ArticlePloS one2025
Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Design and rationale of the my heart counts cardiovascular health study: a large-scale, fully digital biobank, and randomized trial of large language model-driven coaching of physical activity.American journal of preventive cardiology · 2026Article
- Patterns of smartphone typing performance by time awake: implications for unobtrusive ambulatory mental fatigue assessment.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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