<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "http://dtd.nlm.nih.gov/publishing/2.0/journalpublishing.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" article-type="abstract" dtd-version="2.0">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IPROC</journal-id>
      <journal-id journal-id-type="nlm-ta">iproc</journal-id>
      <journal-title>Iproceedings</journal-title>
      <issn pub-type="epub">2369-6893</issn>
      <publisher>
        <publisher-name>JMIR Publications</publisher-name>
        <publisher-loc>Toronto, Canada</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
    <article-id pub-id-type="publisher-id">v2i1e7</article-id>
    <article-id pub-id-type="pmid"/>
    <article-id pub-id-type="doi">10.2196/iproc.6103</article-id>
    <article-categories>
      <subj-group subj-group-type="heading">
        <subject>Poster</subject>
      </subj-group>
      <subj-group subj-group-type="article-type">
        <subject>Poster</subject>
      </subj-group>
    </article-categories>
    <title-group>
      <article-title>Sleep Phenotypes in Chronic Pain Sufferers: Application of Machine Learning to a Large Database</article-title>
    </title-group>
    <contrib-group>
      <contrib contrib-type="editor">
        <name>
          <surname>Hale</surname>
          <given-names>Timothy</given-names>
        </name>
      </contrib>
    </contrib-group>
<contrib-group>
<contrib contrib-type="reviewer">
<name>
<surname>CHS Scientific Program Committee</surname>
</name>
</contrib>
</contrib-group>
    <contrib-group>
      <contrib contrib-type="author" id="contrib1" corresp="yes">
      <name name-style="western">
        <surname>Kong</surname>
        <given-names>Xuan</given-names>
      </name>
      <degrees>PhD</degrees>
      <xref rid="aff1" ref-type="aff">1</xref>
      <address>
        <institution>NeuroMetrix Inc.</institution>
        <addr-line>1000 Winter Street</addr-line>
        <addr-line>Waltham, MA, 02451</addr-line>
        <country>United States</country>
        <phone>1 781 314 2722</phone>
        <fax>1 781 890 1556</fax>
        <email>xkong@neurometrix.com</email>
      </address>  
      <ext-link ext-link-type="orcid">http://orcid.org/0000-0001-7225-2408</ext-link></contrib>
      <contrib contrib-type="author" id="contrib2">
        <name name-style="western">
          <surname>Ferree</surname>
          <given-names>Thomas C</given-names>
        </name>
        <degrees>PhD</degrees>
        <xref rid="aff1" ref-type="aff">1</xref>
        <ext-link ext-link-type="orcid">http://orcid.org/0000-0002-7296-9975</ext-link>
      </contrib>
      <contrib contrib-type="author" id="contrib3">
        <name name-style="western">
          <surname>Gozani</surname>
          <given-names>Shai N</given-names>
        </name>
        <degrees>MD, PhD</degrees>
        <xref rid="aff1" ref-type="aff">1</xref>
        <ext-link ext-link-type="orcid">http://orcid.org/0000-0002-8136-5962</ext-link>
      </contrib>
    </contrib-group>
    <aff id="aff1">
      <sup>1</sup>
      <institution>NeuroMetrix Inc.</institution>
      <addr-line>Waltham, MA</addr-line>
      <country>United States</country>
    </aff>
    <author-notes>
      <corresp>Corresponding Author: Xuan Kong 
      <email>xkong@neurometrix.com</email></corresp>
    </author-notes>
    <pub-date pub-type="collection"><season>Jan-Dec</season><year>2016</year></pub-date>
    <pub-date pub-type="epub">
      <day>12</day>
      <month>12</month>
      <year>2016</year>
    </pub-date>
    <volume>2</volume>
    <issue>1</issue>
    <elocation-id>e7</elocation-id>
    <!--history from ojs - api-xml-->
    <history>
      <date date-type="received">
        <day>5</day>
        <month>6</month>
        <year>2016</year>
      </date>
      <date date-type="accepted">
        <day>2</day>
        <month>8</month>
        <year>2016</year>
      </date>
    </history>
    <!--(c) the authors - correct author names and publication date here if necessary. Date in form ', dd.mm.yyyy' after jmir.org-->
    <copyright-statement>©Xuan Kong, Thomas C Ferree, Shai N Gozani. Originally published in Iproceedings (http://www.iproc.org), 12.12.2016.</copyright-statement>
    <copyright-year>2016</copyright-year>
    <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0/">
      <p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in Iproceedings, is properly cited. The complete bibliographic information, a link to the original publication on http://www.iproc.org/, as well as this copyright and license information must be included.</p>
    </license>  
