FallriskPD: fall risk evaluation in Parkinson’s disease using intelligent gait analysis.

• Conditions: G20.1

• Registration Number: DRKS00015085
• Lead Sponsor: niversitätsklinikum Erlangen, Molekular-Neurologische Abteilung

Brief Summary

This study present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week realworld gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-09-03
• Study Completion: 2021-06-22
• Results First Posted: 2022-10-19
• Last Update Posted: 19 August 2024

Recruitment & Eligibility

• Status: Complete
• Sex: All
• Target Recruitment: 51

Inclusion Criteria

Diagnosis of a Parkinson syndrome in accordance with the guideline of the German Society of Neurology (Hoehn & Yahr stadium I-III)
- Ability to walk for 2 Minutes without assistance
- Ability to speak and read
- Ability to use an application running on a smart device
- informed consent with understanding of the study procedures

Exclusion Criteria

- Aphasia and Alexia
- Impaired vision which makes reading unfeasible
- Regular use of gait aids
- Decompensated cardiopulmonary restrictions
- Maximal walking distance < 100 meter
- Inability to understand the informed consent and the study procedures
- Distinct musculoskeletal disorder, which highly restricts movement and gait abilities

Study & Design

• Study Type: observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
1. Retrospective classification of patients into fall risk categories regarding sensor-based gait parameters continuously recorded over a 2 weeks period in home environments. <br>2. Prospective identification of the fall risk sensor-based gait parameters continuously recorded over a 4 weeks period in home environments.

Secondary Outcome Measures

Name	Time	Method
1. Frequency of falls in pre-, post- and follow up-assessments.<br>2. Fear of falling in pre-, post- and follow up-assessments.