Information Flows for Athletes' Health and Performance Data

TL;DR

This paper explores appropriate information flow models for athletes' health and performance data, emphasizing privacy and collaborative sharing to enhance athletic development and well-being.

Contribution

It introduces new information flow types based on contextual integrity, including team-centric, athlete-centric, research-centric, and community-centric models.

Findings

Proposes two main data flow types: team-centric and athlete-centric.

Introduces two additional data flows: research-centric and community-centric.

Discusses applying differential privacy to athlete data.

Abstract

Increasing numbers of athletes and sports teams use data collection technologies to improve athletic development and athlete health with the goal of improving competitive performance. Personal data privacy is managed but it is not always a priority for the coaches who are in charge of athletes. There is a pressing need to investigate what are appropriate information flows as described by contextual integrity for these data technologies and these use cases. We propose two main types of information flows for athletes' health and performance data -- team-centric and athlete-centric -- designed to characterize data used for the collective and individual physical, psychological and social development of athletes. We also present a scenario for applying differential privacy to athletes' data and propose two new information flows -- research-centric and community-centric -- which envision larger-scale, more collaborative sharing of athletes' data in the future.