Publications by authors named "Calvin Herd"

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants' smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors-the accelerometer, gyroscope, and GPS- within and across Android and iOS devices.

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Article Synopsis
  • Most individuals with mental health disorders struggle to get timely and effective care, despite significant healthcare spending, highlighting a gap in accessible treatments.
  • Researchers are exploring digital health technologies to monitor behavior in real-world settings, but results have been inconsistent, indicating a need for improved data sources.
  • A newly created dataset, derived from two clinical trials with over two thousand participants, includes self-reported mood and behavioral data, aimed at advancing research in digital mental health and evaluating remote care effectiveness.
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