Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study
Identifieur interne : 000872 ( Pmc/Checkpoint ); précédent : 000871; suivant : 000873Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study
Auteurs :Source :
- JMIR cancer [ 2369-1999 ] ; 2016.
Abstract
Effective, broad-reaching channels are important for the delivery of health behavior interventions in order to meet the needs of the growing population of cancer survivors in the United States. New technology presents opportunities to increase the reach of health behavior change interventions and therefore their overall impact. However, evidence suggests that older adults may be slower in their adoption of these technologies than the general population. Survivors’ interest for more traditional channels of delivery (eg, clinic) versus new technology-based channels (eg, smartphones) may depend on a variety of factors, including demographics, current health status, and the behavior requiring intervention.
The aim of this study was to determine the factors that predict cancer survivors’ interest in new technology-based health behavior intervention modalities versus traditional modalities.
Surveys were mailed to 1871 survivors of breast, prostate, and colorectal cancer. Participants’ demographics, diet and physical activity behaviors, interest in health behavior interventions, and interest in intervention delivery modalities were collected. Using path analysis, we explored the relationship between four intervention modality variables (ie, clinic, telephone, computer, and smartphone) and potential predictors of modality interest.
In total, 1053 respondents to the survey (56.3% response rate); 847 provided complete data for this analysis. Delivery channel interest was highest for computer-based interventions (236/847, 27.9% very/extremely interested) and lowest for smartphone–based interventions (73/847, 8.6%), with interest in clinic-based (147/847, 17.3%) and telephone-delivered (143/847, 16.9%) falling in between. Use of other technology platforms, such as Web cameras and social networking sites, was positively predictive of interest in technology-based delivery channels. Older survivors were less likely to report interest in smartphone–based diet interventions. Physical activity, fruit and vegetable consumption, weight status, and age moderated relationships between interest in targeted intervention behavior and modality.
This study identified several predictors of survivor interest in various health behavior intervention delivery modalities. Overall, computer-based interventions were found to be most acceptable, while smartphones were the least. Factors related to survivors’ current technology use and health status play a role in their interest for technology-based intervention versus more traditional delivery channels. Future health behavior change research in this population should consider participants’ demographic, clinical, and lifestyle characteristics when selecting a delivery channel. Furthermore, current health behavior interventions for older cancer survivors may be best delivered over the Internet. Smartphone interventions may be feasible in the future following further adoption and familiarization by this particular population.
Url:
DOI: 10.2196/cancer.5247
PubMed: 28410164
PubMed Central: 5369635
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study</title>
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<series><title level="j">JMIR cancer</title>
<idno type="eISSN">2369-1999</idno>
<imprint><date when="2016">2016</date>
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<front><div type="abstract" xml:lang="en"><sec sec-type="background"><title>Background</title>
<p>Effective, broad-reaching channels are important for the delivery of health behavior interventions in order to meet the needs of the growing population of cancer survivors in the United States. New technology presents opportunities to increase the reach of health behavior change interventions and therefore their overall impact. However, evidence suggests that older adults may be slower in their adoption of these technologies than the general population. Survivors’ interest for more traditional channels of delivery (eg, clinic) versus new technology-based channels (eg, smartphones) may depend on a variety of factors, including demographics, current health status, and the behavior requiring intervention.</p>
</sec>
<sec sec-type="objective"><title>Objective</title>
<p>The aim of this study was to determine the factors that predict cancer survivors’ interest in new technology-based health behavior intervention modalities versus traditional modalities.</p>
</sec>
<sec sec-type="methods"><title>Methods</title>
<p>Surveys were mailed to 1871 survivors of breast, prostate, and colorectal cancer. Participants’ demographics, diet and physical activity behaviors, interest in health behavior interventions, and interest in intervention delivery modalities were collected. Using path analysis, we explored the relationship between four intervention modality variables (ie, clinic, telephone, computer, and smartphone) and potential predictors of modality interest.</p>
</sec>
<sec sec-type="results"><title>Results</title>
