Fitness Tracker Information and Privacy Management: Empirical Study.

Abstract

Fitness trackers allow users to collect, manage, track, and monitor fitness-related activities, such as distance walked, calorie intake, sleep quality, and heart rate. Fitness trackers have become increasingly popular in the past decade. One in five Americans use a device or an app to track their fitness-related activities. These devices generate massive and important data that could help physicians make better assessments of their patients' health if shared with health providers. This ultimately could lead to better health outcomes and perhaps even lower costs for patients. However, sharing personal fitness information with health care providers has drawbacks, mainly related to the risk of privacy loss and information misuse. This study investigates the influence of granting users granular privacy control on their willingness to share fitness information. The study used 270 valid responses collected from Mtrurkers through Amazon Mechanical Turk (MTurk). Participants were randomly assigned to one of two groups. The conceptual model was tested using structural equation modeling (SEM). The dependent variable was the intention to share fitness information. The independent variables were perceived risk, perceived benefits, and trust in the system. SEM explained about 60% of the variance in the dependent variable. Three of the four hypotheses were supported. Perceived risk and trust in the system had a significant relationship with the dependent variable, while trust in the system was not significant. The findings show that people are willing to share their fitness information if they have granular privacy control. This study has practical and theoretical implications. It integrates communication privacy management (CPM) theory with the privacy calculus model.