ArticleSensors (Basel, Switzerland)2026
Metadata-Based Privacy Assessment for Mobile mHealth.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The widespread adoption of mobile health applications has increased the volume of sensitive personal and physiological data processed through interconnected devices. Ensuring privacy compliance in this context remains a challenge, as existing app stores and privacy labeling systems rely heavily on self-declared information. App-PI is a data-driven ecosystem designed to offer end users with tools they can easily manage and privacy researchers with structured and reliable app metadata. It is designed to automate the collection, analysis, and visualization of privacy-related metadata from mobile applications. Heterogeneous data sources are integrated into a unified repository (App-PIMD), enabling the empirical assessment of privacy risks. The data flow design is critical to ensure that the data used to assess privacy impact is of good quality, as well as the privacy indicators that end users will be offered. It is shown on a popular mHealth application, demonstrating the importance of data flow design in order to be able to obtain, from documents and files created for consumption by an operating system, a set of data and tools ready for consumption by the true recipients of health apps: people.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.