What is it about?
This paper presents a systematic review that explores how Internet of Things (IoT) technologies and data‐driven solutions are applied in the context of depression and emotional well-being. It examines how IoT devices (such as ambient sensors and wearables) are used to collect data, how artificial intelligence or machine-learning techniques analyze that data, and how those systems support monitoring, detecting, or intervening in depressive symptoms or emotional states. The review also identifies how many existing works focus on passive monitoring versus more active user engagement, and it highlights gaps related to user engagement, privacy/security concerns, and the need for more holistic systems.
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Why is it important?
The importance of this study lies in its consolidation of evidence that IoT-based data systems are increasingly becoming part of the mental health and depression space. Because depression is a widespread and complex condition with a substantial impact on individuals’ emotional well-being, finding technological means to support monitoring and intervention is highly valuable. By reviewing the state of the art, the paper helps to clarify what has been done, what works, and what remains under-addressed, especially the transition from passive sensing to systems that actively engage users and provide intervention rather than just monitoring. This is especially relevant for your research interest in integrated intervention systems that combine monitoring with adaptive environmental or behavioural responses.
Perspectives
From a technical perspective, the review suggests that while many IoT systems for depression focus on sensing (data collection) and analysis (AI/ML), fewer emphasize the feedback/intervention loop and user engagement. From a personalisation perspective, there is a gap in tailoring systems to individual needs, preferences, and contexts (which aligns with your interest in adaptive models). From a deployment and ethical perspective, issues such as data privacy, user consent, system usability, and maintaining long-term engagement become critical. From a future research perspective, this review points toward richer multi-modal data (beyond simple sensor metrics), more active closed-loop systems (monitor → detect → intervene → adapt), and real-world studies (not just prototypes) to validate efficacy. In your work, these perspectives help position the field, identify its current state, and outline its future direction, highlighting how your IoT-based depression management system can contribute.
Sanaz Zamani
Auckland University of Technology
Read the Original
This page is a summary of: Enhancing Emotional Well-Being With IoT Data Solutions for Depression: A Systematic Review, IEEE Journal of Biomedical and Health Informatics, January 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/jbhi.2024.3501254.
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