<p>In total, 1053 respondents to the survey (56.3% response rate); 847 provided complete data for this analysis. Delivery channel interest was highest for computer-based interventions (236/847, 27.9% very/extremely interested) and lowest for smartphone–based interventions (73/847, 8.6%), with interest in clinic-based (147/847, 17.3%) and telephone-delivered (143/847, 16.9%) falling in between. Use of other technology platforms, such as Web cameras and social networking sites, was positively predictive of interest in technology-based delivery channels. Older survivors were less likely to report interest in smartphone–based diet interventions. Physical activity, fruit and vegetable consumption, weight status, and age moderated relationships between interest in targeted intervention behavior and modality.</p>
</sec>
<sec sec-type="conclusions"><title>Conclusions</title>
<p>This study identified several predictors of survivor interest in various health behavior intervention delivery modalities. Overall, computer-based interventions were found to be most acceptable, while smartphones were the least. Factors related to survivors’ current technology use and health status play a role in their interest for technology-based intervention versus more traditional delivery channels. Future health behavior change research in this population should consider participants’ demographic, clinical, and lifestyle characteristics when selecting a delivery channel. Furthermore, current health behavior interventions for older cancer survivors may be best delivered over the Internet. Smartphone interventions may be feasible in the future following further adoption and familiarization by this particular population.</p>
</sec>
</div>
</front>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">JMIR Cancer</journal-id>
<journal-id journal-id-type="iso-abbrev">JMIR Cancer</journal-id>
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<issn pub-type="epub">2369-1999</issn>
<publisher><publisher-name>JMIR Publications Inc.</publisher-name>
<publisher-loc>Toronto, Canada</publisher-loc>
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<article-id pub-id-type="pmc">5369635</article-id>
<article-id pub-id-type="publisher-id">v2i1e1</article-id>
<article-id pub-id-type="doi">10.2196/cancer.5247</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>Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study</article-title>
</title-group>
<contrib-group><contrib contrib-type="editor"><name><surname>Eysenbach</surname>
<given-names>Gunther</given-names>
</name>
</contrib>
</contrib-group>
<contrib-group><contrib contrib-type="reviewer"><name><surname>Hong</surname>
<given-names>Yan</given-names>
</name>
</contrib>
<contrib contrib-type="reviewer"><name><surname>Eakin</surname>
<given-names>Elizabeth</given-names>
</name>
</contrib>
</contrib-group>
<contrib-group><contrib id="contrib1" contrib-type="author" corresp="yes"><name><surname>Martin</surname>
<given-names>Emily C</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff1">1</xref>
<address><institution>University of Texas MD Anderson Cancer Center</institution>
<institution>Department of Behavioral Science</institution>
<addr-line>PO Box 301439, Unit 1330</addr-line>
<addr-line>Houston, TX, 77230-1439</addr-line>
<country>United States</country>
<phone>1 713 745 0338</phone>
<fax>1 713 745 4286</fax>
<email>emilymar@gmail.com</email>
</address>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-2693-9553</contrib-id>
</contrib>
<contrib id="contrib2" contrib-type="author"><name><surname>Basen-Engquist</surname>
<given-names>Karen</given-names>
</name>
<degrees>PhD, MPH</degrees>
<xref ref-type="aff" rid="aff1">1</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-7299-0646</contrib-id>
</contrib>
<contrib id="contrib3" contrib-type="author"><name><surname>Cox</surname>
<given-names>Matthew G</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff1">1</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-0903-0460</contrib-id>
</contrib>
<contrib id="contrib4" contrib-type="author"><name><surname>Lyons</surname>
<given-names>Elizabeth J</given-names>
</name>
<degrees>PhD, MPH</degrees>
<xref ref-type="aff" rid="aff2">2</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-1695-2236</contrib-id>
</contrib>
<contrib id="contrib5" contrib-type="author"><name><surname>Carmack</surname>
<given-names>Cindy L</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff3">3</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-8505-7297</contrib-id>
</contrib>
<contrib id="contrib6" contrib-type="author"><name><surname>Blalock</surname>
<given-names>Janice A</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff1">1</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-2893-0245</contrib-id>
</contrib>
<contrib id="contrib7" contrib-type="author"><name><surname>Demark-Wahnefried</surname>
<given-names>Wendy</given-names>
</name>
<degrees>PhD, RD</degrees>
<xref ref-type="aff" rid="aff4">4</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-5241-932X</contrib-id>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup>
<institution>University of Texas MD Anderson Cancer Center</institution>
<institution>Department of Behavioral Science</institution>
<addr-line>Houston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff2"><sup>2</sup>
<institution>The University of Texas Medical Branch</institution>
<institution>Department of Nutrition and Metabolism</institution>
