Parkinson DiseaseNCT07058714
Multimodal Artificial Intelligence Based Fall Risk Prediction in Parkinson's Disease
An observational study of Parkinson Disease, run by Biruni University at 1 site in Turkey (Türkiye).
Trial summary
Parkinson's disease (PD) is characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, often leading to gait disturbances and a high risk of falls. Read more about this study
- Who is it for?
- Ages 40 to 75 years
- Conditions the study is for
- Parkinson Disease
- How many people
- 30 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jul 2025to 15 Sep 2025
- Phase
- Observational
- 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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study was designed as a cross-sectional study. Patients diagnosed with Idiopathic Parkinson's Disease who applied to the Neurology clinic of Bakırköy Prof. Dr. Mazhar Osman Training and Research Hospital and were referred from there will be included in the study.
Who may be able to join
- Clinical diagnosis of idiopathic Parkinson's disease
- Hoehn and Yahr stage between 1 and 3
- A score of at least 21 on the Montreal Cognitive Assessment (MoCA)
- Stable medication regimen during the past month
- Assessment conducted during the patient's "on" period
- Ability to walk independently on a flat surface (Functional Ambulation Classification ≥ 3)
Who cannot join
- Severe hearing or visual impairments
- Presence of other neurological, cardiovascular, or orthopedic conditions affecting gait
- Diagnosis of any other neurological disorder (e.g., dementia, cerebrovascular disease)
- Less than 5 years of formal education
- Presence of vascular pathology in the lower extremities
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Biruni University
Istanbul, Zeytinburnu, 34752
About this study
Official title: Multimodal Artificial Intelligence-Based Fall Risk Prediction in Patients With Parkinson's Disease: Single vs. Dual-Task Conditions
Parkinson's disease (PD) is characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, often leading to gait disturbances and a high risk of falls. Dual-task walking assessments-requiring simultaneous motor and cognitive engagement-have gained importance in evaluating real-life mobility impairments in PD, as they more accurately reflect challenges faced during daily activities. While clinical tools such as the Timed Up and Go (TUG), Four Square Step Test (FSST), and Mini-BESTest are widely used, their in-person application may not always be feasible for individuals with mobility or access limitations. Telehealth-based assessment methods, therefore, offer practical alternatives. Recently, the integration of artificial intelligence (AI), particularly machine learning (ML), into clinical assessments has opened new possibilities for fall risk prediction by enabling the simultaneous analysis of motor, cognitive, and balance-related parameters. This study aims to predict fall risk in individuals with PD using AI-based models that incorporate multiple data sources. Furthermore, it compares the predictive accuracy of models derived from single-task and dual-task conditions, with the goal of developing a more precise and clinically useful decision-support tool for early intervention.
Read the detailed description
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects the basal ganglia, particularly the substantia nigra, leading to hallmark motor symptoms such as bradykinesia, resting tremor, muscular rigidity, and impaired postural reflexes. These motor impairments often result in gait disturbances, postural instability, and ultimately, a significantly increased risk of falls. Fall-related injuries are a major source of morbidity, reduced mobility, and increased healthcare burden in individuals with PD, making early identification of fall risk a clinical priority.
Traditional balance and gait assessments, such as the Timed Up and Go (TUG) test, the Four Square Step Test (FSST), and the Mini-Balance Evaluation Systems Test (Mini-BESTest), have been widely employed to evaluate static and dynamic balance components in clinical settings. However, these assessments are often conducted under single-task conditions, which may not fully capture the complex, real-life demands placed on individuals with PD. In contrast, dual-task paradigms-where individuals perform a cognitive or motor secondary task while walking-have demonstrated greater sensitivity in detecting subtle deficits in postural control, as they mimic everyday situations more closely.
Nevertheless, the practical implementation of such assessments is often hindered by logistical constraints, particularly among individuals with limited mobility or geographic access to healthcare facilities. In this context, telehealth-based assessment strategies are gaining momentum due to their ability to facilitate remote monitoring and evaluation with minimal equipment and reduced resource requirements.
Recent advancements in artificial intelligence (AI), especially machine learning (ML) techniques, offer promising solutions for enhancing the predictive power of clinical assessments. ML algorithms can integrate and analyze complex datasets encompassing motor, cognitive, and balance-related parameters without relying on predefined statistical assumptions. These models are capable of identifying nonlinear relationships and subtle patterns within the data, thereby enabling more individualized and accurate fall risk predictions.
