  Gestational diabetes (GDM) poses a growing health risk to both pregnant women
and their offspring. While telehealth interventions for GDM management have
proven effective, they have traditionally relied on healthcare professionals
for guidance and feedback. Our aim was to explore self-tracking in GDM with
wearable sensors from self-discovery (i.e., learning associations between
glucose levels and lifestyle) and user experience perspectives. We conducted a
mixed-methods study with women diagnosed with GDM, utilizing continuous glucose
monitor and three types of physical activity sensors (activity bracelet,
hip-worn sensor, and electrocardiography sensor) for a week. Data from the
sensors was collected, and participants were later interviewed about their
experience with the wearable sensors. Additionally, we gathered maternal
nutrition data through a 3-day food diary and recorded self-reported physical
activity using a logbook. We discovered that continuous glucose monitors were
especially valuable for self-discovery, particularly when establishing links
between glucose levels and nutritional intake. Challenges associated with using
wearable sensors data for self-discovery in GDM included: (1) Separation of
glucose and physical activity data in different applications, (2) Missing key
trackable features, such as light physical activity and non-walking activities,
(3) Discrepancies in data, and (4) Differences in perceived versus measured
physical activity. The placement of sensors on the body emerged as a critical
factor influencing data quality and personal preferences. To conclude, an app
where glucose, nutrition, and physical activity data are combined is needed to
support self-discovery. This app should enable tracking of essential features
for women with GDM, including light physical activity, with data originating
from a single sensor to ensure consistency and eliminate redundancy.
