Clinical trial NCT06904079: Prediction and Intervention Effect of Rehabilitation Status for Severe Mental Disorder Patients Based on Multimodal Analysis and AI Agents
Official title: Prediction and Intervention Effect of Rehabilitation Status for Severe Mental Disorder Patients Based on Multimodal Analysis and AI Agents
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Severe Mental Disorder
Schizophrenia
Schizoaffective Disorder
Paranoid Psychosis
Bipolar Affective Disorder
Mental Disorders Due to Epilepsy
Mental Retardation Accompanied by Mental Disorders
Keywords: Severe Mental Disorder
Reinforcement Learning
Agent-based Modeling
Multimodal and Crossmodal AI framework
Digital rehabilitation
Lead sponsor: Shanghai Mental Health Center
Sponsor class: OTHER
Collaborators: name: Shanghai Jiao Tong University School of Medicine
First posted: 2025-04-01
Enrollment: 82
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief summary: Mental health issues represent a major public health and social problem that significantly impacts economic and social development. Compared to other diseases, mental disorders can impair various aspects of a patient' s life, including psychological, social, occupational, and educational functions, affecting their quality of life and daily living abilities. Particularly, severe mental disorders tend to have a chronic course, often resulting in diminished social functions and social withdrawal, making it difficult for patients to integrate into society. Repeated, systematic, and comprehensive rehabilitation training for patients with severe mental disorders can effectively control or delay disease recurrence, improve social functions, enhance quality of life, and facilitate patients' reintegration into society.

In recent years, the scope of mental disorder rehabilitation has expanded to include enhancing patients' social functions and promoting their integration into society. Vocational rehabilitation and social skills training are widely used in the rehabilitation treatment of patients with severe mental disorders, and some physical intervention methods, such as neurofeedback training, have also proven to be significantly effective in the rehabilitation process. However, traditional rehabilitation techniques often lack specificity and fail to meet individualized needs of patients. Additionally, the rehabilitation process lacks long-term monitoring, making it challenging to continuously assess and adjust patients' rehabilitation outcomes. Furthermore, the assessment of rehabilitation effectiveness mainly relies on patients' subjective feelings and clinical observations, lacking high-quality evidence. Therefore, there is an urgent need to introduce new rehabilitation technologies and scientifically evaluate their effectiveness to address the shortcomings of traditional methods and provide more personalized, precise, and effective rehabilitation support.

With the rise of digital health technologies, the field of mental health rehabilitation has encountered new opportunities. Compared to traditional therapies, digital health is revolutionizing the healthcare industry, moving away from traditional approaches to healthcare management to real-time personalized monitoring and therapeutic care.Technologies such as remote monitoring, virtual reality, and computer-assisted cognitive correction therapy are increasingly applied in rehabilitation. However, these methods still need improvements in data management and integration capabilities. A large amount of data accumulates in systems, recording only the training process and real-time effects of patients, without further evaluating their rehabilitation status, leading to resource waste. Therefore, there is an urgent need to develop a digital rehabilitation model that better meets the genuine needs of patients with severe mental disorders.

This study aims to integrate multimodal technology, reinforcement learning, and agent-based modeling (ABM) into the research of mental health rehabilitation to more accurately assess and predict the rehabilitation status of mental disorder patients and to more effectively guide and support decision-making in mental rehabilitation treatment.

Detailed description: This study aims to integrate multimodal technology, reinforcement learning(RL), and agent-based modeling (ABM) into the research of mental health rehabilitation to more accurately assess and predict the rehabilitation status of mental disorder patients and to more effectively guide and support decision-making in mental rehabilitation treatment.

This research project is divided into three main phases: theoretical and experimental phase, multimodal analysis phase, and application and optimization phase.

Firstly, we will conduct in-depth research across 20 community mental health facilities in Shanghai. This will be combined with an analysis of existing literature and studies to understand the needs of potential users, providing theoretical support and design basis for subsequent gamification interventions. This phase of user feedback, literature review, and needs assessment will offer directional guidance for the entire research.

In the second phase, based on the user needs and literature analysis results from the previous phase, we will design and implement gamification interventions, followed by a randomized controlled trial. Simultaneously, we will collect and analyze game behavior data to systematically evaluate the actual effects of the gamification interventions. This phase, focusing on intervention design and effect evaluation, is the core part of the research, and user feedback will continuously guide us in optimizing the interventions.

In the third phase, using the Multimodal and Crossmodal AI framework(MMCRAI), we will analyze multimodal data including patients' game behavior, physiological indicators, and psychological health information to better understand the key factors and dynamic changes in the rehabilitation process. This will provide training signals for the subsequent modeling and optimization phase.

Finally, combining agent-based models and reinforcement learning algorithms, we will simulate and predict the effects of gamification interventions in actual community settings, thus translating theoretical and experimental results into practical guidelines. In this phase, we will validate the effectiveness of the agent-based model through real-world application scenarios and discuss potential limitations and assumptions.

Overall, these four phases are interrelated and logically coherent, progressively deepening our understanding of the mental health rehabilitation process and forming a complete research framework, thereby laying a solid foundation for practical applications. Throughout the study, we will uphold ethical principles and privacy policies, ensuring that all research activities comply with regulations.

Eligibility criteria: Inclusion criteria:

* Registered in the Shanghai Mental Health Information Management System,

  * Diagnosed patients with one of the six severe mental disorders: schizophrenia, schizoaffective disorder, paranoid psychosis, bipolar (affective) disorder, mental disorder due to epilepsy, and mental retardation accompanied by mental disorder,

    * Aged between 18 and 65 years old, ④ Normal vision or hearing, or within the normal range after correction, ⑤ Patients or their families have provided informed consent for this study and signed the informed consent form.

Exclusion criteria:

Patients with severe physical illnesses or organic brain diseases.

Primary outcomes:
measure: BPRS reduction rate
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
Secondary outcomes:
measure: MMAS-8
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
measure: GAD-7
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
measure: PHQ-9
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
measure: WHOQOL-BREF
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
measure: SDSS
timeFrame: Baseline (pre-intervention), immediately post-intervention (3 months), 1-month follow-up (4 months), 3-month follow-up (6 months) and 6-month follow-up (9 months).
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai Mental Health Center
Study officials:
affiliation: Shanghai Mental Health Center
name: Jun Cai
role: PRINCIPAL_INVESTIGATOR
References: