# Requirements and Concerns of Individuals Remitted From Depression for an Early Relapse Detection mHealth App: Focus Group Study

## TL;DR

This study explores what people who have recovered from depression want and worry about in an app that helps detect early signs of relapse.

## Contribution

The study identifies user-driven requirements and concerns for designing an mHealth app for early depression relapse detection.

## Key findings

- Participants emphasized the need for customization in data collection and self-assessment frequency.
- Transparency in data use and machine learning predictions was deemed essential for trust.
- Privacy and emotional burdens from passive and active monitoring were key concerns.

## Abstract

Major depressive disorder is often a recurrent condition, with a high risk of relapse for individuals remitted from depression. Early detection of relapse is critical to improve clinical outcomes. Mobile health (mHealth) technologies offer new opportunities for real-time monitoring and prevention of relapse, if the user requirements of the target population are effectively implemented.

This study investigated the requirements and concerns of individuals remitted from depression for an mHealth app aimed at monitoring depressive symptoms and detecting early signs of relapse through integrating both active ecological momentary assessment data and passive data from the user’s smartphone and smartwatch.

Three focus group discussions were conducted with 17 participants remitted from depression. Before the focus group, participants had gained some experience with an in-house designed ecological momentary assessment monitoring app, prompting questions regarding their mood multiple times throughout the day. During the focus groups, feedback and insights were gathered on participants’ expectations, requirements, concerns, and attitudes toward a depression monitoring app. A thematic analysis was performed to identify recurring themes and subthemes, shedding light on the desired user experience and functionalities.

We identified 5 main themes. Participants highlighted (1) a need for customization settings, particularly in terms of data collection and sharing, and frequency of self-assessments. They also valued (2) positivity in the app’s design through positive reinforcement and journaling features. Additionally, participants emphasized (3) interventions to be the main motivator for adoption and long-term usage. More specifically, they wanted the app to foster self-awareness, self-reflection, and insights, and to offer support during deteriorations in mental health. Furthermore, participants deemed (4) transparency in data use and machine learning predictions to be essential for building trust. Participants required these functionalities to bear (5) the user burdens of self-monitoring. Key concerns were for passive monitoring to cause a privacy burden and for active monitoring to raise an emotional burden.

Considering the vulnerability of potential users, the design of an mHealth app for early depression relapse detection should be guided by user preferences and approached with caution. Requirements for customization, positivity, interventions, and transparency must be addressed, while minimizing both the emotional and privacy burden. Future iterations should implement these findings to improve and test the app’s acceptability, adoption, and usability for clinical use.