Michael Winter , Thomas Probst , Dennis John , Rüdiger Pryss
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By capturing real-time, longitudinal stress data from adults during a public health crisis, this dataset enables researchers to examine how stress levels fluctuated in response to pandemic restrictions and recovery phases. The integration of ecological momentary assessments with mobile sensing data (e.g., app usage statistics, coarse-grained location information) provides opportunities to analyze adult stress trajectories, identify stress resilience factors, and evaluate the effectiveness of mobile health approaches for stress monitoring during crisis situations. 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Recognizing and understanding stress in adults during Covid-19: Data insights from the corona health app
The dataset presented in this work is derived from the Stress Recognition Study in the Corona Health app, a digital health platform designed with the German Robert Koch Institute (RKI) to monitor stress levels and associated factors in adults during and after the COVID-19 pandemic. Data were collected using a mobile-based survey completed by 627 adults (18 years and older) at baseline, with 385 of these participants also contributing 4,331 follow-up assessments over time. The study utilized baseline and follow-up questionnaires to capture changes in participants' stress levels throughout the pandemic period and beyond (December 2020 to May 2025). The questionnaires cover key stress indicators such as perceived stress levels, demographic factors, and smartphone sensor data. By capturing real-time, longitudinal stress data from adults during a public health crisis, this dataset enables researchers to examine how stress levels fluctuated in response to pandemic restrictions and recovery phases. The integration of ecological momentary assessments with mobile sensing data (e.g., app usage statistics, coarse-grained location information) provides opportunities to analyze adult stress trajectories, identify stress resilience factors, and evaluate the effectiveness of mobile health approaches for stress monitoring during crisis situations. The data, including questionnaire responses and mobile sensing data, are publicly available under a Creative Commons license at https://zenodo.org/records/15780255.
期刊介绍:
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