Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts.
Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts.
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DOI：
10.1093/gigascience/giab044
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发表时间：
2021-06-21
期刊：
影响因子：
9.2
通讯作者：
Nagy Z
中科院分区：
文献类型：
--
作者：
Wu C;Fritz H;Bastami S;Maestre JP;Thomaz E;Julien C;Castelli DM;de Barbaro K;Bearman SK;Harari GM;Cameron Craddock R;Kinney KA;Gosling SD;Schnyer DM;Nagy Z
随着移动的技术变得越来越传感器丰富、便携和无处不在，智能设备捕获的数据以前所未有的全面性和生态有效性为用户的日常生活提供了丰富的见解。已经进行了许多人类受试者研究，以检查使用移动的传感来揭示个人行为模式和健康结果，但很少关注测量生活环境以及其他以人为中心的传感数据。此外，在大多数现有研究中，参与者样本量福尔斯远低于几百人，这使得关于移动的传感信号与人类结果之间关系的研究结果的可靠性存在疑问。为了解决这些局限性，我们开发了一种用于持续室内空气质量跟踪的家庭环境传感器套件，并将其与智能手机，Fitbits和生态瞬时评估一起部署在一项队列研究中，每种数据类型多达1，584名大学生参与者，为期3周。我们提出了一个概念框架，系统地组织以人为中心的数据模态的时间覆盖和空间自由。然后，我们报告了我们的研究程序，所采用的技术和方法，以及对所收集的数据的描述性统计，这些数据反映了参与者的情绪，睡眠，行为和生活环境。我们能够从一个大的参与者队列收集令人满意的完整的多模态传感和调查数据的数据连续性和参与者的依从性。我们的新数据和概念发展为未来以人为中心的传感研究中的数据收集和假设生成提供了重要指导。
As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.
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影响因子：
4.3
作者：
Aharony, Nadav;Pan, Wei;Pentland, Alex
通讯作者：
Pentland, Alex
影响因子：
2.8
作者：
de Zambotti, Massimiliano;Goldstone, Aimee;Baker, Fiona C.
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Baker, Fiona C.
DOI：
10.1109/tsmc.2016.2562509
发表时间：
2017-12-01
影响因子：
8.7
作者：
Wannenburg, Johan;Malekian, Reza
通讯作者：
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DOI：
10.1177/1745691616650285
发表时间：
2016-11
期刊：
Perspectives on psychological science : a journal of the Association for Psychological Science
影响因子：
--
作者：
Harari GM;Lane ND;Wang R;Crosier BS;Campbell AT;Gosling SD
通讯作者：
Gosling SD
影响因子：
4.5
作者：
Cornet VP;Holden RJ
通讯作者：
Holden RJ