Prediction and Intervention Effect of Rehabilitation Status for Severe Mental Disorder Patients Based on Multimodal Analysis and AI Agents

NCT06904079 | Recruiting DMC

• 1 other identifier: Jun Cai
• Study Type: interventional
• Target: 82
• Locations: 1 country
• Sites: 1

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.

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

Outcome Measures

Primary Outcomes (1)

• BPRS reduction rate

  The Brief Psychiatric Rating Scale (BPRS) was used to measure the presence and severity of psychiatric symptoms entailing positive symptoms, general psychopathology, and affective symptoms (e.g., thought disturbance, emotional withdrawal, hostility, and suspiciousness) for patients with mental illness, particularly schizophrenia. Each of the 18 items are designed to represent a discrete symptom area. Items are rated on a 7-point Likert scale, from 1 = 'not present' to 7 = 'extremely severe', with scores ranging from 18 to 126 (achieved through summing the item scores). Higher scores indicated more severity of psychiatric symptoms. Reduction rate was calculated using the following formula: reduction rate= (Score before treatment-Score after treatment)/(Score before treatment-18) × 100%. A reduction rate of BPRS score \> 25% was considered as a clinically meaningful improvement.

  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 (5)

• MMAS-8

  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).

• GAD-7

  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).

• PHQ-9

  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).

• WHOQOL-BREF

  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).

• SDSS

  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).

Study Arms (2)

Intervention Group (Gamified Digital Rehabilitation)

EXPERIMENTAL

Participants in this arm will receive routine pharmacological treatment and standard community rehabilitation services, combined with a structured, story-based gamified digital rehabilitation intervention. The interventions are digital functional games five times a week (30 minutes each) for 3 months.

Other: Gamified Digital Rehabilitation; Behavioral: Routine Care

Control Group (Routine Care)

ACTIVE COMPARATOR

Participants in this arm will receive routine pharmacological treatment and standard community rehabilitation services during the same period.

Behavioral: Routine Care

Interventions

Gamified Digital Rehabilitation OTHER

The gamification intervention measures are as follows: Currently, the initial version of the community mental health rehabilitation interactive game mainly focuses on six dimensions of medication management, including the importance of taking medication, identifying and dealing with adverse reactions to antipsychotic drugs, learning self-management of medication, assessing the effectiveness of medication treatment, long-term management of medication treatment, and discussing issues related to medication effects with medical staff. Next, we will continue to design a series of games themed on symptom management and psychological rehabilitation, and implement game-based digital rehabilitation interventions for patients in the intervention group based on these games.

Intervention Group (Gamified Digital Rehabilitation)

Routine Care BEHAVIORAL

Receive regular psychiatric medication treatment and regular community rehabilitation services, including regular follow-ups, rehabilitation guidance, and community education,etc.

Control Group (Routine Care); Intervention Group (Gamified Digital Rehabilitation)

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• Patients with severe physical illnesses or organic brain diseases.

Sponsors & Collaborators

• Shanghai Mental Health Center: lead
• Shanghai Jiao Tong University School of Medicine: collaborator

Study Sites (1)

Shanghai Mental Health Center

Shanghai, China

RECRUITING

MeSH Terms

Conditions

Mental Disorders; Schizophrenia; Psychotic Disorders; Paranoid Disorders; Bipolar Disorder

Condition Hierarchy (Ancestors)

Schizophrenia Spectrum and Other Psychotic Disorders; Bipolar and Related Disorders; Mood Disorders

Study Officials

• Jun Cai

  Shanghai Mental Health Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Weibo Zhang

CONTACT

86-13764694223; zhangweibo600@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: This randomized trial is an open trial, and the interventions involved cannot be blinded, so the trial is open to participants, observers, and outcome evaluators.
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 25, 2025
• First Posted: April 1, 2025
• Study Start: March 12, 2024
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): June 30, 2026
• Last Updated: August 17, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)