Clustering of office workers from the OFFICAIR study in The Netherlands based on their self-reported health and comfort

Dong Hyun Kim, P.M. Bluyssen

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
64 Downloads (Pure)

Abstract

The growing field of indoor health and comfort studies recently shifted from predicting the average comfort and wellbeing of a large population into identifying the needs of individuals in different scenarios. This study aimed to identify different profiles of office workers in the Netherlands who took part in the OFFICAIR study, based on their self-reported health and comfort. Associations of respondents’ health and comfort with gender and type of office indicated that female occupants experienced significantly higher numbers of building-related symptoms and consistently lower satisfaction levels of their office environment than male occupants. Workers in open space offices without partitions reported lower satisfaction and suffered from building-related symptoms more frequently than occupants in single person offices. TwoStep cluster analysis revealed three profiles of occupants: Healthy and satisfied workers, Moderate healthy and noise-bothered workers and Unhealthy and Air and temperature-bothered workers. While the first group was by far the healthiest, significant higher risks for building-related symptoms such as dry eyes (OR: 3.38), dry skin (OR: 2.87) and watering, itchy eyes (OR: 2.7) were identified for the unhealthy group than for the moderate healthy group. The results confirm the need of an integrated approach to better understand moderate and unhealthy groups in order to provide customised solutions for individuals with different complaints and needs.
Original languageEnglish
Article number106860
Number of pages13
JournalBuilding and Environment
Volume176
DOIs
Publication statusPublished - 2020

Keywords

  • Building-related symptoms
  • Indoor environmental quality
  • Office workers
  • Self-reported health and comfort
  • TwoStep cluster analysis

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