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      Evaluating Motivational Interviewing and Habit Formation to Enhance the Effect of Activity Trackers on Healthy Adults’ Activity Levels: Randomized Intervention

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          Abstract

          Background

          While widely used and endorsed, there is limited evidence supporting the benefits of activity trackers for increasing physical activity; these devices may be more effective when combined with additional strategies that promote sustained behavior change like motivational interviewing (MI) and habit development.

          Objective

          This study aims to determine the utility of wearable activity trackers alone or in combination with these behavior change strategies for promoting improvements in active and sedentary behaviors.

          Methods

          A sample of 91 adults (48/91 female, 53%) was randomized to receive a Fitbit Charge alone or in combination with MI and habit education for 12 weeks. Active and sedentary behaviors were assessed pre and post using research-grade activity monitors (ActiGraph and activPAL), and the development of habits surrounding the use of the trackers was assessed postintervention with the Self-Reported Habit Index. During the intervention, Fitbit wear time and activity levels were monitored with the activity trackers. Linear regression analyses were used to determine the influence of the trial on outcomes of physical activity and sedentary time. The influence of habits was examined using correlation coefficients relating habits of tracker use (wearing the tracker and checking data on the tracker and associated app) to Fitbit wear time and activity levels during the intervention and at follow-up.

          Results

          Regression analyses revealed no significant differences by group in any of the primary outcomes (all P>.05). However, personal characteristics, including lower baseline activity levels (beta=–.49, P=.01) and lack of previous experience with pedometers (beta=–.23, P=.03) were predictive of greater improvements in moderate and vigorous physical activity. Furthermore, for individuals with higher activity levels at the baseline, MI and habit education were more effective for maintaining these activity levels when compared with receiving a Fitbit alone (eg, small increase of ~48 steps/day, d=0.01, vs large decrease of ~1830 steps/day, d=0.95). Finally, habit development was significantly related to steps/day during ( r=.30, P=.004) and following the intervention ( r=.27, P=.03).

          Conclusions

          This study suggests that activity trackers may have beneficial effects on physical activity in healthy adults, but benefits vary based on individual factors. Furthermore, this study highlights the importance of habit development surrounding the wear and use of activity trackers and the associated software to promote increases in physical activity.

          Trial Registration

          ClinicalTrials.gov NCT03837366; https://clinicaltrials.gov/ct2/show/NCT03837366

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          Most cited references24

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          Goal Achievement: The Role of Intentions

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            Mechanisms of health behavior change in persons with chronic illness or disability: the Health Action Process Approach (HAPA).

            The present article presents an overview of theoretical constructs and mechanisms of health behavior change that have been found useful in research on people with chronic illness and disability. A self-regulation framework (Health Action Process Approach) serves as a backdrop, making a distinction between goal setting and goal pursuit. Risk perception, outcome expectancies, and task self-efficacy are seen as predisposing factors in the goal-setting (motivational) phase, whereas planning, action control, and maintenance/recovery self-efficacy are regarded as being influential in the subsequent goal-pursuit (volitional) phase. The first phase leads to forming an intention, and the second to actual behavior change. Such a mediator model serves to explain social-cognitive processes in health behavior change. By adding a second layer, a moderator model is provided in which three stages are distinguished to segment the audience for tailored interventions. Identifying persons as preintenders, intenders, or actors offers an opportunity to match theory-based treatments to specific target groups. Numerous research and assessment examples, especially within the physical activity domain, serve to illustrate the application of the model to rehabilitation settings and health promotion for people with chronic illness or disability. The theoretical developments and research evidence for the self-regulation framework explain the cognitive mechanisms of behavior change and adherence to treatment in the rehabilitation setting.
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              Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women.

              Direct-to-consumer mHealth devices are a potential asset to behavioral research but rarely tested as intervention tools. This trial examined the accelerometer-based Fitbit tracker and website as a low-touch physical activity intervention. The purpose of this study is to evaluate, within an RCT, the feasibility and preliminary efficacy of integrating the Fitbit tracker and website into a physical activity intervention for postmenopausal women.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                February 2019
                14 February 2019
                : 7
                : 2
                : e10988
                Affiliations
                [1 ] Department of Kinesiology Iowa State University Ames, IA United States
                [2 ] Department of Health and Human Performance University of Tennessee-Chatanooga Chattanooga, TN United States
                [3 ] Deparment of Rehabilitation and Movement Science University of Vermont Burlington, VT United States
                [4 ] Department of Psychology Iowa State University Ames, IA United States
                Author notes
                Corresponding Author: Laura D Ellingson ellingl@ 123456iastate.edu
                Author information
                http://orcid.org/0000-0003-4493-0085
                http://orcid.org/0000-0001-9597-7954
                http://orcid.org/0000-0002-6718-9421
                http://orcid.org/0000-0002-8691-2232
                http://orcid.org/0000-0001-6751-3896
                http://orcid.org/0000-0002-1597-1278
                http://orcid.org/0000-0003-1126-1559
                http://orcid.org/0000-0001-7132-9725
                Article
                v7i2e10988
                10.2196/10988
                6393778
                30762582
                9b2dbcb0-e86b-4123-824e-2a8958081831
                ©Laura D Ellingson, Jeni E Lansing, Kathryn J DeShaw, Karissa L Peyer, Yang Bai, Maria Perez, L Alison Phillips, Gregory J Welk. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 14.02.2019.

                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 mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/.as well as this copyright and license information must be included.

                History
                : 7 May 2018
                : 1 September 2018
                : 4 October 2018
                : 20 October 2018
                Categories
                Original Paper
                Original Paper

                activity tracker,habit,mhealth,motivational interviewing,mobile phone,physical activity,wearable electronic devices

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