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Multimodal Artificial Intelligence Based Fall Risk Prediction in Parkinson's Disease

NCT07058714 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 30

Last updated 2025-09-19

No results posted yet for this study

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. 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.

Conditions

  • Parkinson Disease

Sponsors & Collaborators

  • Biruni University

    lead OTHER

Principal Investigators

  • Guzin Kaya Aytutuldu · Biruni University

Eligibility

Min Age
40 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-07-01
Primary Completion
2025-07-15
Completion
2025-09-15

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07058714 on ClinicalTrials.gov