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Research Article

Engaging with self-tracking applications: how do users respond to their performance data?

, , &
Pages 941-961 | Received 09 Jan 2021, Accepted 15 May 2022, Published online: 30 May 2022
 

ABSTRACT

Self-tracking devices and applications have become popular in recent years and changed user behaviour. Previous research has primarily focused on the adoption of self-tracking devices and their effects on self-assessment. As adoption increases, user engagement becomes prominent for the continuous use of the devices and the applications. In this study, we focus on user engagement with activity tracking applications, e.g., Fitbit Flex and Jawbone Up that offer data on user performance. We collected data from semi-structured interviews with 54 participants. We propose a process model comprising four stages which involve distinct user interactions with data: review, react, reflect, and respond. We advance research in this domain by the proposed process model that explicates user engagement in two cases: when the user encounters satisfactory or unsatisfactory results. In the latter case, we depict four response tactics when users are confronted with unsatisfactory results.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. For example, the link between covarying “user engagement” and “continuance intention” can be identified in a regression analysis. This perspective presumes an invariant static (i.e., not evolving, Langley, Citation1999) relationship, which does not account for contingencies such as goal attainment or situational challenges (such as long work days with not enough time to reach the goal) that might have affected how the relationship changes.

2. For example, for the activity planning goal, the “activity recording” affordance is related to the “comparative analysis” affordance, which is then related “recommending activity level” affordance, which then feeds the “activity planning” affordance (Abouzahra & Ghasemaghaei, Citation2021)

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