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People go online for information and support about sensitive topics like depression, infertility, death, or divorce. However, what happens when such topics are algorithmically recommended to them even if they are not looking for it? This article examines people's self-diagnostic behaviors based on algorithmically-recommended content, for example, wondering if they might have depression because an algorithm pushed that topic into their view. Specifically, it examines what happens when the sensitive content is not generated by users, but by companies in the form of targeted advertisements. This paper explores these questions in three parts. The first part reviews literature on self-diagnosis and targeted advertising. The second part presents a mixed-methods study of how targeted ads can enable self-diagnostic reactions. The third part reflects on the mechanisms that influence self-diagnosis and examines potential regulatory implications. 

Efficient Type 1 Diabetes (T1D) management necessitates comprehensive tracking of various factors that influence blood sugar levels. However, tracking health data for children with T1D poses unique challenges, as it requires the active involvement of both children and their parents. This study aims to uncover the benefits, challenges, and strategies associated with collaborative tracking for children (ages 6-12) with T1D and their parents. Over a three-week data collection probe study with 22 child-parent pairs, we found that collaborative tracking, characterized by the shared responsibility of tracking management and data provision, yielded positive outcomes for both children and their parents. Drawing from these findings, we delineate four distinct tracking approaches: child-independent, child-led, parent-led, and parent-independent. Our study offers insights for designing health technologies that empower both children and parents in learning and encourage the sharing of different perspectives through collaborative tracking.