Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit
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 unk...
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| Published in | PloS one Vol. 20; no. 8; p. e0329707 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
13.08.2025
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0329707 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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.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. |
| Audience | Academic |
| Author | Pereira-Lima, Karina Funk, Courtney Sen, Srijan Kheterpal, Vik Fang, Yu Frank, Elena Zhao, Zhuo Horwitz, Adam G. |
| AuthorAffiliation | 1 Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America 3 Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America Villanova University, UNITED STATES OF AMERICA 4 CareEvolution, Ann Arbor, Michigan, United States of America 2 Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America |
| AuthorAffiliation_xml | – name: 1 Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America – name: Villanova University, UNITED STATES OF AMERICA – name: 2 Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America – name: 4 CareEvolution, Ann Arbor, Michigan, United States of America – name: 3 Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America |
| Author_xml | – sequence: 1 givenname: Courtney surname: Funk fullname: Funk, Courtney – sequence: 2 givenname: Zhuo surname: Zhao fullname: Zhao, Zhuo – sequence: 3 givenname: Adam G. orcidid: 0000-0002-6087-7950 surname: Horwitz fullname: Horwitz, Adam G. – sequence: 4 givenname: Yu surname: Fang fullname: Fang, Yu – sequence: 5 givenname: Karina orcidid: 0000-0003-4313-743X surname: Pereira-Lima fullname: Pereira-Lima, Karina – sequence: 6 givenname: Vik surname: Kheterpal fullname: Kheterpal, Vik – sequence: 7 givenname: Srijan surname: Sen fullname: Sen, Srijan – sequence: 8 givenname: Elena orcidid: 0000-0001-7862-9042 surname: Frank fullname: Frank, Elena |
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| Snippet | Privacy is a growing concern in mobile health research, particularly regarding passive data. Apple SensorKit provides a novel platform for collecting phone and... |
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| SubjectTerms | 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 |
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| Title | Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit |
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