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Date of Award

1-21-2016

Document Type

Thesis and Dissertation-ISU Access Only

Degree Name

Master of Science (MS)

Department

School of Kinesiology and Recreation

First Advisor

Michael Torry

Abstract

Clinicians use ankle rehabilitation to treat foot and ankle disorders. The goal of rehabilitation is to increase range of motion, muscular strength and proprioception of the foot and lower leg muscles. With the amount of exercises that can be chosen for rehabilitating the ankle and foot and in order to maximize clinic time and outcome of rehabilitation sessions, clinicians must make choices on what exercises and what positions to use during the rehabilitation protocol to maximize outcome. Little data exists to aide in this decision process and it is generally unknown what exercise would best activate the target muscles during rehabilitation.

The purpose of this study was to use Electromyography (EMG) to establish a list of exercises and positions that activate the included muscles. This will then help clinicians determine what exercises are best to strengthen the foot and ankle musculature while also minimizing time required in the clinical setting to accomplish the rehabilitation goal. It was hypothesized that the more dynamic an exercise, the greater %MVIC that will be achieved during that exercise.

Nineteen healthy subjects (11 males & 8 females, 20.89 +/- 1.52 yrs., 1723.22 +/- 72.39 mm, and 73.06 +/- 13.62 kg) volunteered to participate. Participants were instructed through six different exercises in three different weight bearing postures. EMG data was collected while participants completed the exercises. Maximum voluntary contraction (MVIC) was collected to compare to the amount of activation of the muscle during the exercises. A Delysys Bagnolli system with DE- 2.1 electrodes was used to collect EMG data with samples being collected with 100K amplification online with Vicon Nexus 2.1.1. A sampling rate of 1000 Hz was used for all channels. The raw signal was processed in a custom Matlab program using a bandpass filter (50-500Hz; a 60/120 Hz notch and the quiescent baseline). Root-mean-square values were calculated with a moving window of 50 ms and all trial data processed with full wave rectification. Trial data was divided by the average MVIC to calculate a %MVIC.

RMANOVA detected significant differences (p < .0004) between exercises. Exercises were then ranked from least to greatest amount of activation based on %MVIC.

Comments

Imported from ProQuest Egeland_ilstu_0092N_10672.pdf

Page Count

80

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