    <self-uri xlink:href="http://www.iproc.org/2016/1/e7/" xlink:type="simple"/>
    <abstract>
      <sec sec-type="background">
        <title>Background</title>
        <p>Chronic pain affects over 100 million American adults. There is a negative reciprocal relationship between chronic pain and sleep. As many as 80% of chronic pain patients report poor sleep quality and daytime fatigue. We have recently reported on the clinical benefits of fixed-site high-frequency transcutaneous electrical nerve stimulation (Quell, NeuroMetrix, Inc) in a chronic pain cohort. In addition to delivering therapeutic neurostimulation, this device collects health data including utilization, sleep measures, and activity metrics. The data is communicated to the patient through a smartphone app and aggregated in a cloud server. This digital health database presents a novel opportunity to study population characteristics in a large cohort of chronic pain sufferers.</p>
      </sec>
      <sec sec-type="objective">
        <title>Objective</title>
        <p>Our primary objective was to use machine learning techniques on a large database of sleep data in chronic pain sufferers to determine “sleep phenotypes.” The long-term goal of this research is to develop personalized therapeutic profiles that optimize sleep in chronic pain patients.</p>
      </sec>
      <sec sec-type="methods">
        <title>Methods</title>
        <p>De-identified data from device users consenting to have their data uploaded to a cloud server was analyzed. Individual users were characterized by their median sleep data. The analyzed sleep parameters included total sleep time (TST, hours), sleep efficiency (SE, %), periodic leg movement index (PLMI, events/hour), position change rate (PCR, events/hour), and time out of bed (OOB, minutes). K-means clustering was used to partition the data set into 3 mutually exclusive clusters based on TST, PLMI, PCR, and OOB. The optimal number of clusters was determined by the Silhouette value. Clustering was based on the correlation metric. One-way ANOVA was used to test whether the 3 cluster groups had a common mean for each sleep parameter. For parameters with differences in group means, <italic>t</italic> test was used to identify which pairs of means were different.</p>
      </sec>
      <sec sec-type="results">
        <title>Results</title>
        <p>A total of 389 users with 5 or more nights of TST between 4 and 12 hours were included in the analysis. The sizes of the 3 clusters were 161 (41.4%), 147 (37.8%), and 81. None of the sleep parameters had the same mean among three clusters (<italic>P</italic>&#60;.001). The 3 clusters represented 3 sleep phenotypes. The largest group (n=161) was a “good sleeper” phenotype characterized by a mean TST of 7.3, SE of 95.2, and low PLMI (2.1), PCR (1.3), and OOB (1.4). The second largest cluster was a “moderate sleeper” phenotype characterized by a mean TST of 7.4, SE of 92.4, low PLMI (3.9) and PCR (0.9), but relatively high OOB of 12.7. The third cluster was a “poor sleeper” phenotype characterized by TST of 6.6, SE of 91.2, and a high PLMI of 11.7. All pair-wise cluster means were different (<italic>P</italic>&#60;.025), except for TST between good and moderate sleepers (<italic>P</italic>=.452).</p>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclusions</title>
        <p>We identified 3 sleep phenotypes in a large cohort of chronic pain sufferers. The phenotypes reflected a progression from good to poor sleepers. The poorer sleepers were characterized by either a large amount of time out of bed during the night or a high rate of periodic leg movements.</p>
      </sec>
    </abstract>
    <kwd-group>
      <kwd>sleep phenotype</kwd>
      <kwd>chronic pain</kwd>
      <kwd>machine learning</kwd>
      <kwd>clustering analysis</kwd>
    </kwd-group></article-meta>
  </front>
  <body>
    <p>This poster was presented at the Connected Health Symposium 2016, October 20-21, Boston, MA, United States. The poster is displayed as an image in <xref ref-type="fig" rid="figure1">Figure 1</xref> and as a PDF in <xref ref-type="app" rid="app1">Multimedia Appendix 1</xref>.</p>
    <fig id="figure1" position="float">
      <label>Figure 1</label>
      <caption>
        <p>Poster.</p>
      </caption>
      <graphic xlink:href="iproc_v2i1e7_fig1.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
    </fig>
  </body>
  <back>
    <app-group>
      <app id="app1">
        <title>Multimedia Appendix 1</title>
        <p>Poster.</p>
        <media xlink:href="iproc_v2i1e7_app1.pdf" xlink:title="PDF File (Adobe PDF File), 226KB"/>
      </app>
    </app-group>
  </back>
</article>
