36
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Submit your digital health research with an established publisher
      - celebrating 25 years of open access

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Determinants for Sustained Use of an Activity Tracker: Observational Study

      research-article
      , MSc 1 , , , MSc 1 , , PhD 2 , , PhD 3 , , PhD 4
      (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      mobile health, mHealth, physical activity, machine learning, habits

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time.

          Objective

          The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity.

          Methods

          A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique.

          Results

          The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn.

          Conclusions

          This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: not found

          An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

          Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Social support and patient adherence to medical treatment: a meta-analysis.

            In a review of the literature from 1948 to 2001, 122 studies were found that correlated structural or functional social support with patient adherence to medical regimens. Meta-analyses establish significant average r-effect sizes between adherence and practical, emotional, and unidimensional social support; family cohesiveness and conflict; marital status; and living arrangement of adults. Substantive and methodological variables moderate these effects. Practical support bears the highest correlation with adherence. Adherence is 1.74 times higher in patients from cohesive families and 1.53 times lower in patients from families in conflict. Marital status and living with another person (for adults) increase adherence modestly. A research agenda is recommended to further examine mediators of the relationship between social support and health.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Self-Regulation Failure: An Overview

                Bookmark

                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                October 2017
                30 October 2017
                : 5
                : 10
                : e164
                Affiliations
                [1] 1 Institute for Communication Research Group Crossmedial Communication in the Public Domain Utrecht University of Applied Sciences Utrecht Netherlands
                [2] 2 Department of Communication Science Vrije Universiteit Amsterdam Netherlands
                [3] 3 Centre of Expertise Energy Hanze University of Applied Sciences Groningen Netherlands
                [4] 4 Quantified Self Institute Hanze University of Applied Sciences Groningen Netherlands
                Author notes
                Corresponding Author: Sander Hermsen sander.hermsen@ 123456hu.nl
                Author information
                http://orcid.org/0000-0001-8781-5445
                http://orcid.org/0000-0002-3903-3120
                http://orcid.org/0000-0002-2700-2204
                http://orcid.org/0000-0002-6211-4981
                http://orcid.org/0000-0003-1294-698X
                Article
                v5i10e164
                10.2196/mhealth.7311
                5695980
                29084709
                ebbb6654-507e-4b19-991f-d949d37eb8b9
                ©Sander Hermsen, Jonas Moons, Peter Kerkhof, Carina Wiekens, Martijn De Groot. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 30.10.2017.

                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
                : 12 January 2017
                : 30 January 2017
                : 6 July 2017
                : 31 July 2017
                Categories
                Original Paper
                Original Paper

                mobile health,mhealth,physical activity,machine learning,habits

                Comments

                Comment on this article