Over the past decade as smartphones and wearable tracking devices have grown in popularity, more individuals have begun collecting their own health and behavioral data. Innovations in sensor technology now allow individuals to continuously collect data over long periods of time with minimal effort. As a result, more data has become accessible for individuals and their healthcare providers to analyze and inform decisions. While these data are often assumed to be a record of health and a reflection of the self-tracker’s routine living, it is inevitable that data is captured during a period of disruption or non-routine circumstances. This dissertation research investigated how self-tracked data that has been captured during such circumstances are reflected upon after the disruption has ended. The preliminary study explored the use of wearable data captured during an intensive outpatient therapy program, a circumstance outside of the patients’ routine environment. The main study explored how women reflected on data from their pregnancy around one year after giving birth. Through these studies, I formulated the notion of bounded situational contexts to encapsulate how individuals perceive the boundaries of disruption within data that is captured. I discuss how self-tracking tools can be designed to enable individuals to modify visualizations of their data according to their perceived bounded situational contexts to aid in data interpretation.

Creator

• Ng, Ada

DOI

• https://doi.org/10.21985/n2-z6d1-nj04

Subject

• Computer science
• Communication

Language

• en

Alternate Identifier

• etdadmin_upload_840621
• http://dissertations.umi.com/northwestern:15690

Keyword

• patient-generated data
• human-computer interaction
• mental health
• personal informatics
• human-data interaction
• self-tracking

Date created

• 2021-01-01

Resource type

• Dissertation

Rights statement

• In Copyright