Management of Behavioral Addictions via Artificial Intelligence based Digital Technologies

Behavioral addictions, such as problematic gaming, gambling, social media use, and online shopping, have emerged as a growing global concern with significant psychological, social, and economic consequences. Traditional assessment and treatment approaches, while clinically grounded, often struggle with scalability, accessibility, and long-term engagement. In recent years, advances in digital technologies and artificial intelligence (AI) have enabled the development of novel, data-driven solutions for the prevention, detection, treatment, and recovery of behavioral addictions. This book presents a comprehensive survey of the management of behavioral addictions through digital technologies, bridging clinical foundations with state-of-the-art computational approaches. Firstly, the clinical and psychological underpinnings of behavioral addictions are outlined. These include neurobiological reward mechanisms, cognitive-behavioral models, and comorbidity patterns, while highlighting the limitations of conventional offline treatment paradigms. Then, systematically a wide spectrum of digital technologies are reviewed. These include wearable and IoT devices, web-based intervention platforms, AI-powered DTx, virtual and augmented reality systems, conversational agents, telehealth services, community-based platforms, and gamified interventions. Building on this, the key functions enabled by these systems, such as digital screening, risk prediction, early relapse detection, behavior tracking, personalized intervention, and continuous monitoring through just-in-time adaptive interventions are categorized. The survey further examines the diverse data sources underpinning these systems, ranging from self-reported and physiological data to behavioral, contextual, and social interaction signals, with a particular focus on the challenges of multimodal data fusion. An in-depth analysis of AI and machine learning techniques is provided. It includes risk prediction models, personalized recommendation systems, reinforcement learning (RL) for behavior change, and natural language processing (NLP) for interaction and sentiment analysis, alongside critical discussions on explainability, bias, and generalization. In addition, ethical, legal, and societal considerations, as well as economic and ecosystem-level dimensions shaping the adoption of digital addiction solutions are explored. Finally, key challenges and outline future research directions are identified. These include AI-driven personalized health, generative AI-based conversational therapy, multimodal digital phenotyping, hybrid human-AI care models, proactive public health strategies, and integration within the broader digital mental health ecosystem. This book aims to provide researchers, clinicians, and practitioners with a unified, interdisciplinary perspective on the opportunities and limitations of AI-based digital technologies in behavioral addiction management,and to highlight pathways toward more scalable, personalized, and effective intervention strategies.