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Recently, there has been a proliferation of personal health applications describing to use Artificial Intelligence (AI) to assist health consumers in making health decisions based on their data and algorithmic outputs. However, it is still unclear how such descriptions influence individuals' perceptions of such apps and their recommendations. We therefore investigate how current AI descriptions influence individuals' attitudes towards algorithmic recommendations in fertility self-tracking through a simulated study using three versions of a fertility app. We found that participants preferred AI descriptions with explanation, which they perceived as more accurate and trustworthy. Nevertheless, they were unwilling to rely on these apps for high-stakes goals because of the potential consequences of a failure. We then discuss the importance of health goals for AI acceptance, how literacy and assumptions influence perceptions of AI descriptions and explanations, and the limitations of transparency in the context of algorithmic decision-making for personal health. 

The research community on the study and design of systems for personal informatics has grown over the past decade. To take stock of what the topics the field has studied and methods the field has used, we map and label 523 publications from ACM's library, IEEE Xplore, and PubMed. We surface that the literature has focused on studying and designing for health and wellness domains, an emphasis on understanding and overcoming barriers to data collection and reflection, and progressively fewer contributions involving artifacts being made. Our mapping review suggests directions future research could explore, such as identifying and resolving barriers to tracking stages beyond collection and reflection, engaging more with domain experts, and further discussing the privacy and ethical concerns around tracked data.