Sociodemographic characteristics of missing data in digital phenotyping.
Sociodemographic characteristics of missing data in digital phenotyping.
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DOI：
10.1038/s41598-021-94516-7
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发表时间：
2021-07-29
影响因子：
4.6
通讯作者：
Onnela JP
中科院分区：
文献类型：
--
作者：
Kiang MV;Chen JT;Krieger N;Buckee CO;Alexander MJ;Baker JT;Buckner RL;Coombs G 3rd;Rich-Edwards JW;Carlson KW;Onnela JP
无处不在的智能手机及其日益复杂的传感器阵列，为研究人员提供了前所未有的机会，可以收集有关人类行为的纵向、多样化、时间密集的数据，同时最大限度地减少参与者的负担。研究人员越来越多地利用智能手机进行“数字表型”，收集和分析原始手机传感器和日志数据，以研究受试者在自然环境中使用自己的设备的生活体验。虽然数字表现型在精神病学和神经科学等领域显示出前景，但我们对基于智能手机的数字表现型的数据收集和非收集（即缺失数据）的知识存在根本差距。在这项荟萃研究中，我们使用贝叶斯分层负二项回归分析了211名参与者的加速度计和GPS传感器数据，共29,500人日的观测数据，并采用研究和用户水平的随机截取。敏感性分析包括可选模型规格和分层模型。我们发现iOS用户的GPS不收集率低于Android用户。对于GPS数据，未收集率没有因种族/民族、教育、年龄或性别而异。对于加速度计数据，黑人参与者有更高的未收集率，但这一比率没有性别、教育程度或年龄的差异。对于这两种传感器，每周未收集的数据增加0.5%至0.9%。这些结果证明了在不同人群中使用基于智能手机的数字表型的可行性，可以延长时间，并在不同的队列中使用。随着智能手机越来越多地融入日常生活，这项研究的见解将有助于指导数字表型研究的设计、规划和分析。
The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.
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