Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Participatory Medicine

Date Submitted: Dec 23, 2025
Date Accepted: Mar 10, 2026

The final, peer-reviewed published version of this preprint can be found here:

Integrating Data Visualizations Into Digital Mental Health Care for Adults With Anxiety and Depression: Participatory Design and Case Study

Torous J, Bodner R, Lim K, Ledley K, Wang S, Schueur L

Integrating Data Visualizations Into Digital Mental Health Care for Adults With Anxiety and Depression: Participatory Design and Case Study

J Particip Med 2026;18:e90255

DOI: 10.2196/90255

PMID: 36037122

Integrating Data Visualizations Into Digital Mental Health Care for Adults With Anxiety and Depression: Participatory Design and Case Study

  • John Torous; 
  • Rebekah Bodner; 
  • Katherine Lim; 
  • Kathryn Ledley; 
  • Shiwei Wang; 
  • Luke Schueur

ABSTRACT

Background:

Digital phenotyping offers unprecedented opportunities for capturing real-time mental health data through smartphones, yet translating this data into clinically actionable insights remains challenging. While smartphones can generate nearly one million data points per patient per day, healthcare systems have struggled to incorporate even basic ecological momentary assessment data into routine care workflows.

Objective:

This paper presents a model for clinician-facing data visualizations that can be shared with patients to increase understanding of mental health symptoms and enhance shared decision-making. We describe (1) a participatory design process through which visualizations were co-created with clinicians; (2) integration of these visualizations into a Digital Navigator-supported workflow; and (3) a case example illustrating how data visualizations can enhance patient insight and support treatment adjustments

Methods:

This work was conducted within the Digital Clinic program at Beth Israel Deaconess Medical Center. Fifteen clinicians and three clinical supervisors participated in a participatory design process to develop visualizations meeting clinical workflow needs. Data visualizations were integrated into weekly Digital Navigator sessions following a three-phase model (guide, refinement, autonomy) based on self-determination theory.

Results:

Six visualization types were developed: gauge charts for engagement behaviors, symptom trajectory graphs, correlation matrices linking passive and active data, sleep visualizations, polar/radar charts for multidimensional assessment, and passive-active data integration graphs. A clinical case demonstrates how these visualizations, when delivered through structured Digital Navigator facilitation, supported patient engagement, behavioral insight, and autonomous self-management across an eight-week treatment program.

Conclusions:

Thoughtfully designed data visualizations, when developed collaboratively with clinicians and delivered through structured support, can transform digital phenotyping from a technical capability into a practical tool for enhancing engagement, therapeutic alliance, and patient outcomes in digital mental health care.


 Citation

Please cite as:

Torous J, Bodner R, Lim K, Ledley K, Wang S, Schueur L

Integrating Data Visualizations Into Digital Mental Health Care for Adults With Anxiety and Depression: Participatory Design and Case Study

J Particip Med 2026;18:e90255

DOI: 10.2196/90255

PMID: 36037122

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© 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.