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Early Aerobic Training Program After Ischemic Stroke

2014-08-27 03:47:51 | BioPortfolio

Summary

The purpose of this study is to examine the feasibility and efficacy of an early supervised aerobic exercise program for patients following a minor ischemic stroke.

Description

The purpose of this study is to examine the feasibility and efficacy of an early supervised aerobic exercise program for patients following a minor ischemic stroke. Patients after a minor ischemic stroke would be randomized within 1-3 weeks of stroke onset to a control group (6 weeks of low intensity stretching and coordination exercises followed by a supervised aerobic training program) or an experimental group (immediate aerobic training program). Patients will be evaluated for measures of ambulation endurance, mobility, gait, functional assessment and clinical follow-up after 6 weeks and again 3 months later.

Study Design

Allocation: Randomized, Control: Active Control, Endpoint Classification: Safety/Efficacy Study, Intervention Model: Parallel Assignment, Masking: Open Label, Primary Purpose: Treatment

Conditions

Stroke

Intervention

Supervised aerobic training

Location

Chaim Sheba Medical Center
Tel Hashomer
Israel
52621

Status

Completed

Source

Sheba Medical Center

Results (where available)

View Results

Links

Published on BioPortfolio: 2014-08-27T03:47:51-0400

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Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

Restoration of functions to the maximum degree possible in a person or persons suffering from a stroke.

Financial support for training including both student stipends and loans and training grants to institutions.

Stroke caused by lacunar infarction or other small vessel diseases of the brain. It features hemiparesis (see PARESIS), hemisensory, or hemisensory motor loss.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

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