Cardiovascular risk and work stress in biomedical researchers in China: An observational, big data study protocol

TL;DR

This study aims to characterize the health and work stress of biomedical researchers in China using big data and browser-based data collection, exploring the links between work environment, behaviour, and mental health.

Contribution

It introduces a novel browser extension and a large-scale observational protocol to analyze behavioural and psychological data of researchers in China.

Findings

Insights into the impact of work stress on health.

Data on role conflict and family support effects.

Methodology for large-scale behavioural health data collection.

Abstract

Introduction: Internet technologies could strengthen data collection and integration and have been used extensively in public health research. It is necessary to apply this technology to further investigate the behaviour and health of biomedical researchers. A browser-based extension was developed by researchers and clinicians to promote the collection and analysis of researchers' behavioural and psychological data. This protocol illustrates an observational study aimed at (1) characterising the health status of biomedical researchers in China and assessing work stress, job satisfaction, role conflict, role ambiguity, and family support; (2) identifying the association between work, behaviour, and health; and (3) investigating the association between behaviour and mental status. Our findings will contribute to the understanding of the influences of job, work environment, and family support on the mental and physical health of biomedical researchers. Methods and analysis: This is a prospective observational study; all candidates will be recruited from China. Participants will install an extension on their Internet browsers, which will collect data when they are accessing PubMed. A web-based survey will be sent to the user interfaces every 6 months that will involve sociodemographic variables, perceived stress scale, job satisfaction scale, role conflict and ambiguity scale, and family support scale. Machine-learning algorithms will analyse the data generated during daily access. Ethics and dissemination: This study received ethical approval from the ethics committee of the Shanghai Children's Medical Centre (reference number SCMCIRB-K2018082). Study results will be disseminated through peer-reviewed publications and conference presentations.