to label that "chunk," or period, of time using activity categories.Conclusion: Sensor-driven CS-EMA and end-of-day recall smartphone apps can be used to augment physical activity data collected by objective activity monitors, filling in gaps during non-wear bouts and providing additional real-time data on environmental, social, and emotional correlates of behavior. Smartphone apps such as these have potential for affordable deployment in large-scale epidemiological and intervention studies.