Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis
Identifieur interne : 000553 ( Pmc/Curation ); précédent : 000552; suivant : 000554Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis
Auteurs :Source :
- Journal of Medical Internet Research [ 1439-4456 ] ; 2014.
Abstract
Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another.
The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors.
Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature.
All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory.
Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.
Url:
DOI: 10.2196/jmir.3469
PubMed: 25131661
PubMed Central: 4147713
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis</title>
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<series><title level="j">Journal of Medical Internet Research</title>
<idno type="ISSN">1439-4456</idno>
<idno type="eISSN">1438-8871</idno>
<imprint><date when="2014">2014</date>
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<front><div type="abstract" xml:lang="en"><sec sec-type="background"><title>Background</title>
<p>Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another.</p>
</sec>
<sec sec-type="objective"><title>Objective</title>
<p>The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors.</p>
</sec>
<sec sec-type="methods"><title>Methods</title>
<p>Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature.</p>
</sec>
<sec sec-type="results"><title>Results</title>
<p>All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory.</p>
</sec>
<sec sec-type="conclusions"><title>Conclusions</title>
<p>Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Med Internet Res</journal-id>
<journal-id journal-id-type="iso-abbrev">J. Med. Internet Res</journal-id>
<journal-id journal-id-type="publisher-id">JMIR</journal-id>
<journal-title-group><journal-title>Journal of Medical Internet Research</journal-title>
</journal-title-group>
<issn pub-type="ppub">1439-4456</issn>
<issn pub-type="epub">1438-8871</issn>
<publisher><publisher-name>JMIR Publications Inc.</publisher-name>
<publisher-loc>Toronto, Canada</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25131661</article-id>
<article-id pub-id-type="pmc">4147713</article-id>
<article-id pub-id-type="publisher-id">v16i8e192</article-id>
<article-id pub-id-type="doi">10.2196/jmir.3469</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject>
</subj-group>
<subj-group subj-group-type="article-type"><subject>Original Paper</subject>
</subj-group>
</article-categories>
<title-group><article-title>Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis</article-title>
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<contrib-group><contrib contrib-type="editor"><name><surname>Eysenbach</surname>
<given-names>Gunther</given-names>
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<given-names>Christine</given-names>
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<given-names>William</given-names>
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<given-names>Elizabeth J</given-names>
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<degrees>MPH, PhD</degrees>
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<address><institution>The University of Texas Medical Branch</institution>
<institution>Institute for Translational Sciences</institution>
<addr-line>301 University Blvd</addr-line>
<addr-line>Galveston, TX, 77555-0342</addr-line>
<country>United States</country>
<phone>1 409 772 1917</phone>
<fax>1 409 772 1968</fax>
<email>ellyons@utmb.edu</email>
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<xref ref-type="aff" rid="aff3">3</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-1695-2236</contrib-id>
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<degrees>BS</degrees>
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<given-names>Brian G</given-names>
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<degrees>BA, MS</degrees>
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<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-1372-6954</contrib-id>
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<given-names>Jennifer L</given-names>
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<degrees>PT, MPH, PhD</degrees>
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<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-5185-3447</contrib-id>
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<aff id="aff1"><sup>1</sup>
<institution>The University of Texas Medical Branch</institution>
<institution>Institute for Translational Sciences</institution>
<addr-line>Galveston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff2"><sup>2</sup>
<institution>The University of Texas Medical Branch</institution>
<institution>Center for Interdisciplinary Research in Women's Health</institution>
<addr-line>Galveston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff3"><sup>3</sup>
<institution>The University of Texas Medical Branch</institution>
<institution>Department of Rehabilitation Sciences</institution>
<addr-line>Galveston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff4"><sup>4</sup>
<institution>University of Central Florida</institution>
<institution>College of Medicine</institution>
<addr-line>Orlando, FL</addr-line>
<country>United States</country>
</aff>
<aff id="aff5"><sup>5</sup>
<institution>The University of Texas Medical Branch</institution>
<institution>Department of Physical Therapy</institution>
<addr-line>Galveston, TX</addr-line>
<country>United States</country>
</aff>
<author-notes><corresp>Corresponding Author: Elizabeth J Lyons <email>ellyons@utmb.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><month>8</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="epub"><day>15</day>
<month>8</month>
<year>2014</year>
</pub-date>
<volume>16</volume>
<issue>8</issue>
<elocation-id>e192</elocation-id>
<history><date date-type="received"><day>14</day>
<month>4</month>
<year>2014</year>
</date>
<date date-type="rev-request"><day>16</day>
<month>7</month>
<year>2014</year>
</date>
<date date-type="rev-recd"><day>30</day>
<month>7</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>30</day>
<month>7</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement>©Elizabeth J Lyons, Zakkoyya H Lewis, Brian G Mayrsohn, Jennifer L Rowland. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.08.2014. </copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0/"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>
This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0/">http://creativecommons.org/licenses/by/2.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="http://www.jmir.org/,">http://www.jmir.org/,</ext-link>
as well as this copyright and license information must be included.</license-p>
</license>
</permissions>
<self-uri xlink:type="simple" xlink:href="http://www.jmir.org/2014/8/e192/"></self-uri>
<abstract><sec sec-type="background"><title>Background</title>
<p>Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another.</p>
</sec>
<sec sec-type="objective"><title>Objective</title>
<p>The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors.</p>
</sec>
<sec sec-type="methods"><title>Methods</title>
<p>Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature.</p>
</sec>
<sec sec-type="results"><title>Results</title>
<p>All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory.</p>
</sec>
<sec sec-type="conclusions"><title>Conclusions</title>
<p>Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.</p>
</sec>
</abstract>
<kwd-group><kwd>electronic activity monitor</kwd>
<kwd>mobile</kwd>
<kwd>mhealth</kwd>
<kwd>physical activity</kwd>
<kwd>behavior change technique</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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