ABSTRACT
Context: Long COVID can appear as a severe late consequence (sequela) of a COVID-19 infection, leading to the inability to work or participate in social life for an unknown amount of time. To see friends or family struggling with long COVID might influence people's risk perceptions, vaccine efficacy expectations, and self-efficacy perceptions to prevent COVID-19 and its consequences. Methods: In an online survey in August 2022, n = 989 German-speaking participants indicated whether they knew someone who suffered from long COVID illness. Four dimensions of protection motivation theory (PMT) were assessed afterwards, as well as vaccination intentions. Results: Multiple mediation analysis with participants who knew vs. didn't know someone with long COVID (n = 767) showed that knowing someone with long COVID was associated with higher perceived affective and cognitive risk of long COVID-19 as well as higher perceived vaccine efficacy. Self-efficacy, i.e., the ease to protect oneself against long COVID, was lower in participants who knew long-COVID patients. Indirect positive effects for response efficacy and affective risk suggest that vicarious experience with long COVID is associated with increased intentions to get a COVID-19 vaccine. Conclusion: The protection from long COVID through vaccination are relevant aspects for individual decisions and health communication.
ABSTRACT
Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.