Severe Mental DisorderNCT06904079
Prediction and Intervention Effect of Rehabilitation Status for Severe Mental Disorder Patients Based on Multimodal Analysis and AI Agents
A study testing Gamified Digital Rehabilitation and Routine Care in people with Severe Mental Disorder and Schizophrenia, run by Shanghai Mental Health Center at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Severe Mental Disorder, Schizophrenia, Schizoaffective Disorder
- What is being tested?
- Gamified Digital Rehabilitation and Routine Care
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 82 participants
- Where
- 1 siteChina
- Study dates
- 12 Mar 2024to 30 Jun 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- Patients with severe physical illnesses or organic brain diseases.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Shanghai Mental Health Center
Shanghai
Jun Cai, 86-18017311058, caijun533@163.com
About this study
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.
Read more
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.
Read the 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.
- Conditions
- Severe Mental Disorder, Schizophrenia, Schizoaffective Disorder, Paranoid Psychosis, Bipolar Affective Disorder, Mental Disorders Due to Epilepsy
- Being tested
- Gamified Digital Rehabilitation, Routine Care
- Keywords
- Severe Mental Disorder, Reinforcement Learning, Agent-based Modeling, Multimodal and Crossmodal AI framework, Digital rehabilitation
- Medical subject headings
- Mental Disorders, Schizophrenia, Psychotic Disorders, Paranoid Disorders, Bipolar Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Gamified Digital RehabilitationOther
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.
Routine CareBehavioural
Receive regular psychiatric medication treatment and regular community rehabilitation services, including regular follow-ups, rehabilitation guidance, and community education,etc.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Behavioral: Routine Care |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
The 8-item Morisky medicationadherence scale is the most widely used medication compliance assessment scale, which has been applied to patients with multiple chronic diseases and has been translated into different languages.
Measured: 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
The Generalized Anxiety Disorder Screener (GAD-7) is a brief, 7-item self-report measure for anxiety symptoms in adolescents and adults. Although developed primarily as a screening tool for GAD, it can also be used as a diagnostic tool to monitor changes in symptoms over time, and as a screener for social anxiety, panic disorder, and post-traumatic stress disorder. Each item is rated on a likert-type scale from zero ('not at all') to three ('nearly every day'), with total scores ranging from 0 to 21.
Measured: 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
The Patient Health Questionnaire-9 (PHQ-9) is a multipurpose instrument for screening , diagnosing, monitoring and measuring the severity of depression. The tool rates the frequency of the symptoms which factors into the scoring severity index. The PHQ -9 is brief and useful in clinical practice. The PHQ -9 can be administered repeatedly , which can reflect improvement or worsening of depression in response to treatment .Each item is rated on a likert-type scale from zero ('not at all') to three ('nearly every day').
Measured: 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
The WHO Quality of Life Scale-Brief (WHOQOL-BREF) was developed by the Word Health Organisation as a quality of life assessment that would be applicable cross-culturally. It is a 26 item self report measure, especially useful for measuring outcomes with adults with a psychosocial disability. The WHOQOL-BREF measures four factors: physical health, psychological Health, social relationships, and environment. The total score is presented between 26 and 156, where higher scores represent higher levels of quality of life.
Measured: 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
The 10-item social disability screening schedule (SDSS) was used for measuring social function. It is a shortened version of psychiatric disability assessment schedule (DAS). SDSS was used to evaluate social disability, with 10 items in total, 0~2 points for each item.
Measured: 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).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2025
- Study start12 Mar 2024
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Severe Mental DisorderStudies of the same condition, open and under way.
- More trials run by Shanghai Mental Health CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 82 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Mental Health Center
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Jiao Tong University School of Medicine
- Responsible party
- Sponsor
- Investigators
- Jun Cai, Principal investigator, Shanghai Mental Health Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06904079
- Sponsor's protocol ID
- Jun Cai
- Status last verified
- Aug 2025
- First submitted
- 25 Mar 2025
- Last updated
- 17 Aug 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction and Intervention Effect of Rehabilitation Status for Severe Mental Disorder Patients Based on Multimodal Analysis and AI Agents
- ClinicalTrials.gov ID
- NCT06904079
- Acronym / sponsor ID
- Jun Cai
- Conditions
- Severe Mental Disorder, Schizophrenia, Schizoaffective Disorder, Paranoid Psychosis, Bipolar Affective Disorder, Mental Disorders Due to Epilepsy
- Keywords
- Severe Mental Disorder, Reinforcement Learning, Agent-based Modeling, Multimodal and Crossmodal AI framework, Digital rehabilitation
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 82 participants (estimated)
- Lead sponsor
- Shanghai Mental Health CenterUniversity, hospital or other
- Collaborators
- Shanghai Jiao Tong University School of Medicine
- Study start
- 12 Mar 2024 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 1 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention Group (Gamified Digital Rehabilitation) | Experimental | Other: Gamified Digital Rehabilitation, Behavioral: Routine CareParticipants 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. |
| Control Group (Routine Care) | Active comparator | Behavioral: Routine CareParticipants in this arm will receive routine pharmacological treatment and standard community rehabilitation services during the same period. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Registered in the Shanghai Mental Health Information Management System,
Key exclusion criteria (1 listed)
- Patients with severe physical illnesses or organic brain diseases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Gamified Digital RehabilitationThe 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. | Other | Intervention Group (Gamified Digital Rehabilitation) |
| Routine CareReceive regular psychiatric medication treatment and regular community rehabilitation services, including regular follow-ups, rehabilitation guidance, and community education,etc. | Behavioural | Control Group (Routine Care), Intervention Group (Gamified Digital Rehabilitation) |
Primary outcome
BPRS reduction rate
Time frame: 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).
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.
5 secondary outcomes
MMAS-8
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jun Cai principal investigator
Shanghai Mental Health Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Gamified Digital Rehabilitation and Routine Care in people with Severe Mental Disorder and Schizophrenia, run by Shanghai Mental Health Center at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Weibo Zhang (86-13764694223, zhangweibo600@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
BPRS reduction rate (time frame: 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).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Aug 2025.