Fitness Trackers & Personal Fitness Information

Overview: The growth of health and fitness-tracking apps and related wearable technologies is rooted in the burgeoning “quantified self” movement, where individuals employ self-tracking technologies to discover quantifiable knowledge about their bodies and themselves (Wolf, 2010). Health and fitness applications have become increasingly popular (Borrison, 2014), typically collecting information on distance, speed, heart rate, calories, and/or steps climbed, as well as more advanced data on sleep patterns, eating habits, and even mood. This ecosystem encourages sharing data within social networks to compare performance and provide motivation.

These applications are located within the broader ecosystem of the Internet of Things (IoT), cloud computing, and big data: users’ health and fitness data are captured by networked devices, transmitted and stored through third-party cloud providers, and analyzed through various algorithmic techniques to identify patterns and trends. While these broader ecosystems are receiving growing scrutiny about data security, user privacy, and algorithmic transparency, the unique nature of health and fitness-tracking applications demands specific investigation. First, health data has particular privacy concerns that need to be considered in the design (Al Ameen et al., 2012). Second, the popularity of these devices, and how they increasingly are woven into our day-to-day lives and routinized, suggest particular attention must be paid to how users understand the privacy implications of their data being tracked. Third, nascent implementations of providing health data to researchers and developers (e.g., Apple’s HealthKit, ResearchKit, and CareKit platforms) require careful evaluation to ensure data collection, aggregation, and evaluations protect individual privacy.

To inform these nascent efforts for crafting information policy within the this rapidly-growing domain—and to move beyond existing research in this space which focuses on critical, rather than empirical—analyses (e.g., Paul & Irvine, 2014; Thierer, 2015; Zhou & Piramuthu, 2015), we have collected data during the first half of 2017 to better understand how health and fitness-tracking apps have become integrated into users’ daily life, how much users’ actively think about the data they’re sharing, whether they understand who can access the information they share through these apps, and what (if any) privacy concerns they have about sharing health data.