JMIR Publications

ABSTRACT

Background:

User experience research is increasingly important for designing, evaluating, and improving digital health technologies, yet many approaches remain manual, resource intensive, and difficult to integrate across the innovation lifecycle. AI-enabled user research software offers capabilities spanning study planning, recruitment, data collection, behavioral analytics, synthesis, and insight translation, but its role in academic digital health research remains unclear.

Objective:

The objective of our study was to identify and characterize user research software relevant to digital health innovation, examine how these software platforms have been used and reported in peer-reviewed digital health studies involving patient, caregiver, or public end users, and identify methodological gaps and future opportunities for software-supported user research.

Methods:

We conducted a two-phase study comprising a software identification and content analysis followed by a rapid scoping review informed by Joanna Briggs Institute guidance and PRISMA-ScR. In Phase 1, user research software was identified through a structured Google search and characterized using publicly available website information. In Phase 2, PubMed records published from 2021 onward were searched for use of the identified software platforms from Phase 1, supplemented by a ChatGPT-assisted search. Eligible studies used at least one of these platforms to conduct remote or online user research representing patient, caregiver, or public end users of a digital health technology. Findings were synthesized descriptively and narratively, including software use, theory use, alignment with Phase 1 software capabilities, and translation of findings into digital health innovation changes.

Results:

Phase 1 identified 36 user research software platforms with capabilities spanning 11 areas of the user research lifecycle, from study planning and participant recruitment through usability testing, behavioral analytics, and insight synthesis. The rapid scoping review included 14 studies. Academic use of the user research software was concentrated on usability testing (9/14), with less frequent use for recruitment (4/14), exploratory qualitative feedback (5/14), structured survey feedback (5/14), and user behavior analytics (4/14). How studies used the user research software to support study planning, innovation experimentation, and insight synthesis were not clearly documented. Academic theories, models, and frameworks were inconsistently integrated with software use, and studies varied in how explicitly they connected the user research findings to implemented or proposed digital health changes.

Conclusions:

AI-enabled user research software has the potential to support more of the digital health innovation lifecycle than is currently reflected in academic research. Future work should determine how these emerging AI software capabilities can support digital health user research that is rigorous, transparent, and methodologically grounded across design, implementation, and ongoing optimization.