Enhancing Emotional Well-Being With IoT Data Solutions for Depression: A Systematic Review

Area of Science:

• Digital Health
• Mental Health Technology
• Systematic Literature Review

Background:

• Managing adult depression presents significant challenges.
• Technology offers potential for enhanced emotional well-being monitoring.
• Current data collection and analysis methods for depression are fragmented.

Purpose of the Study:

• To explore the use of Internet of Things (IoT) for data collection and analysis in managing depression.
• To identify how IoT can enhance emotional well-being and depression management.
• To review existing literature on IoT applications in mental health.

Main Methods:

• Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework.
• Selection of 42 studies from an initial pool of 559 published works.
• Analysis of literature on IoT for emotion detection, analysis, monitoring, and depression management.

Main Results:

• Extensive coverage found in literature regarding IoT for emotion detection, analysis, and monitoring.
• Significant focus on therapeutic interventions for emotional well-being and depression management.
• Predominant use of sensors for IoT data collection and Artificial Intelligence (AI) for analysis.

Conclusions:

• A notable gap exists in prioritizing active, user-engaging IoT systems for depression management.
• There is a critical need to address privacy and security concerns in IoT-based mental health solutions.
• Further research should focus on developing integrated and secure IoT platforms for effective depression care.