<addr-line>Galveston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff3"><sup>3</sup>
<institution>University of Texas MD Anderson Cancer Center</institution>
<institution>Department of Palliative, Rehabilitation and Integrative Medicine</institution>
<addr-line>Houston, TX</addr-line>
<country>United States</country>
</aff>
<aff id="aff4"><sup>4</sup>
<institution>University of Alabama at Birmingham</institution>
<institution>Department of Nutrition Sciences</institution>
<addr-line>Birmingham, AL</addr-line>
<country>United States</country>
</aff>
<author-notes><corresp>Corresponding Author: Emily C Martin <email>emilymar@gmail.com</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><season>Jan-Jun</season>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub"><day>11</day>
<month>2</month>
<year>2016</year>
</pub-date>
<volume>2</volume>
<issue>1</issue>
<elocation-id>e1</elocation-id>
<history><date date-type="received"><day>15</day>
<month>10</month>
<year>2015</year>
</date>
<date date-type="rev-request"><day>22</day>
<month>11</month>
<year>2015</year>
</date>
<date date-type="rev-recd"><day>28</day>
<month>12</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>3</day>
<month>1</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>©Emily C Martin, Karen Basen-Engquist, Matthew G Cox, Elizabeth J Lyons, Cindy L Carmack, Janice A Blalock, Wendy Demark-Wahnefried. Originally published in JMIR Cancer (http://cancer.jmir.org), 11.02.2016. </copyright-statement>
<copyright-year>2016</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 JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="http://cancer.jmir.org/,">http://cancer.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://cancer.jmir.org/2016/1/e1/"></self-uri>
<abstract><sec sec-type="background"><title>Background</title>
<p>Effective, broad-reaching channels are important for the delivery of health behavior interventions in order to meet the needs of the growing population of cancer survivors in the United States. New technology presents opportunities to increase the reach of health behavior change interventions and therefore their overall impact. However, evidence suggests that older adults may be slower in their adoption of these technologies than the general population. Survivors’ interest for more traditional channels of delivery (eg, clinic) versus new technology-based channels (eg, smartphones) may depend on a variety of factors, including demographics, current health status, and the behavior requiring intervention.</p>
</sec>
<sec sec-type="objective"><title>Objective</title>
<p>The aim of this study was to determine the factors that predict cancer survivors’ interest in new technology-based health behavior intervention modalities versus traditional modalities.</p>
</sec>
<sec sec-type="methods"><title>Methods</title>
<p>Surveys were mailed to 1871 survivors of breast, prostate, and colorectal cancer. Participants’ demographics, diet and physical activity behaviors, interest in health behavior interventions, and interest in intervention delivery modalities were collected. Using path analysis, we explored the relationship between four intervention modality variables (ie, clinic, telephone, computer, and smartphone) and potential predictors of modality interest.</p>
</sec>
<sec sec-type="results"><title>Results</title>
<p>In total, 1053 respondents to the survey (56.3% response rate); 847 provided complete data for this analysis. Delivery channel interest was highest for computer-based interventions (236/847, 27.9% very/extremely interested) and lowest for smartphone–based interventions (73/847, 8.6%), with interest in clinic-based (147/847, 17.3%) and telephone-delivered (143/847, 16.9%) falling in between. Use of other technology platforms, such as Web cameras and social networking sites, was positively predictive of interest in technology-based delivery channels. Older survivors were less likely to report interest in smartphone–based diet interventions. Physical activity, fruit and vegetable consumption, weight status, and age moderated relationships between interest in targeted intervention behavior and modality.</p>
</sec>
<sec sec-type="conclusions"><title>Conclusions</title>
<p>This study identified several predictors of survivor interest in various health behavior intervention delivery modalities. Overall, computer-based interventions were found to be most acceptable, while smartphones were the least. Factors related to survivors’ current technology use and health status play a role in their interest for technology-based intervention versus more traditional delivery channels. Future health behavior change research in this population should consider participants’ demographic, clinical, and lifestyle characteristics when selecting a delivery channel. Furthermore, current health behavior interventions for older cancer survivors may be best delivered over the Internet. Smartphone interventions may be feasible in the future following further adoption and familiarization by this particular population.</p>
</sec>
</abstract>
<kwd-group><kwd>cancer survivor</kwd>
<kwd>technology</kwd>
<kwd>smartphone</kwd>
<kwd>behavioral intervention</kwd>
<kwd>physical activity</kwd>
<kwd>diet</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree></tree>
</affiliations>
</record>
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