Clinical Trials Logo

Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT06380049
Other study ID # 0720242110
Secondary ID 20240012366
Status Not yet recruiting
Phase
First received
Last updated
Start date April 29, 2024
Est. completion date April 28, 2026

Study information

Verified date April 2024
Source Seoul National University Hospital
Contact JungHyun Kim, prof
Phone 82+1088632341
Email kiking0@naver.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.


Description:

Objective: The primary objective is to develop and validate a machine learning-based model that uses multi-sensor (EMG) signals to identify stroke patients at high risk of falls. This model aims to improve on traditional fall risk assessments which rely heavily on physical assessments and patient history. Study Design: This is a prospective, multicenter, open-label, confirmatory clinical trial. It involves collecting EMG data from stroke patients and applying machine learning techniques to predict fall risk. The study will compare the predictive accuracy of the machine learning model against conventional fall risk assessment tools. Methods: 1. Participants: • Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings. 2. Interventions: • Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements. 3. Outcome Measures: - Primary Outcome: Sensitivity and specificity of the machine learning model in predicting high-risk fall patients. - Secondary Outcomes: Comparison of the machine learning model's predictive performance with traditional fall risk assessment tools (e.g., Berg Balance Scale). Data Collection: - EMG sensors will be attached to the patients' muscles of the lower limbs. Sensors will record muscle activity during movement, which will then be analyzed using the machine learning model. - The predictive model will be trained using features extracted from the EMG signals, and its performance will be validated against actual fall incidents reported during the follow-up period. Statistical Analysis: - The machine learning model's efficacy will be measured through its sensitivity (ability to correctly identify high-risk patients) and specificity (ability to correctly identify low-risk patients). - Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) statistics will be used to assess model performance.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 90
Est. completion date April 28, 2026
Est. primary completion date April 28, 2025
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 19 Years and older
Eligibility Stroke Participants Inclusion Criteria: - 19 years and older - the onset of the stroke is less than 3months ago - Lower extremity weakness due to stroke (MMT =< 4 grade) - Cognitive ability to follow commands Exclusion Criteria: - stroke recurrence - other neurological abnormalities (e.g. parkinson's disease). - severely impaired cognition - serious and complex medical conditions(e.g. active cancer) - cardiac pacemaker or other implanted electronic system Health Participants Inclusion Criteria: - 19 years and older - Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects Exclusion Criteria: - other neurological abnormalities (e.g. parkinson's disease). - severely impaired cognition - serious and complex medical conditions(e.g. active cancer) - cardiac pacemaker or other implanted electronic system

Study Design


Related Conditions & MeSH terms


Intervention

Device:
EMG Analysis Software
Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.

Locations

Country Name City State
n/a

Sponsors (2)

Lead Sponsor Collaborator
Seoul National University Hospital Ministry of Trade, Industry & Energy, Republic of Korea

Outcome

Type Measure Description Time frame Safety issue
Other Area Under the Receiver Operating Characteristic Curve This is a performance measurement for classification problems at various threshold settings. ROC is a probability curve, and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. At the time of the single visit
Other Matthews Correlation Coefficient The MCC is used in machine learning as a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. At the time of the single visit
Primary Sensitivity of the Machine Learning Model The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified. At the time of the single visit
Secondary Specificity of the Machine Learning Model Specificity measures the proportion of actual negatives that are correctly identified. At the time of the single visit
See also
  Status Clinical Trial Phase
Recruiting NCT04043052 - Mobile Technologies and Post-stroke Depression N/A
Recruiting NCT03869138 - Alternative Therapies for Improving Physical Function in Individuals With Stroke N/A
Completed NCT04034069 - Effects of Priming Intermittent Theta Burst Stimulation on Upper Limb Motor Recovery After Stroke: A Randomized Controlled Trial N/A
Completed NCT04101695 - Hemodynamic Response of Anodal Transcranial Direct Current Stimulation Over the Cerebellar Hemisphere in Healthy Subjects N/A
Terminated NCT03052712 - Validation and Standardization of a Battery Evaluation of the Socio-emotional Functions in Various Neurological Pathologies N/A
Completed NCT00391378 - Cerebral Lesions and Outcome After Cardiac Surgery (CLOCS) N/A
Recruiting NCT06204744 - Home-based Arm and Hand Exercise Program for Stroke: A Multisite Trial N/A
Active, not recruiting NCT06043167 - Clinimetric Application of FOUR Scale as in Treatment and Rehabilitation of Patients With Acute Cerebral Injury
Active, not recruiting NCT04535479 - Dry Needling for Spasticity in Stroke N/A
Completed NCT03985761 - Utilizing Gaming Mechanics to Optimize Telerehabilitation Adherence in Persons With Stroke N/A
Recruiting NCT00859885 - International PFO Consortium N/A
Recruiting NCT06034119 - Effects of Voluntary Adjustments During Walking in Participants Post-stroke N/A
Completed NCT03622411 - Tablet-based Aphasia Therapy in the Chronic Phase N/A
Completed NCT01662960 - Visual Feedback Therapy for Treating Individuals With Hemiparesis Following Stroke N/A
Recruiting NCT05854485 - Robot-Aided Assessment and Rehabilitation of Upper Extremity Function After Stroke N/A
Active, not recruiting NCT05520528 - Impact of Group Participation on Adults With Aphasia N/A
Completed NCT03366129 - Blood-Brain Barrier Disruption in People With White Matter Hyperintensities Who Have Had a Stroke
Completed NCT05805748 - Serious Game Therapy in Neglect Patients N/A
Completed NCT03281590 - Stroke and Cerebrovascular Diseases Registry
Recruiting NCT05621980 - Finger Movement Training After Stroke N/A