The primary objective of this study is to develop and validate AI-based predictive models for fall risk estimation in individuals with Parkinson's disease by incorporating multimodal data obtained from both single-task and dual-task walking assessments. Additionally, the study aims to compare the predictive performance of models derived under these two conditions to determine whether dual-task data enhance the sensitivity and specificity of fall risk classification. Through this approach, the research seeks to establish a clinically relevant, remote-friendly, and data-driven decision-support tool to inform timely interventions and personalized rehabilitation strategies.
- Conditions
- Parkinson Disease
- Keywords
- fall risk, dual-task, machine learning, artificial intelligence, balance evaluation, Parkinson's Disease
- Medical subject headings
- Parkinson Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
Mini-Balance Evaluation Systems Test (Mini-BESTest)
The Mini-BESTest is a 14-item balance assessment tool designed to evaluate dynamic balance, including postural responses, sensory orientation, and dynamic gait. The final section allows for assessment of dual-task performance within the context of a mobility test involving cognitive load. Each item is scored on a scale from 0 to 2, where 0 indicates inability to complete the task and 2 indicates normal performance. The maximum total score is 28. It is a unidimensional measure that takes approximately 15 minutes to complete and is considered valid and reliable for use in individuals with Parkinson's disease.
Measured: Mini-BESTest will be administered once during a single assessment session, which is expected to last approximately 10-15 minutes.
Four Square Step Test (FSST)
This test evaluates the ability to step over obstacles in multiple directions. At the start, the participant stands in the top left square (Square 1) and faces Square 2. The stepping sequence begins clockwise through Squares 2, 4, and 3, and then continues counterclockwise through Squares 3, 4, 2, and back to 1. The clinician demonstrates the sequence, and the participant is allowed to practice. If the participant fails to complete the sequence correctly, loses balance, or touches the aid, the test is repeated. Two trials are performed, and the best time is recorded. Timing begins when the leading foot contacts Square 2 and ends when the trailing foot returns to Square 1.
During this test, participants' stepping and changing direction movements will be recorded on video.
Measured: will be administered once during a single assessment session, which is expected to last approximately 10 minutes.
Secondary outcomes (6)
Digit Span Test
This test consists of two components: forward and backward digit span. Each section begins with short sequences of numbers that gradually increase in length. Numbers are read aloud at a consistent pace, and the participant repeats them in order. If the participant fails two consecutive trials in either direction, the test is discontinued. The total number of correctly repeated digits in the last successful trial is recorded as the span score.
Measured: The Digit Span Test will be administered once during a single session and is expected to take approximately 5 minutes to complete.
Verbal fluency task
This task measures phonemic verbal fluency. Participants are instructed to generate as many words as possible beginning with specific letters (e.g., F, A, S) within one minute per letter. Proper nouns and derivatives are excluded. The total score is the sum of all valid words generated across the letters.
Measured: The phonemic verbal fluency task will be administered once during a single session and is expected to take approximately 3 minutes (1 minute per letter).
Mental Flexibility Task
Mental flexibility is evaluated through a serial subtraction task involving backward counting from 100 in sevens, stopping at 65. For individuals with limited education who are unable to perform this task, an alternative version involving reciting the days of the week in reverse order is used. One point is awarded for each correct response in both formats.
Measured: The mental flexibility task will be performed once in a single session, taking approximately 2-3 minutes depending on the participant's cognitive status
Dual-Task Questionnaire
This 10-item questionnaire assesses the frequency of difficulties experienced during daily dual-task activities. Participants respond on a 5-point scale ranging from "never" to "very often" or "not applicable." The final score is calculated by summing the individual item scores and dividing by ten, yielding an average difficulty score.
Measured: The dual-task questionnaire will be completed once during the assessment session and will require approximately 3-5 minutes to complete.
Gait parameters
Gait parameters are evaluated using Kinovea® motion analysis software. The system provides quantitative analysis of spatiotemporal and kinematic aspects of walking. A camera positioned in the sagittal plane records a 3-meter walk, with colored markers placed on both heels to facilitate tracking. Parameters such as step length and stride length are analyzed based on heel strike sequences extracted from the recorded video.
Measured: Video-based gait assessment will be conducted once per participant during a single session. The walking task and data recording are expected to take approximately 5-7 minutes, including marker placement and calibration.
