Publication date: Feb 03, 2025
The transition from traditional office work to telework has accelerated significantly since the late 20th century, especially in light of the COVID-19 pandemic. Despite its widespread adoption, the long-term health impacts of telework remain unclear. This study seeks to clarify the telework-health relationship by integrating longitudinal self-reported health data with health-related administrative records. An online self-reported longitudinal survey with four follow-ups of 6 months each, starting in November 2024, will be set up and linked with administrative data sources. In total, a non-probabilistic sample of 5000 non-teleworkers and teleworkers will be recruited. This survey will mainly assess the effect of teleworking on mental (eg, depression and anxiety) and physical (eg, pain) health. Administrative data (eg, healthcare consumption contacts and socioeconomic status) will be extracted from Belgian administrative data sources (Statistics Belgium and the InterMutualistic Agency) for the same period. This administrative data will be linked to the survey data using the Social Security ID. The underlying relationships between telework and health will be analysed via regression models and mediation models embedded in the natural effects framework. The analysis will aim to (1) identify the impact of telework on self-reported health and administrative data, (2) identify the moderators and mediators between the telework-health relationship, (3) understand the long-term patterns of telework and health interaction and (4) predict the health outcomes of teleworkers. To mitigate biases associated with non-probabilistic samples and attrition, standardised probability weights scoring will be derived from the data. This study involves human participants and has been approved by the Ethics Committee of Universitair Ziekenhuis Gent (Nr^0. ONZ-2023-0630). The participants will participate in the study after signing an informed consent form. The study will be disseminated in academic journals, on (social) media and on the project website.
Open Access PDF
Semantics
Type | Source | Name |
---|---|---|
disease | MESH | COVID-19 pandemic |
disease | MESH | data sources |
disease | MESH | depression |
disease | MESH | anxiety |
disease | MESH | Health Status |