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          <dc:identifier>https://hdl.handle.net/2286/R.2.N.187723</dc:identifier>
                  <dc:rights>http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
          <dc:rights>All Rights Reserved</dc:rights>
                  <dc:date>2023</dc:date>
                  <dc:format>174 pages</dc:format>
                  <dc:type>Doctoral Dissertation</dc:type>
          <dc:type>Academic theses</dc:type>
          <dc:type>Text</dc:type>
                  <dc:language>eng</dc:language>
                  <dc:contributor>Cosgrove, Kelly Sarah</dc:contributor>
          <dc:contributor>Wharton, Christopher</dc:contributor>
          <dc:contributor>Adams, Marc</dc:contributor>
          <dc:contributor>DesRoches, Tyler</dc:contributor>
          <dc:contributor>Grebitus, Carola</dc:contributor>
          <dc:contributor>Johnston, Carol</dc:contributor>
          <dc:contributor>Arizona State University</dc:contributor>
                  <dc:description>Partial requirement for: Ph.D., Arizona State University, 2023</dc:description>
          <dc:description>Field of study: Exercise and Nutritional Sciences</dc:description>
          <dc:description>Tools designed to help match people with behaviors they identify as likely to lead to a successful behavioral outcome remain under-researched. This study assessed the effect of a participant-driven behavior-matching intervention on 1) the adoption of a new behavior related to fruit and vegetable (F&amp;V) consumption, 2) study attrition, and 3) changes in F&amp;V consumption. In this two-arm randomized controlled trial, 64 adults who did not meet standard F&amp;V recommendations were allocated to an intervention (n=33) or control group (n=31). Participants in the intervention group ranked 20 F&amp;V-related behaviors according to their perceived likelihood of engagement in the behavior and their perception of the behavior’s efficacy in increasing F&amp;V consumption. Participants in the intervention group were subsequently shown the list of 20 behaviors in order of their provided rankings, with the highest-ranked behaviors at the top, and were asked to choose a behavior they would like to perform daily for 4 weeks. The control group chose from a random-order list of the same 20 behaviors to adopt daily for 4 weeks. During the study period, text messages were sent to all participants 90 minutes before their reported bedtime to collect Yes/No data reflecting successful behavior engagement each day. The binary repeated-measures data collected from the text messages was analyzed using mixed-effects logistic regression, differences in attrition were assessed using log-rank analysis, and change scores in F&amp;V consumption were compared between the two groups using the Man-Whitney U test. P&lt;0.05 indicated significance. The rate of successful behavior adoption did not differ significantly between the two groups (b=0.09, 95%CI= -0.81, 0.98, p=0.85). The log rank test results indicated that there was no significant difference in attrition between the two groups (χ2=2.68, df=1, p=0.10). F&amp;V consumption increased significantly over the 4 weeks in the total sample (Z=-5.86, p&lt;0.001), but no differences in F&amp;V change scores were identified between the control and intervention groups (Z=-0.21, p=0.84). The behavior-matching tool assessed in this study did not significantly improve behavior adoption, study attrition, or F&amp;V intake over 4 weeks.</dc:description>
                  <dc:subject>Health Sciences</dc:subject>
          <dc:subject>Nutrition</dc:subject>
          <dc:subject>Behavioral Sciences</dc:subject>
                  <dc:title>Effects of a Behavior-Matching Tool on Behavior Adoption and Attrition in an eHealth Intervention Focused on Fruit and Vegetable Consumption</dc:title></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>
