Carina Wiekens

Determinants for Sustained Use of an Activity Tracker: Observational Study

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.

End User Research in PowerMatching City II

In PowerMatching City, the leading Dutch smart grid project, 40 households participated in a field laboratory designed for sustainable living. The participating households were equipped with various decentralized energy sources (PV and micro combined heat-power units), hybrid heat pumps, smart appliances, smart meters, and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid and to be able to make optimal use of the decentralized energy sources, two energy services were developed jointly with the end users: Smart Cost Savings enabled users to keep the costs of energy consumption as low as possible, and Sustainable Together enabled them to become a sustainable community. Furthermore, devices could be controlled automatically, smartly, or manually to optimize the energy use of the households. Quantitative and qualitative studies were conducted to provide insight into the experiences and behaviours of end users. In this chapter, these experiences and behaviours are described. The chapter argues that end users: (1) prefer to consume self-produced energy, even when it is not the most efficient strategy to follow, (2) prefer feedback on costs over feedback on sustainability, and (3) prefer automatic and smart control, even though manual control of appliances felt most rewarding. Furthermore, we found that experiences and behaviours were fully dependent on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.