Personalized Mobility Interventions Using Smart Sensor Resources for

Status Active, not recruiting

Clinical Trial Summary

The goal of this research is to analyze data from smartphone-based and wearable sensors, using advanced machine-learning and data-mining techniques, and to combine this information with performance-based measures, participant-reported measures, and structured interviews to create a clinical toolbox to (i) identify individuals who exhibit reduced prosthesis use (compared to expected usage levels based on K-level designation and/or participant goals of community mobility and social interaction), (ii) identify prosthetic/physical and psychological factors that limit prosthesis use, and (iii) determine the effect of targeted interventions to increase prosthesis use and facilitate achievement of participant goals. Objective sensor-based measurement of home and community activities will allow for the correlation of real-world function to in-clinic assessments and to monitor changes resulting from rehabilitation interventions in real time. Machine-learning and data mining techniques will be used to identify a subset of measures from this toolbox that sensitively and accurately reflect real-world function, enabling clinicians to predict and assess activity and provide effective interventions to optimize prosthesis use. The goal of this project, to improve overall performance with respect to activities of daily living and other real-world activities, thus addresses the Fiscal Year 2017 (FY17) Orthotics and Prosthetics Outcomes Research Program (OPORP) Focus Area of Orthotic or Prosthetic Device Function.

Clinical Trial Description

The overall goal of this research is to create a clinical toolkit to predict prosthesis use and function in the community. With this toolkit, clinicians will be able best determine an individual's K-Level designation, resulting in increased prosthesis use. Aim 1: Determine whether a participant's prosthesis use matches the assigned K-level and/or self-reported goals and, if not, determine the reason(s) using an expert panel to evaluate data from performance-related measures, participant-reported measures, and smartphone and prosthesis sensors (clinical toolbox). Aim 2: Quantify the effects of targeted physical intervention (prosthesis repair/refit, physical rehabilitation) or psychological intervention (motivational interviewing) or both on activity levels and patient goals. Aim 3: Identify measure(s) that sensitively predict prosthesis use to create a clinically deployable toolkit to evaluate and optimize prosthesis use in the community. ;

Study Design

Related Conditions & MeSH terms

Amputation

Clinical Trial Details

NCT number	NCT03930199
Study type	Interventional
Source	Shirley Ryan AbilityLab
Contact
Status	Active, not recruiting
Phase	N/A
Start date	May 1, 2020
Completion date	September 30, 2024

View Details

See also
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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

Personalized Mobility Interventions Using Smart Sensor Resources for Lower-Limb Prosthesis Users

Clinical Trial Summary

Clinical Trial Description

Study Design

Related Conditions & MeSH terms