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

被引：3
作者：
Langholm C. [1 ]
Kowatsch T. [2 ,3 ,4 ]
Bucci S. [5 ]
Cipriani A. [6 ,7 ,8 ]
Torous J. [1 ]
机构：
[1] Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
[2] Institute for Implementation Science in Health Care, University of Zurich, Zurich
[3] School of Medicine, University of St. Gallen, St. Gallen
[4] Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich
[5] Department of Psychiatry, University of Oxford, Oxford
[6] Department of Psychiatry, University of Manchester, Manchester
[7] Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford
[8] Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford
来源：
Digital Biomarkers | 2023年 / 7卷 / 01期
关键词：
Cortex; Digital phenotyping; Mobile applications; Psychiatry; SensorKit;
D O I：
10.1159/000530698
中图分类号：
学科分类号：
摘要：
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 processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data. © 2023 The Author(s). Published by S. Karger AG, Basel. permission.
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收藏
页码：104 / 114
页数：10
相关论文
共 23 条
  • [1] Torous J., Kiang M.V., Lorme J., Onnela J.P., New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research, JMIR Mental Health, 3, 2, (2016)
  • [2] DeBoever C., Tanigawa Y., Aguirre M., McInnes G., Lavertu A., Rivas M.A., Assessing digital phenotyping to enhance genetic studies of human diseases, Am J Hum Genet, 106, 5, pp. 611-622, (2020)
  • [3] Henson P., D'Mello R., Vaidyam A., Keshavan M., Torous J., Anomaly detection to predict relapse risk in schizophrenia, Transl Psychiatry, 11, 1, (2021)
  • [4] Mercier H.W., Hamner J.W., Torous J., Onnela J.P., Taylor J.A., Digital phenotyping to quantify psychosocial well-being trajectories after spinal cord injury, Am J Phys Med Rehabil, 99, 12, pp. 1138-1144, (2020)
  • [5] Mishra V., Kunzler F., Kramer J.N., Fleisch E., Kowatsch T., Kotz D., Detecting receptivity for mHealth interventions in the natural environment, Proc ACM Interact Mob Wearable Ubiquitous Technol, 5, 2, (2021)
  • [6] Kunzler F., Mishra V., Kramer J.N., Kotz D., Fleisch E., Kowatsch T., Exploring the state-ofreceptivity for mHealth interventions, Proc ACM Interact Mob Wearable Ubiquitous Technol, 3, 4, pp. 140-227, (2019)
  • [7] Banholzer N., Feuerriegel S., Fleisch E., Bauer G.F., Kowatsch T., Computer mouse movements as an indicator of work stress: longitudinal observational field study, J Med Internet Res, 23, 4, (2021)
  • [8] Tinschert P., Rassouli F., Barata F., Steurer-Stey C., Fleisch E., Puhan M.A., Et al., Nocturnal cough and sleep quality to assess asthma control and predict attacks, J Asthma Allergy, 13, pp. 669-678, (2020)
  • [9] Rassouli F., Tinschert P., Barata F., Steurer-Stey C., Fleisch E., Puhan M.A., Et al., Characteristics of asthma-related nocturnal cough: a potential new digital biomarker, J Asthma Allergy, 13, pp. 649-657, (2020)
  • [10] Barata F., Tinschert P., Rassouli F., Steurer-Stey C., Fleisch E., Puhan M.A., Et al., Automatic recognition, segmentation, and sex assignment of nocturnal asthmatic coughs and cough epochs in smartphone audio recordings: observational field study, JMed Internet Res, 22, 7, (2020)
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