Timed Up and Go Test (TUG)
This simple and widely used test assesses mobility, balance, and fall risk. The participant is instructed to rise from a standard chair, walk three meters, turn around, return to the chair, and sit down. The total time to complete the sequence is recorded using a stopwatch. TUG is performed under both single-task and dual-task conditions. Cognitive dual-tasks include counting backwards by threes or reciting the days of the week in reverse. Motor dual-tasks include passing a ball between hands or carrying a glass of water. Dual-task cost will be calculated using the following formula:
(Single-task time - Dual-task time) / Single-task time × 100
Measured: The TUG test will be conducted under both single- and dual-task conditions in a single session. The total testing time, including all conditions, is estimated at approximately 7-10 minutes.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jul 2025
- Study start1 Jul 2025
- Primary completion15 Jul 2025
- Study completion15 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Parkinson 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
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 30 participants (actual)
Sponsor and investigators
- Lead sponsor
- Biruni University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Guzin Kaya Aytutuldu, Biruni University
- Investigators
- Guzin Kaya Aytutuldu, Principal investigator, Biruni University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07058714
- Sponsor's protocol ID
- BiruniUniverc
- Status last verified
- Sep 2025
- First submitted
- 1 Jul 2025
- Last updated
- 19 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Boddy A, Mitchell K, Ellison J, Brewer W, Perry LA. Reliability and validity of modified Four Square Step Test (mFSST) performance in individuals with Parkinson's disease. Physiother Theory Pract. 2023 May;39(5):1038-1043. doi: 10.1080/09593985.2022.2031360. Epub 2022 Jan 29.
PubMed 35098864Caronni A, Amadei M, Diana L, Sangalli G, Scarano S, Perucca L, Rota V, Bolognini N. In Parkinson's disease, dual-tasking reduces gait smoothness during the straight-walking and turning-while-walking phases of the Timed Up and Go test. BMC Sports Sci Med Rehabil. 2025 Mar 7;17(1):42. doi: 10.1186/s13102-025-01068-8.
PubMed 40055732Chen IC, Chuang IC, Chang KC, Chang CH, Wu CY. Dual task measures in older adults with and without cognitive impairment: response to simultaneous cognitive-exercise training and minimal clinically important difference estimates. BMC Geriatr. 2023 Oct 16;23(1):663. doi: 10.1186/s12877-023-04390-3.
PubMed 37845603Dite W, Temple VA. A clinical test of stepping and change of direction to identify multiple falling older adults. Arch Phys Med Rehabil. 2002 Nov;83(11):1566-71. doi: 10.1053/apmr.2002.35469.
PubMed 12422327Dou J, Wang J, Gao X, Wang G, Bai Y, Liang Y, Yang K, Yang Y, Zhang L. Effectiveness of Telemedicine Interventions on Motor and Nonmotor Outcomes in Parkinson Disease: Systematic Review and Network Meta-Analysis. J Med Internet Res. 2025 Jun 3;27:e71169. doi: 10.2196/71169.
PubMed 40460428Silva-Batista C, de Almeida FO, Wilhelm JL, Horak FB, Mancini M, King LA. Telerehabilitation by Videoconferencing for Balance and Gait in People with Parkinson's Disease: A Scoping Review. Geriatrics (Basel). 2024 May 23;9(3):66. doi: 10.3390/geriatrics9030066.
PubMed 38920422
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Artificial Intelligence-Based Fall Risk Prediction in Patients With Parkinson's Disease: Single vs. Dual-Task Conditions
- ClinicalTrials.gov ID
- NCT07058714
- Acronym / sponsor ID
- BiruniUniverc
- Conditions
- Parkinson Disease
- Keywords
- fall risk, dual-task, machine learning, artificial intelligence, balance evaluation, Parkinson's Disease
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 30 participants (actual)
- Lead sponsor
- Biruni UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 15 Jul 2025 (actual)
- Study completion
- 15 Sep 2025 (actual)
- First posted
- 10 Jul 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.
