Funk Courtney, Zhao Zhuo, Horwitz Adam G, Fang Yu, Pereira-Lima Karina, Kheterpal Vik, Sen Srijan, Frank Elena
Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS One. 2025 Aug 13;20(8):e0329707. doi: 10.1371/journal.pone.0329707. eCollection 2025.
Privacy is a growing concern in mobile health research, particularly regarding passive data. Apple SensorKit provides a novel platform for collecting phone and wearable usage and sensor data, however the acceptability and feasibility of collecting these sensitive data to research subjects remain unknown. To address this gap, we piloted the SensorKit platform as part of the longitudinal Intern Health Study. Unlike prior research on digital privacy, which has often relied on small samples, this study leverages a large and demographically diverse cohort of US medical residents to explore racial and ethnic differences in the acceptability of passive sensor data collection. Findings demonstrate that successful enrollment and retention rates can be achieved in a longitudinal e-Cohort study that collects SensorKit data, however lower opt-in rates among racial minorities suggest the need for further evaluation of the equity implications around specific data types in mobile health research.
隐私问题在移动健康研究中日益受到关注，尤其是涉及被动数据时。苹果传感器套件提供了一个用于收集手机和可穿戴设备使用情况及传感器数据的新颖平台，然而，向研究对象收集这些敏感数据的可接受性和可行性仍然未知。为了填补这一空白，我们将传感器套件平台作为纵向实习医生健康研究的一部分进行了试点。与以往常常依赖小样本的数字隐私研究不同，本研究利用了大量且人口统计学特征多样的美国医学住院医师队列，以探讨被动传感器数据收集可接受性方面的种族和族裔差异。研究结果表明，在收集传感器套件数据的纵向电子队列研究中可以实现成功的招募和保留率，然而少数族裔较低的选择加入率表明，需要进一步评估移动健康研究中围绕特定数据类型的公平性影响。
