CARWatch – An open-source framework for improving cortisol awakening response sampling (CARWatch)
Internally funded project
Acronym: CARWatch
Start date : 01.09.2019
Project details
Short description
Many studies investigating the cortisol awakening response (CAR) suffer from a lack of precise and objective methods for assessing the awakening and saliva sampling times. Failure to correctly report times or to adhere to the study protocol, which is common in unsupervised real-world studies, can lead to a measurement bias on CAR quantification and can even contribute to erroneous findings in psychoneuroendocrinological (PNE) research.
To address this gap in available methodology, we developed CARWatch. CARWatch is a framework to support objective and low-cost assessment of cortisol samples in unsupervised, real-world environments. It consists of an Android application that schedules sampling times and tracks them by scanning a barcode on the respective sampling tube as well as a Python package that provides tools to configure studies, prepare the study materials, and process the log data recorded by the app.
Scientific Abstract
Many studies investigating the cortisol awakening response (CAR) suffer from a lack of precise and objective methods for assessing the awakening and saliva sampling times. Failure to correctly report times or to adhere to the study protocol, which is common in unsupervised real-world studies, can lead to a measurement bias on CAR quantification and can even contribute to erroneous findings in psychoneuroendocrinological (PNE) research.
To address this gap in available methodology, we developed CARWatch. CARWatch is an open-source framework to support objective and low-cost assessment of cortisol samples in unsupervised, real-world environments. It consists of an Android application that schedules sampling times and tracks them by scanning a barcode on the respective sampling tube as well as a Python package that provides tools to configure studies, prepare the study materials, and process the log data recorded by the app.
Involved:
Robert Richer
Project Leader
Björn Eskofier
Project Leader
Nicolas Rohleder
Project Leader
Robert Richer
Project Member
Luca Abel
Project Member
Arne Küderle
Project Member
