  Personal informatics (PI) systems, powered by smartphones and wearables,
enable people to lead healthier lifestyles by providing meaningful and
actionable insights that break down barriers between users and their health
information. Today, such systems are used by billions of users for monitoring
not only physical activity and sleep but also vital signs and women's and heart
health, among others. Despite their widespread usage, the processing of
sensitive PI data may suffer from biases, which may entail practical and
ethical implications. In this work, we present the first comprehensive
empirical and analytical study of bias in PI systems, including biases in raw
data and in the entire machine learning life cycle. We use the most detailed
framework to date for exploring the different sources of bias and find that
biases exist both in the data generation and the model learning and
implementation streams. According to our results, the most affected minority
groups are users with health issues, such as diabetes, joint issues, and
hypertension, and female users, whose data biases are propagated or even
amplified by learning models, while intersectional biases can also be observed.