Parkinson's disease (PD) is characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, often leading to gait disturbances and a high risk of falls. Dual-task walking assessments-requiring simultaneous motor and cognitive engagement-have gained importance in evaluating real-life mobility impairments in PD, as they more accurately reflect challenges faced during daily activities. While clinical tools such as the Timed Up and Go (TUG), Four Square Step Test (FSST), and Mini-BESTest are widely used, their in-person application may not always be feasible for individuals with mobility or access limitations. Telehealth-based assessment methods, therefore, offer practical alternatives. Recently, the integration of artificial intelligence (AI), particularly machine learning (ML), into clinical assessments has opened new possibilities for fall risk prediction by enabling the simultaneous analysis of motor, cognitive, and balance-related parameters. This study aims to predict fall risk in individuals with PD using AI-based models that incorporate multiple data sources. Furthermore, it compares the predictive accuracy of models derived from single-task and dual-task conditions, with the goal of developing a more precise and clinically useful decision-support tool for early intervention.
Detailed description
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects the basal ganglia, particularly the substantia nigra, leading to hallmark motor symptoms such as bradykinesia, resting tremor, muscular rigidity, and impaired postural reflexes. These motor impairments often result in gait disturbances, postural instability, and ultimately, a significantly increased risk of falls. Fall-related injuries are a major source of morbidity, reduced mobility, and increased healthcare burden in individuals with PD, making early identification of fall risk a clinical priority.
Traditional balance and gait assessments, such as the Timed Up and Go (TUG) test, the Four Square Step Test (FSST), and the Mini-Balance Evaluation Systems Test (Mini-BESTest), have been widely employed to evaluate static and dynamic balance components in clinical settings. However, these assessments are often conducted under single-task conditions, which may not fully capture the complex, real-life demands placed on individuals with PD. In contrast, dual-task paradigms-where individuals perform a cognitive or motor secondary task while walking-have demonstrated greater sensitivity in detecting subtle deficits in postural control, as they mimic everyday situations more closely.
Nevertheless, the practical implementation of such assessments is often hindered by logistical constraints, particularly among individuals with limited mobility or geographic access to healthcare facilities. In this context, telehealth-based assessment strategies are gaining momentum due to their ability to facilitate remote monitoring and evaluation with minimal equipment and reduced resource requirements.
Recent advancements in artificial intelligence (AI), especially machine learning (ML) techniques, offer promising solutions for enhancing the predictive power of clinical assessments. ML algorithms can integrate and analyze complex datasets encompassing motor, cognitive, and balance-related parameters without relying on predefined statistical assumptions. These models are capable of identifying nonlinear relationships and subtle patterns within the data, thereby enabling more individualized and accurate fall risk predictions.
The primary objective of this study is to develop and validate AI-based predictive models for fall risk estimation in individuals with Parkinson's disease by incorporating multimodal data obtained from both single-task and dual-task walking assessments. Additionally, the study aims to compare the predictive performance of models derived under these two conditions to determine whether dual-task data enhance the sensitivity and specificity of fall risk classification. Through this approach, the research seeks to establish a clinically relevant, remote-friendly, and data-driven decision-support tool to inform timely interventions and personalized rehabilitation strategies.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 40 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study was designed as a cross-sectional study. Patients diagnosed with Idiopathic Parkinson's Disease who applied to the Neurology clinic of Bakırköy Prof. Dr. Mazhar Osman Training and Research Hospital and were referred from there will be included in the study.
Key inclusion criteria (6 listed)
- Clinical diagnosis of idiopathic Parkinson's disease
- Hoehn and Yahr stage between 1 and 3
- A score of at least 21 on the Montreal Cognitive Assessment (MoCA)
- Stable medication regimen during the past month
- Assessment conducted during the patient's "on" period
Key exclusion criteria (5 listed)
- Severe hearing or visual impairments
- Presence of other neurological, cardiovascular, or orthopedic conditions affecting gait
- Diagnosis of any other neurological disorder (e.g., dementia, cerebrovascular disease)
- Less than 5 years of formal education
- Presence of vascular pathology in the lower extremities
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Mini-Balance Evaluation Systems Test (Mini-BESTest)
Time frame: Mini-BESTest will be administered once during a single assessment session, which is expected to last approximately 10-15 minutes.
The Mini-BESTest is a 14-item balance assessment tool designed to evaluate dynamic balance, including postural responses, sensory orientation, and dynamic gait. The final section allows for assessment of dual-task performance within the context of a mobility test involving cognitive load. Each item is scored on a scale from 0 to 2, where 0 indicates inability to complete the task and 2 indicates normal performance. The maximum total score is 28. It is a unidimensional measure that takes approximately 15 minutes to complete and is considered valid and reliable for use in individuals with Parkinson's disease.
