Evidence map›Paper›PMID 42015941›Full record

ArticleData in brief2026

Longitudinal datasets of health app reviews for privacy and trust modeling.

Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Timoteo KellyInstitute for Data Science & Informatics, University of Missouri, Columbia, MO, USA.
Abdulkadir KorkmazDept. Electrical Engineering & Computer Science, University of Missouri, Columbia, MO, USA.
Samuel MalletDept. Electrical Engineering & Computer Science, University of Missouri, Columbia, MO, USA.
Connor SoudersDept. Electrical Engineering & Computer Science, University of Missouri, Columbia, MO, USA.
Sadra AliakbarpourDept. of Computer Science, Yale University, New Haven, CT, USA.
Praveen RaoInstitute for Data Science & Informatics, University of Missouri, Columbia, MO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present Health App Reviews for Privacy & Trust (HARPT), a large-scale annotated corpus of user reviews from patient portal and telehealth applications (apps) aimed at advancing research in user privacy and trust. The dataset comprises 480,450 user reviews labeled across seven classes that capture critical aspects of trust in applications, trust in providers, and privacy concerns. Our multistage strategy integrated keyword-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers. In parallel, we manually annotated a curated subset of 7000 reviews to support the development and evaluation of machine learning models. We benchmarked a broad range of models, providing a baseline for future work. HARPT is released under an open resource license to support reproducible research in usable privacy, trust and health informatics.

Indexed as

Cognitive securityDigital healthmHealth appsNatural language processingPatient portalTelehealthText classificationTrust

Identifiers

PMID42015941
PMCPMC13092470

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.