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      Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study

      research-article
      , MPH, PhD 1 , , MS 1 , , MPH, PhD 1 ,
      (Reviewer), (Reviewer)
      JMIR Formative Research
      JMIR Publications
      self-monitoring, adherence, weight loss, digital tools, mobile phone

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          Abstract

          Background

          Digital self-monitoring tools offer promise to improve adherence to self-monitoring of weight and weight-related behaviors; however, less is known regarding the patterns of participant consistency and disengagement with these tools.

          Objective

          This study characterizes the consistency of use and time to disengagement with digital self-monitoring tools during a 6-month weight loss intervention and investigates whether the provision of phone-based intervention improved self-monitoring adherence.

          Methods

          Participants were 54 adults with overweight or obesity (mean age 49.6 years, SD 12.4 years; mean BMI 32.6 kg/m 2, SD 3.2 kg/m 2) enrolled in a pilot trial assessing the impact of self-monitoring technology (Fitbit Zip, Aria scale, and smartphone app), with and without additional interventionist contact, on weight loss. All participants received weight loss education and were asked to self-monitor weight, dietary intake, and physical activity daily throughout the 6-month program. Consistency was defined as the number of weeks that participants adhered to self-monitoring recommendations (7 out of 7 days). Disengagement was defined as the first of 2 consecutive weeks that the 7-day self-monitoring adherence goal was not met. Wilcoxon signed-rank tests were used to examine differences in consistency and disengagement by behavioral targets. t tests (2-tailed) and Cox proportional hazards models were used to examine whether providing additional interventionist contact would lead to significant improvements in consistency and time to disengagement from self-monitoring tools, respectively. Linear regressions were used to examine associations between consistency, time to disengagement, and weight loss.

          Results

          Participants consistently self-monitored physical activity for more weeks (mean 17.4 weeks, SD 8.5 weeks) than weight (mean 11.1 weeks, SD 8.5 weeks) or dietary intake (mean 10.8 weeks, SD 8.7 weeks; P<.05). Similarly, participants had a significantly longer time to disengagement from self-monitoring of physical activity (median 19.5 weeks) than weight (4 weeks) or dietary intake (10 weeks; P<.001). Participants randomized to receive additional interventionist contact had significantly greater consistency and longer time to disengagement for self-monitoring of dietary intake compared with participants who did not ( P=.006); however, there were no statistically significant differences between groups for self-monitoring of weight or physical activity ( P=.24 and P=.25, respectively). Greater consistency and longer time to disengagement were associated with greater weight loss for self-monitoring of weight and dietary intake ( P<.001 and P=.004, respectively) but not for physical activity ( P=.57).

          Conclusions

          Results demonstrated that self-monitoring adherence differed by behavioral target, with greater consistency and longer time to disengagement associated with lower-burden tools (ie, self-monitoring of physical activity). Consistent with supportive accountability theory, additional interventionist contact improved consistency and lengthened time to disengagement from self-monitoring of dietary intake. Given the observed associations between consistency, disengagement, and weight loss outcomes, it is important to identify additional methods of increasing consistency and engagement with digital self-monitoring tools.

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          Most cited references48

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          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Obesity is associated with serious health risks (1). Severe obesity further increases the risk of obesity-related complications, such as coronary heart disease and end-stage renal disease (2,3). From 1999-2000 through 2015-2016, a significantly increasing trend in obesity was observed (4). This report provides the most recent national data for 2017-2018 on obesity and severe obesity prevalence among adults by sex, age, and race and Hispanic origin. Trends from 1999-2000 through 2017-2018 for adults aged 20 and over are also presented.
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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                February 2022
                18 February 2022
                : 6
                : 2
                : e33603
                Affiliations
                [1 ] Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida Gainesville, FL United States
                Author notes
                Corresponding Author: Kathryn M Ross kmross@ 123456phhp.ufl.edu
                Author information
                https://orcid.org/0000-0001-8834-7137
                https://orcid.org/0000-0003-2383-6647
                https://orcid.org/0000-0002-3628-766X
                Article
                v6i2e33603
                10.2196/33603
                8900900
                35179513
                f29be22b-ba5d-4b0f-8e1c-3e311a2d6148
                ©Chelsea A Carpenter, Abraham Eastman, Kathryn M Ross. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.02.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 15 September 2021
                : 11 November 2021
                : 23 December 2021
                : 9 January 2022
                Categories
                Original Paper
                Original Paper

                self-monitoring,adherence,weight loss,digital tools,mobile phone

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