A Gamified AI-Driven System for Depression Monitoring and Management

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments.

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Publication Details

Journal
Applied Sciences
Published
2025-06-24
DOI
https://doi.org/10.3390/app15137088
Citations
4
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
6.18
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article

A Gamified AI-Driven System for Depression Monitoring and Management

Ali Rostami, Samaneh Madanian, Roopak Sinha, Minh Nguyen et al.
4 citations
Applied Sciences
Digital Mental Health Interventions
6.18
article

A Gamified AI-Driven System for Depression Monitoring and Management

Ali Rostami, Samaneh Madanian, Roopak Sinha, Minh Nguyen, Sanaz Zamani
article en
4 citations

Abstract

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments.

Applied SciencesVol. 15(13)
Deakin University (AU), Amirkabir University of Technology (IR), Auckland University of Technology (NZ)
Openalex Percentile: Top 3%
Digital Mental Health Interventions
6.18
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A Gamified AI-Driven System for Depression Monitoring and Management — Ali Rostami, Samaneh Madanian, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS