Neurodegenerative DiseaseRAISE_2023_026NCT07566728
ADAI - Home Care in Intelligent Environments Clinical Care Ecosystem for the Management of Home Digital Therapies Through the Use of Assistive Robots and Clinical Predictive Systems Based on Artificial Intelligence Systems
A study testing CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE in people with Neurodegenerative Disease and Artificial Intelligence (AI), run by IRCCS Centro Neurolesi Bonino Pulejo at 1 site in Italy.
Trial summary
Dementia is a neurocognitive disorder that causes a deterioration in cognitive function, significantly impacting social and work abilities and daily activities. Alzheimer's disease is diagnosed when cognitive decline affects at least two cognitive domains, one of which must involve memory. Read more about this study
- Who is it for?
- Ages 40 to 80 years
- Conditions the study is for
- Neurodegenerative Disease, Artificial Intelligence (AI), Mild Cognitive Impairment (MCI)
- What is being tested?
- CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE
- Type of treatment
- Pill or oral medicine, Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 23 participants
- Where
- 1 siteItaly
- Study dates
- 22 Sep 2025to 31 Oct 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 40 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age between 40 and 80
- Clinical Rating Scale (CDR) score < 1
- Patients with moderate to mild cognitive impairment
Who cannot join
- Subjects with marked visual and hearing impairments that prevent proper understanding of the trial
- Patients with impaired language comprehension
- Patients with comorbid psychiatric disorders
- Lack of consent to participate by signing the informed consent form
Trial locations and contacts
1 site in 1 country.
About this study
Dementia is a neurocognitive disorder that causes a deterioration in cognitive function, significantly impacting social and work abilities and daily activities. Alzheimer's disease is diagnosed when cognitive decline affects at least two cognitive domains, one of which must involve memory. Mild Cognitive Impairment (MCI) is a critical diagnosis as it represents a potentially early stage of cognitive decline. In the DSM-5, MCI is defined as a "minor neurocognitive disorder," characterized by functional decline affecting at least one of six cognitive domains: memory and learning, language, visuospatial function, attention, executive function, and social functioning. It is important to emphasize that this decline is not severe enough to significantly impair the patient's daily activities. In this context, support for people with MCI and dementia is crucial, not only at the family and social level, but also through the adoption of innovative technological solutions. Artificial intelligence (AI) is emerging as a valuable tool for early diagnosis, and through machine learning processes, it is possible to predict cognitive decline, thus providing personalized treatment and day-to-day patient management. This allows for intervention at a less advanced stage of the disease, thus slowing its progression, while maintaining autonomy and independence for as long as possible, which tends to decline over time in this patient population. Investing in innovative technologies is therefore essential not only to improve prevention and treatment opportunities but also to provide concrete support to caregivers, especially at a time when the aging population requires an increasingly structured and effective global response.
The objectives of the study are as follows:
- The objective of this study is to evaluate the effectiveness of software in administering cognitive and motor tests via a humanoid robot in patients with early-stage Alzheimer's disease (AD) or other forms of mild to moderate dementia.
- Support medical professionals in personalizing therapeutic treatments, using predictive models based on advanced artificial intelligence systems. These models will begin by collecting, monitoring, and processing demographic and clinical data and the results of cognitive and motor assessments obtained from patients to predict the course of the disease and the effectiveness of rehabilitation treatments. This will then allow them to suggest personalized treatment options and optimize care pathways, thus improving overall clinical outcomes.
Read the detailed description
Artificial intelligence (AI), particularly through machine learning techniques, offers promising opportunities in this field. By analyzing large volumes of clinical, behavioral, and demographic data, AI systems can detect patterns associated with early cognitive decline and predict disease progression. This predictive capability enables healthcare professionals to intervene earlier, when therapeutic strategies are more likely to be effective, thereby slowing the progression of the disease and prolonging the patient's independence and quality of life.
The present study aims to explore the integration of advanced technological tools into clinical practice, with a specific focus on the use of humanoid robotic systems. These systems are designed to administer standardized cognitive and motor assessments in a consistent and engaging manner, particularly for patients in the early stages of Alzheimer's disease or other forms of mild to moderate dementia. The use of a humanoid robot may enhance patient engagement, reduce variability in test administration, and allow for more precise and objective data collection.
In addition, the study seeks to support clinicians in tailoring therapeutic interventions through the use of predictive models powered by artificial intelligence. These models will be developed using comprehensive datasets that include patient demographics, medical history, and results from repeated cognitive and motor evaluations. By continuously collecting and analyzing this information, the system will be able to identify trends, estimate disease trajectories, and evaluate the effectiveness of different rehabilitation strategies.
Ultimately, the integration of AI-driven predictive analytics with robotic-assisted assessment tools aims to provide a more personalized and adaptive approach to patient care. This approach has the potential to optimize treatment plans, improve clinical outcomes, and enhance the overall efficiency of healthcare delivery. Furthermore, it offers valuable support to caregivers by providing actionable insights and facilitating more structured care pathways.
As populations continue to age globally, the demand for innovative, scalable, and effective solutions in the management of cognitive disorders is rapidly increasing. Investing in advanced technologies such as artificial intelligence and robotics is therefore crucial not only for improving early diagnosis and therapeutic interventions but also for addressing the broader societal challenges associated with dementia care.
- Being tested
- CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE
- Keywords
- DEMENTIA, MILD COGNITIVE IMPAIRMENT, ARTIFICIAL INTELLIGENCE, DIGITAL THERAPEUTICS
- Medical subject headings
- Neurodegenerative Diseases, Cognitive Dysfunction, Dementia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CEDevice
The proposed study is an interventional study that aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood.
The data will be recorded manually via tablet by the physician. After data collection, patients will undergo
| Study group | Role | What they receive |
|---|---|---|
| early-stage AD or other forms of mild to moderate dementia who interact with the robot | Experimental | The study aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood.
The data will be recorded manually via tablet by the physician. After data collection, patients will undergo neuropsychological and motor tests. Receives: Device: CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mini Mental State Examination (MMSE) total score
The MMSE will be administered through a humanoid robot interface. The total score (range 0-30) will be recorded, and mean scores and/or change from baseline will be analyzed.
The aim of this study is therefore to evaluate the effectiveness of the software in administering MMSE via a humanoid robot in patients with early-stage Alzheimer's dementia (AD) or other forms of mild to moderate dementia.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 May 2026
- Study start22 Sep 2025
- Primary completion15 Oct 2025
- Study completion31 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Neurodegenerative DiseaseStudies of the same condition, open and under way.
- 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
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- The study aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood. The data will be recorded manually via tablet by the physician. After data collection, patients will undergo neuropsychological and motor tests. Specifically, te
- Masking
- None (open label)
- Enrolment
- 23 participants (actual)
Sponsor and investigators
- Lead sponsor
- IRCCS Centro Neurolesi Bonino Pulejo
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Caterina Formica, IRCCS Centro Neurolesi Bonino Pulejo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07566728
- Sponsor's protocol ID
- ADAI
- Other IDs
- AGILE srl
- Acronym
- RAISE_2023_026
- Status last verified
- Apr 2026
- First submitted
- 26 Mar 2026
- Last updated
- 5 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant data set and data dictionaries
Study at a glance
From the registry record, last updated by the sponsor on 5 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ADAI - Home Care in Intelligent Environments Clinical Care Ecosystem for the Management of Home Digital Therapies Through the Use of Assistive Robots and Clinical Predictive Systems Based on Artificial Intelligence Systems
- ClinicalTrials.gov ID
- NCT07566728
- Acronym / sponsor ID
- RAISE_2023_026 / ADAI
- Keywords
- DEMENTIA, MILD COGNITIVE IMPAIRMENT, ARTIFICIAL INTELLIGENCE, DIGITAL THERAPEUTICS
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 23 participants (actual)
- Lead sponsor
- IRCCS Centro Neurolesi Bonino PulejoUniversity, hospital or other
- Study start
- 22 Sep 2025 (actual)
- Primary completion
- 15 Oct 2025 (actual)
- Study completion
- 31 Oct 2025 (actual)
- First posted
- 5 May 2026 (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.
Dementia is a neurocognitive disorder that causes a deterioration in cognitive function, significantly impacting social and work abilities and daily activities. Alzheimer's disease is diagnosed when cognitive decline affects at least two cognitive domains, one of which must involve memory. Mild Cognitive Impairment (MCI) is a critical diagnosis as it represents a potentially early stage of cognitive decline. In the DSM-5, MCI is defined as a "minor neurocognitive disorder," characterized by functional decline affecting at least one of six cognitive domains: memory and learning, language, visuospatial function, attention, executive function, and social functioning. It is important to emphasize that this decline is not severe enough to significantly impair the patient's daily activities. In this context, support for people with MCI and dementia is crucial, not only at the family and social level, but also through the adoption of innovative technological solutions. Artificial intelligence (AI) is emerging as a valuable tool for early diagnosis, and through machine learning processes, it is possible to predict cognitive decline, thus providing personalized treatment and day-to-day patient management. This allows for intervention at a less advanced stage of the disease, thus slowing its progression, while maintaining autonomy and independence for as long as possible, which tends to decline over time in this patient population. Investing in innovative technologies is therefore essential not only to improve prevention and treatment opportunities but also to provide concrete support to caregivers, especially at a time when the aging population requires an increasingly structured and effective global response.
