Fitness Tracker Information and Privacy Management: Empirical Study

Area of Science:

• Health Informatics
• Human-Computer Interaction
• Behavioral Science

Background:

• Fitness trackers monitor activities like steps, calories, sleep, and heart rate, generating vast health data.
• Widespread adoption of fitness trackers creates opportunities for improved patient health assessments by providers.
• Sharing personal fitness data with healthcare providers poses privacy risks and concerns about information misuse.

Purpose of the Study:

• To investigate how granular privacy controls influence users' willingness to share fitness information with healthcare providers.
• To explore the impact of perceived risk, perceived benefits, and trust on the intention to share fitness data.

Main Methods:

• A study involving 270 participants recruited via Amazon Mechanical Turk (MTurk).
• Participants were randomly assigned to experimental groups to test a conceptual model.
• Structural equation modeling (SEM) was employed to analyze the relationships between variables.

Main Results:

• Structural equation modeling explained approximately 60% of the variance in the intention to share fitness information.
• Perceived risk and trust in the system were significant predictors of willingness to share data.
• Three out of four hypotheses were supported in the study.

Conclusions:

• Individuals are more inclined to share fitness data when provided with granular privacy control options.
• Findings integrate Communication Privacy Management (CPM) theory with the privacy calculus model.
• The study highlights the importance of user control in facilitating health data sharing.