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

Area of Science:

• Digital Health
• Mobile Health Research
• Data Privacy

Background:

• Privacy concerns are rising in mobile health (mHealth) research, especially for passive data collection.
• Apple SensorKit offers a new way to gather phone and wearable sensor data, but its acceptability and feasibility are not well understood.

Purpose of the Study:

• To pilot the Apple SensorKit platform within a longitudinal study of medical residents.
• To explore racial and ethnic differences in the acceptability of passive sensor data collection.
• To assess the feasibility and participant retention in a large, diverse cohort study using SensorKit.

Main Methods:

• Utilized the Apple SensorKit platform for passive data collection.
• Recruited a large, demographically diverse cohort of US medical residents for a longitudinal study.
• Analyzed enrollment, retention, and opt-in rates, examining differences across racial and ethnic groups.

Main Results:

• Achieved successful enrollment and retention rates in the longitudinal e-Cohort study.
• Demonstrated the feasibility of collecting SensorKit data in a large-scale mHealth study.
• Observed lower opt-in rates for passive sensor data collection among racial minority participants.

Conclusions:

• Longitudinal mHealth studies using Apple SensorKit are feasible with good retention.
• Lower opt-in rates among minority groups highlight the need to investigate equity implications of specific data types.
• Further research is needed to ensure equitable data collection practices in digital health.