The objectives of the study are as follows:
- The objective of this study is to evaluate the effectiveness of software in administering cognitive and motor tests via a humanoid robot in patients with early-stage Alzheimer's disease (AD) or other forms of mild to moderate dementia.
- Support medical professionals in personalizing therapeutic treatments, using predictive models based on advanced artificial intelligence systems. These models will begin by collecting, monitoring, and processing demographic and clinical data and the results of cognitive and motor assessments obtained from patients to predict the course of the disease and the effectiveness of rehabilitation treatments. This will then allow them to suggest personalized treatment options and optimize care pathways, thus improving overall clinical outcomes.
Detailed description
Artificial intelligence (AI), particularly through machine learning techniques, offers promising opportunities in this field. By analyzing large volumes of clinical, behavioral, and demographic data, AI systems can detect patterns associated with early cognitive decline and predict disease progression. This predictive capability enables healthcare professionals to intervene earlier, when therapeutic strategies are more likely to be effective, thereby slowing the progression of the disease and prolonging the patient's independence and quality of life.
The present study aims to explore the integration of advanced technological tools into clinical practice, with a specific focus on the use of humanoid robotic systems. These systems are designed to administer standardized cognitive and motor assessments in a consistent and engaging manner, particularly for patients in the early stages of Alzheimer's disease or other forms of mild to moderate dementia. The use of a humanoid robot may enhance patient engagement, reduce variability in test administration, and allow for more precise and objective data collection.
In addition, the study seeks to support clinicians in tailoring therapeutic interventions through the use of predictive models powered by artificial intelligence. These models will be developed using comprehensive datasets that include patient demographics, medical history, and results from repeated cognitive and motor evaluations. By continuously collecting and analyzing this information, the system will be able to identify trends, estimate disease trajectories, and evaluate the effectiveness of different rehabilitation strategies.
Ultimately, the integration of AI-driven predictive analytics with robotic-assisted assessment tools aims to provide a more personalized and adaptive approach to patient care. This approach has the potential to optimize treatment plans, improve clinical outcomes, and enhance the overall efficiency of healthcare delivery. Furthermore, it offers valuable support to caregivers by providing actionable insights and facilitating more structured care pathways.
As populations continue to age globally, the demand for innovative, scalable, and effective solutions in the management of cognitive disorders is rapidly increasing. Investing in advanced technologies such as artificial intelligence and robotics is therefore crucial not only for improving early diagnosis and therapeutic interventions but also for addressing the broader societal challenges associated with dementia care.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- The study aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood. The data will be recorded manually via tablet by the physician. After data collection, patients will undergo neuropsychological and motor tests. Specifically, te
| Arm | Type | Receives |
|---|---|---|
| early-stage AD or other forms of mild to moderate dementia who interact with the robot | Experimental | Device: CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CEThe study aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood. The data will be recorded manually via tablet by the physician. After data collection, patients will undergo neuropsychological and motor tests. |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Age between 40 and 80
- Clinical Rating Scale (CDR) score < 1
- Patients with moderate to mild cognitive impairment
Key exclusion criteria (4 listed)
- Subjects with marked visual and hearing impairments that prevent proper understanding of the trial
- Patients with impaired language comprehension
- Patients with comorbid psychiatric disorders
- Lack of consent to participate by signing the informed consent form
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 |
|---|---|---|
| CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CEThe proposed study is an interventional study that aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood. The data will be recorded manually via tablet by the physician. After data collection, patients will undergo | Device | early-stage AD or other forms of mild to moderate dementia who interact with the robot |
Primary outcome
Mini Mental State Examination (MMSE) total score
Time frame: Through study completion, an average of 1 year
The MMSE will be administered through a humanoid robot interface. The total score (range 0-30) will be recorded, and mean scores and/or change from baseline will be analyzed. The aim of this study is therefore to evaluate the effectiveness of the software in administering MMSE via a humanoid robot in patients with early-stage Alzheimer's dementia (AD) or other forms of mild to moderate dementia.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 May 2026.
- Individual participant dataSharing planned: Yes. Individual participant data set and data dictionaries
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE in people with Neurodegenerative Disease and Artificial Intelligence (AI), run by IRCCS Centro Neurolesi Bonino Pulejo at 1 site in Italy.
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 4 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: finished, as of the sponsor's last update on 5 May 2026. 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 Italy. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mini Mental State Examination (MMSE) total score (time frame: Through study completion, an average of 1 year). 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 5 May 2026.