Event Abstract

Determinants for sustained use of an activity tracker: an observational study among 711 participants in France

  • 1 Utrecht University of Applied Sciences, Institute of Communication, Netherlands
  • 2 VU University Amsterdam, Netherlands
  • 3 Hanze University of Applied Sciences, Netherlands

One of the biggest threats to our health is physical inactivity, which is considered to cause 6% of deaths globally. Feedback from digital technology, e.g. activity trackers, has the potential to encourage physical activity. However, a crucial ingredient for lasting effects on behaviour change is sustained use of the intervention. Unfortunately, there is as yet little research available on the natural development of use of activity trackers. What little (anecdotal) evidence available, suggests activity trackers may have a poor record when it comes to sustained use, since they are easy to switch off, ignore, lose, or neglect. Furthermore, there is to date little research available that could shed light 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 behaviour change) and which users stop using their trackers after a short time. The current study attempts to contribute to bridging this gap in our knowledge, by looking into factors predicting sustained use in the first year after purchase. Specifically, we look at the relative importance of a range of determinants from literature, i.e. demographic and socio-economical, 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. 711 participants from four urban areas in France received a Fitbit ZIP activity tracker and gave permission to use their logged data. Participants filled out three questionnaires, at start, after 98 days, and after 232 days, to measure the above-mentioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit for 320 days. We determined the relative importance of all included predictors by using a machine learning analysis technique, Random Forest. This is an ensemble method that makes use of a large number of decision trees, strengthened by drawing random samples from the original data set with replacement. At each branch of the tree, a random selection of the predictor variables is considered, one of which is used to split the cases in an optimal manner. Random Forest analysis can produce a list of predictors, sorted by relative importance, and has the benefit of being able to deal with large numbers of predictor variables with complex interactions, especially in cases of relatively few participants. The data showed a slow exponential decay in Fitbit use, with 74% of participants still tracking after 100 days, and 16% 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 or lost trackers (21.5% of all respondents). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience-related factors, smartphone type, household type, perceived effect of the Fitbit, and goal-related factors. We explore the role of those predictors of which a difference in marginal means constitutes meaningful differences in days worn. This study offers an overview of the natural development of the use of an activity tracker, and 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, but 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 spent on technical and user experience-related aspects of activity trackers.

Keywords: Digital Health, mHealth, Activity tracking, Quantified Self, Fitbit, sustained use, adherence, random forest

Conference: 3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change, London, United Kingdom, 22 Feb - 23 Feb, 2017.

Presentation Type: Research abstract

Topic: Digital Health

Citation: Hermsen S, Moons J, Kerkhof P, Wiekens C and De Groot M (2017). Determinants for sustained use of an activity tracker: an observational study among 711 participants in France. Front. Public Health. Conference Abstract: 3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change. doi: 10.3389/conf.FPUBH.2017.03.00044

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Received: 22 Feb 2017; Published Online: 22 Feb 2017.

* Correspondence: Mr. Sander Hermsen, Utrecht University of Applied Sciences, Institute of Communication, Utrecht, 3584 CJ, Netherlands, sander.hermsen@hu.nl

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