Abstract
Self-tracking technologies have the potential to increase users’ motivation to engage in healthy behaviours. However, the Theory of Self-Determination (SDT) shows that motivation lies on a spectrum. The ways behaviours are initiated and sustained have a significant impact on a person’s physical and emotional wellbeing. Research suggests that self-tracking technology can have a positive effect on user wellbeing. However, conflicting studies suggest that the use of this technology can lead to anxiety, guilt and other negative consequences. The objective of this study was to determine the impact of fitness self-tracking on users’ wellbeing. This was achieved by a) analysing the impact of achievement and social related elements on the satisfaction of the three basic psychological needs b) analysing the effect that the fulfilment of these needs have on autonomous and controlled motivation and lastly c) determining if the type of motivation experienced by users impacts their levels of wellbeing. Data was collected from 411 users of fitness self-tracking technology in South Africa through an online survey based on models of SDT in health contexts and gamification in sports apps. It was analysed using partial least squares structural equation analysis (PLS-SEM). The results show that the achievement and social elements inherent in self-tracking technology contribute to the satisfaction of the needs for competence and relatedness. The fulfilment of these needs results in increased levels of autonomous motivation and improved user wellbeing. These insights can be applied in the design of new fitness self-tracking technology, with the aim of increasing users’ levels of autonomous motivation and wellbeing.
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Jones, J., Tsibolane, P., van Belle, JP. (2022). Motivations and Challenges Related to the Use of Fitness Self-tracking Technology. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. Lecture Notes in Computer Science, vol 13454. Springer, Cham. https://doi.org/10.1007/978-3-031-15342-6_29
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