  Objective digital data is scarce yet needed in many domains to enable
research that can transform the standard of healthcare. While data from
consumer-grade wearables and smartphones is more accessible, there is critical
need for similar data from clinical-grade devices used by patients with a
diagnosed condition. The prevalence of wearable medical devices in the diabetes
domain sets the stage for unique research and development within this field and
beyond. However, the scarcity of open-source datasets presents a major barrier
to progress. To facilitate broader research on diabetes-relevant problems and
accelerate development of robust computational solutions, we provide the
DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal
data from wearable medical devices, including a total of 27,561 days of
continuous glucose monitor data and 8,220 days of insulin pump data from 54
patients with diabetes. This dataset is useful for developing novel analytic
solutions that can reduce the disease burden for people living with diabetes
and increase knowledge on chronic condition management in outpatient settings.
