Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit

• Published in: PloS one Vol. 20; no. 8; p. e0329707
• Main Authors: Funk, Courtney, Zhao, Zhuo, Horwitz, Adam G., Fang, Yu, Pereira-Lima, Karina, Kheterpal, Vik, Sen, Srijan, Frank, Elena
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.08.2025; Public Library of Science (PLoS)
• Subjects: Acceptability; Adult; Biology and Life Sciences; Cell Phone; Colleges & universities; Data collection; Data Collection - methods; E-health; Enrollments; Feasibility; Feasibility Studies; Female; Humans; Information management; Internships; Keyboards; Light; Longitudinal Studies; Male; Medical personnel; Medical research; Medical residencies; Medical schools; Medicine and Health Sciences; Medicine, Experimental; Mental health; Methods; Mobile Applications; People and Places; Privacy; Race; Racial differences; Sensors; Social Sciences; Technology application; Telemedicine; Wearable Electronic Devices; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0329707

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.