Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

作者：Carsten Langholm, Tobias Kowatsch, Sandra Bucci, Andrea Cipriani, John Torous · 发表于：Digital Biomarkers · 年份：2023 · DOI：10.1159/000530698 · 被引用次数：17 · 研究领域：Digital Mental Health Interventions、Mobile Health and mHealth Applications、Mental Health Research Topics

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and proces...