ArticlePLOS digital health2026
Exploring digital health user engagement: General app usage patterns from a clinical trial with the mLab App.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03803683 (mLab App for Improving Uptake of Rapid HIV Self-testing and Linking Youth to Care), which is not on this map. Cited by 1 paper.
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.
mLab App for Improving Uptake of Rapid HIV Self-testing and Linking Youth to Care
Who cites it
1 citing paper in PubMed.
- Detailed HIV Self-Testing Patterns Derived from Paradata in the mLab App Clinical Trial.AIDS and behavior · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of digital health applications has rapidly grown over recent years, but many of them are limited by sustainability and scalability. Paradata, the detailed interactions of a user with a piece of software, is straightforward to collect without disrupting the user-experience, and can provide a nuanced understanding of in-app user behavior. In this work we analyze paradata from the mLab App arm of a three-arm, multi-site randomized clinical trial (NYC and Chicago), in a sub-study that evaluates longitudinal, session-level interaction logs with multiple sessions per participant, (registered with Clinicaltrials.gov as NCT03803683), a mobile health application to facilitate at-home HIV testing in at-risk populations. We investigated application-level usage statistics to identify feature usage, as well as common navigational paths within the application. Temporal patterns were observed for login events and test-taking patterns. Despite being under-collected and underreported, paradata reveal feature gaps, guide targeted revisions for subsequent research and implementation, and deepen understanding of user behavior-enabling digital-health applications with lasting impact.
Identifiers
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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.