Biruni University
Istanbul, Zeytinburnu, 34752, Turkey (Türkiye)
NCT Number: NCT07058714
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.
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Notify Me40 year–75 year
All sexes
Observational
Istanbul, Zeytinburnu, 34752, Turkey (Türkiye)
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
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)
Exclusion criteria
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
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.
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.
Time frame: The Digit Span Test will be administered once during a single session and is expected to take approximately 5 minutes to complete.
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.
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).
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.
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
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.
Time frame: The dual-task questionnaire will be completed once during the assessment session and will require approximately 3-5 minutes to complete.
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.
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.
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.
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.
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
Time frame: This self-report questionnaire will be completed once by each participant and is expected to take approximately 5-7 minutes.
Quality of life is assessed using a disease-specific self-report scale. It consists of multiple domains including mobility, daily activities, emotional well-being, social support, cognition, and communication. Each item is scored using a five-point Likert scale. The total score ranges from 0 to 100, with higher scores indicating lower perceived quality of life.
Biruni University
Other
Multimodal Artificial Intelligence-Based Fall Risk Prediction in Patients With Parkinson's Disease: Single vs. Dual-Task Conditions
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