Four Square Step Test (FSST)
Time frame: will be administered once during a single assessment session, which is expected to last approximately 10 minutes.
This test evaluates the ability to step over obstacles in multiple directions. At the start, the participant stands in the top left square (Square 1) and faces Square 2. The stepping sequence begins clockwise through Squares 2, 4, and 3, and then continues counterclockwise through Squares 3, 4, 2, and back to 1. The clinician demonstrates the sequence, and the participant is allowed to practice. If the participant fails to complete the sequence correctly, loses balance, or touches the aid, the test is repeated. Two trials are performed, and the best time is recorded. Timing begins when the leading foot contacts Square 2 and ends when the trailing foot returns to Square 1. During this test, participants' stepping and changing direction movements will be recorded on video.
6 secondary outcomes
Digit Span Test
Time frame: The Digit Span Test will be administered once during a single session and is expected to take approximately 5 minutes to complete.
Verbal fluency task
Time frame: The phonemic verbal fluency task will be administered once during a single session and is expected to take approximately 3 minutes (1 minute per letter).
Mental Flexibility Task
Time frame: The mental flexibility task will be performed once in a single session, taking approximately 2-3 minutes depending on the participant's cognitive status
Dual-Task Questionnaire
Time frame: The dual-task questionnaire will be completed once during the assessment session and will require approximately 3-5 minutes to complete.
Gait parameters
Time frame: Video-based gait assessment will be conducted once per participant during a single session. The walking task and data recording are expected to take approximately 5-7 minutes, including marker placement and calibration.
Timed Up and Go Test (TUG)
Time frame: The TUG test will be conducted under both single- and dual-task conditions in a single session. The total testing time, including all conditions, is estimated at approximately 7-10 minutes.
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 19 Sep 2025.
- Background: PubMed 35098864Boddy A, Mitchell K, Ellison J, Brewer W, Perry LA. Reliability and validity of modified Four Square Step Test (mFSST) performance in individuals with Parkinson's disease. Physiother Theory Pract. 2023 May;39(5):1038-1043. doi: 10.1080/09593985.2022.2031360. Epub 2022 Jan 29.
- Background: PubMed 40055732Caronni A, Amadei M, Diana L, Sangalli G, Scarano S, Perucca L, Rota V, Bolognini N. In Parkinson's disease, dual-tasking reduces gait smoothness during the straight-walking and turning-while-walking phases of the Timed Up and Go test. BMC Sports Sci Med Rehabil. 2025 Mar 7;17(1):42. doi: 10.1186/s13102-025-01068-8.
- Background: PubMed 37845603Chen IC, Chuang IC, Chang KC, Chang CH, Wu CY. Dual task measures in older adults with and without cognitive impairment: response to simultaneous cognitive-exercise training and minimal clinically important difference estimates. BMC Geriatr. 2023 Oct 16;23(1):663. doi: 10.1186/s12877-023-04390-3.
- Background: PubMed 12422327Dite W, Temple VA. A clinical test of stepping and change of direction to identify multiple falling older adults. Arch Phys Med Rehabil. 2002 Nov;83(11):1566-71. doi: 10.1053/apmr.2002.35469.
- Background: PubMed 40460428Dou J, Wang J, Gao X, Wang G, Bai Y, Liang Y, Yang K, Yang Y, Zhang L. Effectiveness of Telemedicine Interventions on Motor and Nonmotor Outcomes in Parkinson Disease: Systematic Review and Network Meta-Analysis. J Med Internet Res. 2025 Jun 3;27:e71169. doi: 10.2196/71169.
- Background: PubMed 38920422Silva-Batista C, de Almeida FO, Wilhelm JL, Horak FB, Mancini M, King LA. Telerehabilitation by Videoconferencing for Balance and Gait in People with Parkinson's Disease: A Scoping Review. Geriatrics (Basel). 2024 May 23;9(3):66. doi: 10.3390/geriatrics9030066.
- 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. This study is not recruiting at the moment.
Guzin Kaya Aytutuldu principal investigator
Biruni University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson Disease, run by Biruni University at 1 site in Turkey (Türkiye).
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 6 inclusion and 5 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 19 Sep 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 Turkey (Türkiye). 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-Balance Evaluation Systems Test (Mini-BESTest) (time frame: Mini-BESTest will be administered once during a single assessment session, which is expected to last approximately 10-15 minutes.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07058714, which also lists 6 linked publications.
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 19 Sep 2025.