Previously submitted to: JMIR Mental Health (no longer under consideration since Oct 13, 2026)
Date Submitted: Sep 12, 2026
Open Peer Review Period: Oct 13, 2026 - Oct 13, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
User Perceptions of Siloed Versus Time-Synchronized Data Architectures for AI-Based Digital Mental Health: Within-Subjects Scenario Study
ABSTRACT
Background:
Digital mental health systems increasingly combine electronic health records, mobile applications, self-reports, therapy interactions, and sensor data. Fragmented and asynchronous processing may limit whether these inputs are perceived as timely, complete, transparent, and explainable.
Objective:
This study examined whether users perceived differences in system quality, information quality, and net benefits between a traditional, siloed framework and a time-synchronized, integrated framework for artificial intelligence-based digital mental health.
Methods:
In a quantitative within-subjects scenario study, United States adults familiar with digital mental health technologies reviewed standardized descriptions of both frameworks and completed adapted measures of system quality, information quality, and net benefits. Paired-sample t tests were supplemented with Wilcoxon signed-rank tests. Valid paired samples were 277, 267, and 258 for the 3 constructs, respectively.
Results:
Ratings favored the integrated framework for system quality (integrated mean 3.47, SD 0.59; traditional mean 3.38, SD 0.57; t(276)=-2.66, P=.008; Cohen d=-0.16), information quality (integrated mean 3.48, SD 0.58; traditional mean 3.34, SD 0.59; t(266)=-4.49, P<.001; Cohen d=-0.28), and net benefits (integrated mean 3.79, SD 0.87; traditional mean 3.65, SD 0.86; t(257)=-3.04, P=.003; Cohen d=-0.19). Wilcoxon analyses supported the same conclusions. All standardized effects were small.
Conclusions:
Participants perceived a modest quality advantage for the integrated description, greatest for information quality. Because the study evaluated scenarios rather than functioning systems and bundled temporal alignment with integration, the findings do not demonstrate causal, technical, or clinical benefits. They provide user-centered criteria for subsequent prototype, implementation, and safety evaluation. Clinical Trial: N/A
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.