Gait in Neurodegenerative Disease Database 1.0.0

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    <main><h1 style="margin-top: 1.75em; padding-top: .4em;">Gait Dynamics in Neuro-Degenerative Disease Data Base</h1><nav id="tocwrapper" style="padding: 1.5em 0 0 0; float: right;"> <!-- id to position toc added by KP, 6 July 2015 -->
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<p>
Neuro-degenerative disease often affects gait and mobility.  To understand
better the pathophysiology of these diseases and to improve our ability to
measure responses to therapeutic interventions, it may be helpful to
quantify gait dynamics accurately.  The records in this database are from
patients with <a href="http://www.parkinson.org/" target="other">Parkinson's
disease</a> (n = 15), <a href="http://www.hdfoundation.org/"
target="other">Huntington's disease</a> (n = 20), or <a
href="http://www.alsa.org/" target="other">amyotrophic lateral sclerosis</a> (n
= 13).  Records from 16 healthy control subjects are also included here.</p>

<p>
The raw data were obtained using force-sensitive resistors, with the
output roughly proportional to the force under the foot. 
Stride-to-stride measures of footfall contact times were derived from these
signals, as illustrated below.  (Click <a href="example-ts.ps">here</a> for a
PostScript version of this figure.)</p>

<div class="outer2"><div class="inner2">
<img src=example-ts.png alt="[Examples of Gait Time Series]" width="505">
</div></div>

<p>
Each of the 64 records is identified by the name of the subject group
(<tt>hunt</tt>, <tt>park</tt>, <tt>als</tt>, or <tt>control</tt>) followed by
an arbitrary ID number.  The four files associated with each record have names
beginning with the record name, and suffixes indicating the type and contents
of the file:</p>

<div class="edbtable">
<table> <tr><th>Suffix</th><th>Contents</th></tr>
<tr><td><tt>.hea</tt></td>
    <td><a href="https://physionet.org/physiotools/dbag/header-5.htm">Header</a> (text)</td></tr>
<tr><td><tt>.let</tt></td><td>Left foot signal (binary)</td></tr>
<tr><td><tt>.rit</tt></td><td>Right foot signal (binary)</td></tr>
<tr><td><tt>.ts</tt></td><td>Derived time series (text)</td></tr>
</table>
</div>

<p>Within the time series (<tt>.ts</tt>) files, the contents are:</p>

<div class="edbtable">
<table>
<tr><th>Column</th><th>Contents</th></tr>
<tr><td>1</td><td>Elapsed Time (sec)</td></tr>
<tr><td>2</td><td>Left Stride Interval (sec)</td></tr>
<tr><td>3</td><td>Right Stride Interval (sec)</td></tr>
<tr><td>4</td><td>Left Swing Interval (sec)</td></tr>
<tr><td>5</td><td>Right Swing Interval (sec)</td></tr>
<tr><td>6</td><td>Left Swing Interval (% of stride)</td></tr>
<tr><td>7</td><td>Right Swing Interval (% of stride)</td></tr>
<tr><td>8</td><td>Left Stance Interval (sec)</td></tr>
<tr><td>9</td><td>Right Stance Interval (sec)</td></tr>
<tr><td>10</td><td>Left Stance Interval (% of stride)</td></tr>
<tr><td>11</td><td>Right Stance Interval (% of stride)</td></tr>
<tr><td>12</td><td>Double Support Interval (sec)</td></tr>
<tr><td>13</td><td>Double Support Interval (% of stride)</td></tr>
</table>
</div>

<p>Please note that these time series have not been filtered.</p>

<p>
A separate file (<a
href="subject-description.txt"><tt>subject-description.txt</tt></a>, a
tab-delimited text file) includes clinical information for each
subject, including age, gender, height, weight, walking speed, and a
measure of disease severity or duration.  For the subjects with
Parkinson's disease, this is the Hohn and Yahr score (a higher score
indicates more advanced disease).  For the subjects with Huntington's
disease, this is the total functional capacity measure (a lower score
indicates more advanced functional impairment).  For the subjects with
amyotrophic lateral sclerosis, the number here is the time in months since the
diagnosis of the disease.  For the control subjects, an arbitrary 0 is
used as a place holder.</p>

<h3>Questions for study</h3>

<p>
We have done some initial analyses of these data sets.  Much, however, is still
unknown. For example: Are the dynamics of the sub-phases of the stride (e.g.,
stance and swing) identical to that of the stride itself? What is the effect of
disease on gait asymmetry?  Are stride dynamics nonlinear?  Does this change
with disease?</p>

<h3>References</h3>

<p>
We have reported the association between disease and the dynamics of the stride
time.  For more information about our analyses of these data files, please see:</p>

<ul>
<li> Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D,
     Goldberger AL.
     <a href="http://jap.physiology.org/cgi/content/full/88/6/2045?ijkey=WKwCeA4NUn0rE&keytype=ref&siteid=physiology"
     target="other">Dynamic markers of altered gait rhythm in amyotrophic
     lateral sclerosis.</a>
<!--
     <a href="http://jap.physiology.org/cgi/content/full/88/6/2045"
     target="other">Dynamic markers of altered gait rhythm in amyotrophic
     lateral sclerosis.</a>
-->
     <i>J Applied Physiology</i>; <b>88</b>:2045-2053, 2000.</li>
<li> Hausdorff JM, Mitchell SL, Firtion R, Peng CK, Cudkowicz ME, Wei JY,
     Goldberger AL.
     <a href="http://jap.physiology.org/cgi/content/full/82/1/262"
     target="other">Altered fractal dynamics of gait: reduced stride-interval
     correlations with aging and Huntington's disease.</a>
     <i>J Applied Physiology</i>; <b>82</b>:262-269, 1997.</li>
</ul>

<p>Address for correspondence: <a
href="mailto:jhausdor@caregroup.harvard.edu">jhausdor@caregroup.harvard.edu</a></p>
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