A Longitudinal Study of Fitbit Usage Behavior Among College Students

Cyberpsychol Behav Soc Netw. 2022 Mar;25(3):181-188. doi: 10.1089/cyber.2021.0047. Epub 2022 Feb 2.


Fitbit wearable devices provide users with objective data on their physical activity and sleep habits. However, little is known about how users develop their usage patterns and the key mechanisms underlying the development of such patterns. In this article, we report results from a longitudinal analysis of Fitbit usage behavior among a sample of college students. Survey and Fitbit data were collected from 692 undergraduates at the University of Notre Dame across two waves. We use a structural equation modeling strategy to examine the relationships among three dimensions of Fitbit usage behavior corresponding to three elements of the habit loop model: trust in the accuracy of Fitbit physical activity and sleep data (cue), intensity of Fitbit device use (routine), and adjustment of physical activity and sleep behaviors based on Fitbit data (reward). More than 75 percent of participants trusted the accuracy of Fitbit data and nearly half of the participants reported they adjusted their physical activities based on the data reported by their devices. Participants who trusted the Fitbit physical activity data also tended to trust the sleep data, and those who intensively used Fitbit devices tended to adjust both their physical activities and then sleep habits. Psychological states and traits such as depression, extroversion, agreeableness, and neuroticism help predict multiple dimensions of Fitbit usage behaviors. However, we find little evidence that trust, Fitbit usage, or perceived adjustment of activity or sleep were associated with actual changes in levels of sleep and activity. We discuss the implications of these findings for understanding when and how this new monitoring technology results in changes in people's behavior.

Keywords: Fitbit usage behavior; college students; psychological traits; structural equation modeling.

MeSH terms

  • Exercise
  • Fitness Trackers*
  • Humans
  • Longitudinal Studies
  • Students
  • Wearable Electronic Devices*